International Journal of Automation and Control (48 papers in press)

Regular Issues

• Design of a nonlinear PID controller for a twin rotor aerodynamic system  
  by Marwa Rasheed Ali, Omer Waleed Abdulwahhab 
  Abstract: This paper presents the implementation of an optimal nonlinear PID controller (N-PID) applied to a simplified and lab-scaled version of a real helicopter called a twin-rotor aerodynamic system. The controller is proposed to precisely track the desired trajectories for both the main and tail rotors. This controller is developed by adjusting the integral part of the conventional PID controller to become nonlinear by using nonlinear function. The N-PID is compared with the optimal linear PID controller (L-PID). The simulation results show that the N-PID controller outperforms the optimal L-PID controller. This is clear from the conclusion that the N-PID controller’s having lower performance indices than the L-PID controller’s, suggesting superior overall error minimisation. Some performance indicators are improved by the N-PID. Another comparison between the N-PID controller and the sliding mode controller (SMC) is carried out. The simulation results show that the N-PID controller outperforms the SMC in some performance indicators.
  Keywords: N-PID; nonlinear PID controller; optimal PID controller; twin rotor aerodynamic system; frequency response specifications.
  DOI: 10.1504/IJAAC.2026.10069866
   
  
• Perturbation estimation-based multivariable control of polymerisation reactor with input constraints  
  by Zahra Ahangari Sisi, Mehdi Mirzaei, Maryam Farbodi, Sadra Rafatnia 
  Abstract: The polymerisation reaction in a continuous stirred tank reactor (CSTR) is controlled as a multi-input multi-output (MIMO) nonlinear process. This study proposes a novel optimisation-based approach for estimating the perturbations of polymerisation reactor model. The proposed strategy compensates for disturbances and time-varying uncertainties by appending complementary terms to the model. Accordingly, a continuous predictive controller is designed based on the constructed model considering the limitations of control inputs. In the results, at first, by evaluating the open-loop performance, the effect of noise in the estimated model is attenuated by weighting the complementary terms. Subsequently, the better performance of the estimator based controller is demonstrated in comparing with the other methods. For example, for the same range of the control inputs, the proposed method shows respectively 5.6% and 40% reduction in the root means square (RMS) of tracking errors for the monomer concentration and the temperature with much less simulation time compared to the conventional predictive controller.
  Keywords: polymerisation reactor; model updating; multivariable control; constrained stability; perturbation estimation; stochastic stability.
  DOI: 10.1504/IJAAC.2026.10069946
   
  
• Design and investigations of MIT, fractional-order MIT and modified MIT rule-based model reference adaptive control for noninteracting and interacting two-tank coupled systems  
  by Dhananjay Gupta, Awadhesh Kumar, Vinod Kumar Giri 
  Abstract: The chemical process industries make extensive use of coupled tank systems (CTS) in their industrial applications. In this paper, a normal Massachusetts Institute of Technology (MIT) rule, fractional-order MIT (FOMIT) rule and modified MIT rule-based model reference adaptive controller (MRAC) have been designed to stabilise CTS. After the implementation of all three mentioned methods, it has been found that a stable closed loop system cannot be guaranteed by traditional MIT rule. To overcome this problem the FOMIT and PID enhanced MIT rule-based MRAC has been designed. The FOMIT rule-based MRAC method necessitates a few lower values of adaptation gains to achieve the desired response, while modified MIT rule-based MRAC shows the desired response at a very wide range of adaptation gain values. The research contributes to the understanding of the adaptability and robustness of control systems in the context of two-tank coupled systems.
  Keywords: CTS; coupled tank systems; adaptive control; adaptation gain; MIT rule; fractional-order MIT rule; modified MIT rule; MRAC.
  DOI: 10.1504/IJAAC.2026.10070039
   
  
• Observer-based nonlinear cascade control approach of rewinding systems with uncertainties and disturbances compensation  
  by Van Trong Dang, Thi Dieu Trinh Tran , Dinh Bao Hung Nguyen , Tung Lam Nguyen 
  Abstract: In this paper, the goal is to design a controller based on the backstepping technique and a nonlinear disturbance observer to control the tension and angular velocity of multiple-span rewinding systems under disturbance and uncertainty. The backstepping control technique is considered a control method with great potential for underactuated nonlinear objects with multi-input multi-output. However, there is currently no secure stability in operation when the system is ominously affected by disturbances and uncertainties. Therefore, a non-linear disturbance observer is integrated with the method to address that problem. This observer is designed with auxiliary state variables to reduce the measurement disturbance. The convergence of the proposed observer is taken into consideration and the stability of the whole system is demonstrated by choosing a proper Lyapunov function. The effectiveness of the proposed control schematic is tested through comparative simulations with other typical nonlinear control algorithms on a two-span web transport system.
  Keywords: multiple spans rewinding systems; back stepping technique; HGDOB; high-gain disturbance observer; cascade control structure; lumped uncertainties; disturbances.
  DOI: 10.1504/IJAAC.2026.10070127
   
  
• A novel cascade controller for load frequency control in a multi-sources microgrid  
  by Notchum Deffo Boris Arnaud* , Bakouri Anass 
  Abstract: The increasing integration of renewable energy sources (RESs) into the power grid reduces system inertia, leading to frequency instability due to RES and load variability. To address this, a novel cascaded controller, (PIDn+PDn)-PI, is proposed for load frequency control (LFC) in a microgrid comprising wind turbines, photovoltaic systems, solar thermal power, aqua electrolyser, diesel generator, electric vehicle, and hybrid storage (supercapacitor & battery). The controller's parameters are optimised using the particle swarm optimisation (PSO) algorithm. MATLAB/Simulink simulations demonstrate its superiority in settling time (0.0063 s), maximum overshoot (1.07 × 10^-4), and maximum undershoot (-1.58 × 10^-3) compared to PI, PIDn, PDn-PI, and nonlinear sliding mode controllers. A sensitivity analysis with electric vehicle gain variations (0.1, 1, and 10) confirms robustness. The results highlight the efficiency and reliability of the proposed controller in ensuring frequency stability in microgrids with high RES penetration.
  Keywords: LFC; load frequency control; optimisation algorithms; cascaded controller; RESs; renewable energy sources; microgrids.
  DOI: 10.1504/IJAAC.2026.10070790
   
  
• A sliding mode controller with balancing technique for interleaved buck-boost converters  
  by Ilhem Mhamdi , Mohamed Abbes 
  Abstract: This paper investigates the influence of components mismatch on the operation of interleaved buck-boost converters (IBB). It was shown that intrinsic differences of employed electronic components would cause significant current imbalance through the switching cells, which may impede system performances. Thus, a control strategy based on Sliding Mode Control (SMC) is proposed in this paper in order to ensure efficient control of the output voltage but also maintain equal currents in the two filtering inductors. The performances of the proposed control method were evaluated through simulation and experimental results. It was concluded that the SMC-based strategy effectively maintains balanced currents across the converter stages while ensuring accurate control of the output voltage. The proposed IBB converter along with the proposed control strategy can be efficiently integrated in different high power applications, including renewable energy generation, electric vehicles and fuel cells.
  Keywords: control systems design; power systems; variable structure control; SMC; sliding mode control.
  DOI: 10.1504/IJAAC.2026.10070865
   
  
• Modified discrete internal model control (D-IMC) for cart inverted pendulum system  
  by Sumit Kumar Pandey, Jayati Dey, Subrata Banerjee 
  Abstract: This work suggests a discrete internal model control (D-IMC) in a modified form for control of MIMO systems. The primary goal of the suggested control strategy is to guarantee perfect reference tracking functionality although external disturbances remain with the plant. To this end, the requirements for accurate set-point chasing are also derived in this work. A methodical design technique is developed and presented in an explicit manner. Further, to establish the veracity of the theoretical developments, the design method is applied for stabilization and control of cart inverted pendulum system (CIPS) that is a one of the benchmark examples of unstable under-actuated multi-input multi-output (MIMO) systems. The proposed controller is implemented successfully to the test rig set-up of CIPS made by FEEDBACK INSTRUMENTS LTD, UK. The simulation results prove that the designed controllers are performing satisfactorily. The experimental results also validate the time domain performance and efficacious reference tracking capability of the proposed control technique.
  Keywords: D-IMC; discrete internal model control; UMIMO; under actuated MIMO; CIPS; cart inverted pendulum system; robust control.
  DOI: 10.1504/IJAAC.2026.10071636
   
  
• Advanced power management and optimisation of hybrid battery-supercapacitor energy storage systems in off-grid photovoltaic installations using heuristic-based fractional order control  
  by Hadjer Benfatma, Houari Khouidmi, Boubakar Bessedik 
  Abstract: The implementation of hybrid energy storage solutions, combining batteries and supercapacitors, into off-grid photovoltaic systems is essential for enhancing energy reliability and operational flexibility. This hybrid approach addresses the intermittency of solar power while offering significant economic benefits through optimised energy management. This study introduces an advanced power management strategy for photovoltaic systems featuring a hybrid battery-supercapacitor energy storage system. To enhance system performance, a fractional order proportional integral derivative (FOPID) controller was introduced and optimised using both the hippopotamus optimisation algorithm (HOA) and the Bat Algorithm (BA). The additional tuning parameters of the FOPID controller provide superior control precision compared to traditional PI controllers. Comprehensive simulations under various load and irradiance scenarios were conducted to evaluate the effectiveness of the HOA-optimised FOPID controller relative to conventional control methods. The results demonstrate that the FOPID-HOA approach significantly improves efficiency, stability, and adaptability in managing hybrid energy storage, highlighting its potential to enhance the overall performance of off-grid photovoltaic systems.
  Keywords: energy management; FOPID; fractional order proportional integral-derivative; HESS; hybrid energy storage system; HOA; hippopotamus optimisation algorithm; BA; bat algorithm.
  DOI: 10.1504/IJAAC.2026.10071694
   
  
• Active disturbance rejection control method of electronic oxygen regulator based on cascade extended state observer  
  by Dongsheng Jiang, Heng Yao, Yue Liu, Shanshan Hu, Qiyao Yang 
  Abstract: An active perturbation suppression control method based on cascaded extended state observer (CESO) is proposed to improve the stability of breathing gas pressure regulation to cope with external perturbations encountered during the operation of an electronic oxygen regulator (EOR), to improve the stability of oxygen supply to the EOR, and to reduce the respiratory resistance of pilots during high-altitude flight. We compare the CESO with the traditional extended state observer (ESO) and design a state feedback control system to counteract external effects. Findings indicate that CESO is more efficient and accurate than ESO, especially under high control gain conditions. Simulations and experiments show CESO's superior performance in state observation and its ability to enhance regulator performance across different lung flux scenarios. This research underscores the potential of CESO in boosting system disturbance resistance and presents a novel control approach for EORs.
  Keywords: EOR; electronic oxygen regulator; pressure regulation; ESO; extended state observer; CESO; cascade extended state observer.
  DOI: 10.1504/IJAAC.2026.10071835
   
  
• Constrained and distributed adaptive fault-compensation for synchronising a network of multi-robot manipulators: a Barrier Lyapunov function-based approach  
  by Amir Naderolasli 
  Abstract: In this paper, a constrained and distributed adaptive fault-compensation is proposed for synchronising a network of multi-robot manipulators in the presence of parameter uncertainties, actuator faults and the output state constraints. The actuator faults contain some inputs where their values are unknown and time varying which lead to the undesirable performance of actuators and even system instability. An adaptive approximation law is designed to estimate the actuator faults in the constrained and distributed adaptive fault-compensation without limitations on the initial conditions. The graph theory has been utilised to communicate a network of multi-robot manipulators in which the minimum spanning tree of this graph is obtained through the proposed Kruskal approach. To cope the limitations of required sensors of multi-robot manipulators, an asymmetric time-varying barrier Lyapunov function (BLF)is utilised to constrain the trajectory tracking errors due to these restrictions while the unknown faults occur in the actuators of robot manipulators.
  Keywords: BLF; barrier Lyapunov function; constrained control; dynamic surface control; fault compensation; multi-robot manipulator.
  DOI: 10.1504/IJAAC.2026.10071924
   
  
• Adaptive neural finite-time control for uncertain stochastic semi-Markov jump systems subject to actuator failures  
  by Chengxin Li, Zhen Liu, Ruiping Xu, Baoping Jiang, Quanmin Zhu 
  Abstract: This paper presents an adaptive finite-time (FT) neural network-based sliding mode control (SMC) strategy for a category of uncertain stochastic semi- Markov jump systems (SMJSs) subject to composite actuator failures (AFs). Firstly, an adaptive SMC law is developed based upon RBF neural network (RBFNN) and a novel switching surface of linear (SSL) design. Specifically, the RBFNN is used to approximate both unknown disturbances and AFs, and the corresponding robust terms are introduced to compensate the approximation errors and estimation errors of the neural network, thereby ensuring that the system trajectory reaches the designed SSL in FT. Moreover, by introducing the partition strategy and stochastic stability theory, some novel sufficient conditions for the FT boundedness of the closed-loop system in the sliding mode phase are derived. This result avoids the norm-bounded assumption of AFs and unknown nonlinear disturbances and expands the previous research results. Ultimately, the effectiveness of the presented algorithm is validated through simulation results.
  Keywords: actuator failures; FTB; finite-time boundedness; semi-Markovian jump systems; RBFNN; RBF neural network; SMC; sliding mode control.
  DOI: 10.1504/IJAAC.2026.10072038
   
  
• The PSI-R method: an innovative approach to criteria weighting  
  by Do Duc Trung , Nazli Ersoy, Tran Van Dua, Hoang Xuan Thinh 
  Abstract: Weighting criteria represents a critical step in the application of Multi-Criteria Decision Making (MCDM) methods for ranking alternatives. This study introduces a novel approach for weighting criteria, denoted as PSI-R, which integrates the PSI and R methods. The process begins with the use of the PSI method to determine the initial criteria weights. These weights then form the basis for ranking the relative importance (or priority) of the criteria. The ranking derived from the PSI method is subsequently used to refine the criteria weights through the R method. The efficacy of the PSI-R method has been rigorously assessed through four distinct case studies. In each case, a comprehensive comparison was made among several MCDM ranking techniques, including PSI-R, PSI, Entropy, LOPCOW, WENSLO, SD, and MEREC methods. The robustness and practical applicability of the model were further validated through an extensive comparative analysis. This research proposes the PSI-R method as an innovative and reliable approach for weighting criteria, offering enhanced stability in the ranking of alternatives across various MCDM methods.
  Keywords: MCDM; multi-criteria decision making; PSI method; PSI-R method; objective weight.
  DOI: 10.1504/IJAAC.2026.10072039
   
  
• Distributed fault tolerant coordinated control based on event triggered communication  
  by Panfei Huang, Fuqiang Di, Jiwei Xu, Jinxiong Zhao, Sixiao Wang 
  Abstract: The paper aims to develop a distributed fault tolerant coordinated control strategy with event triggered communication for spacecraft formation flying systems under limited communication, actuator fault, and external disturbance. To suppress the influence of limited communication, an event triggered control strategy that can ease data transmission frequency is adopted. To restrain the influence of actuator fault and external disturbance, two adaptive control laws are adopted respectively which can reduce the conservatism of the control scheme. Different from the traditional triggering conditions, the triggering condition of this paper constructed based on time and can be adjusted adaptively, such that the communication frequency has been further reduced.
  Keywords: spacecraft formation flying; limited communication; actuator fault; external disturbance; event triggered control.
  DOI: 10.1504/IJAAC.2026.10072139
   
  
• Automation of preparation cleaning-in-place (CIP) process in dairy raw material production tank using PID controller  
  by Rindra Yusianto, Pulung Nurtantio Andono, Rabei Raad Ali, Herwin Suprijono, Marimin Marimin 
  Abstract: This study aims to determine the optimal Proportional Integral Derivative (PID) parameter values in a dairy raw material production tank's Cleaning-in-Place (CIP) preparation process. We use PLC with PID control and the Ziegler-Nichol 1 method to clean the CIP equipment. The results showed that the optimal value for each PID parameter was obtained at Kp = 7.2, Ki = 0.12, and Kd = 108, with the results for the material heating process being stable with a rise time of 250 seconds and a stable temperature until the 1600th second. The SSE parameter occurs at 1450 seconds. The research results also show that the automation of the CIP preparation process using a PID controller runs more optimally so that milk quality as a raw material for production is more guaranteed and safer in the logistics chain.
  Keywords: CIP; cleaning-in-place; dairy raw material; logistics chain; PID controller; Ziegler-Nichol’s method.
  DOI: 10.1504/IJAAC.2026.10072748
   
  
• Quadrotor control using hybrid approaches: LQR-PID and LQR-FOPID methods  
  by Serkan Budak, Cemil Sungur, Akif Durdu 
  Abstract: Unmanned aerial vehicles (UAVs) are susceptible to environmental disturbances such as wind, which can compromise flight stability. The linear quadratic regulator (LQR) is known for its robustness and efficiency in minimising state deviations with low control effort. This study explores hybrid control strategies by integrating LQR with PID and fractional order PID (FOPID) controllers. The Big BangBig Crunch (BB-BC) algorithm is utilised to estimate the optimal parameters of all three controllers LQR, PID, and FOPID enhancing their overall performance. Three control structures, namely LQR, LQR-PID, and LQR-FOPID, are compared using rise time, settling time, and overshoot as performance metrics. The LQR-PID configuration exhibits faster response and reduced overshoot, particularly in yaw and pitch axes. Meanwhile, LQR-FOPID demonstrates improved pitch stability with shorter settling time and minimal overshoot. The results emphasise the effectiveness of BBBC-based parameter tuning and highlight the comparative advantages of each hybrid approach in UAV control applications.
  Keywords: quadrotor control; LQR; linear quadratic regulator; PID; proportional integral derivative; FOPID; fractional proportional integral derivative; hybrid control method.
  DOI: 10.1504/IJAAC.2026.10073110
   
  
• A disturbance observer-based sliding mode integrated control method for permanent magnet synchronous motors  
  by Gang Huang, Mingcan Zhang, Yuhan Zhang 
  Abstract: To address the issue of system degradation caused by parameter perturbations and external disturbances of permanent magnet synchronous motors, this paper presents an integrated control method that combines model-free adaptive fast integral terminal sliding-mode control and backstepping control based on an extended super-twisting disturbance observer. Firstly, a novel ultra-local mathematical model is constructed to reduce the dependence of a control method on an accurate model. Secondly, a model-free adaptive fast integral terminal sliding-mode controller is designed for speed loop by combining an adaptive fast convergence law and a non-singular fast integral terminal sliding-mode surface to achieve high-precision speed control. Then, a backstepping controller is designed for current loop to reject current oscillations. Finally, an extended super-twisting disturbance observer is designed to estimate the system total unknown disturbance. Then, the estimates are feedforward compensated to improve control accuracy. A comparative experiment demonstrates the effectiveness of the method.
  Keywords: PMSMs; permanent magnet synchronous motors; terminal sliding mode control; backstepping control; disturbance observer.
  DOI: 10.1504/IJAAC.2026.10073159
   
  
• PMSM wide-speed control using fractional order controllers and its application in hybrid electric vehicles  
  by Irfan Qureshi, Vikas Sharma 
  Abstract: In this paper, the wide-range speed control of Permanent Magnet Synchronous Motor (PMSM) has been proposed. In general operating conditions, speed beyond the rated speed of the motor has not been achieved. To achieve a wide speed, a field weakening technique has been used in this paper to meet the requirements of the high-speed drive at the desired torque. A comparison between conventional PI versus FO-PI controller has been done and the proposed FO-PI technique has been found superior to the conventional PI controller. The Proposed technique has been simulated and verified in MATLAB/Simulink. This paper also presents the hybrid electric vehicles (HEVs) system implemented with the Fractional Order Proportional Integral (FO-PI) controller and results also compare with standard Proportional Integral (PI) controller. Application of PMSM in a Hybrid Electrical Vehicle has also been simulated and results have been verified in MATLAB/Simulink.
  Keywords: wide range speed control; field weakening; PMSM; permanent magnet synchronous motor; MATLAB/Simulink; PI; proportional integral; FO-PI; fractional order proportional integral; HEV; hybrid electric vehicle.
  DOI: 10.1504/IJAAC.2026.10073173
   
  
• Improved ant colony algorithm for takeaway delivery route optimisation under time-varying demand  
  by Tianyu Luo, Teng Ren, Yaya Wang, Yunbao Xu, Lining Xing, Jun Li, Jie Chen 
  Abstract: This study addresses route optimisation for split pickup-and-delivery under time-varying demand in takeaway operations. We model vehicle pickup and-delivery sequences as decision variables. The objective is to minimise penalty, start-up, and driving costs. To tackle the problem’s complexity, we use a heuristic algorithm to generate feasible solutions. We develop three neighbourhood operators and integrate them into a tabu-search algorithm to strengthen local search under time-varying demand. Additionally, a simulated-annealing strategy updates the pheromone values of elite solutions to improve convergence. We validate the model and algorithm on operational data from a takeaway platform using six numerical instances. Comparative experiments with other algorithms demonstrate improved solution quality and convergence.
  Keywords: VRP; vehicle routing problem; takeout delivery; simultaneous pickup and delivery; split demand; improved ant colony algorithm.
  DOI: 10.1504/IJAAC.2026.10073949
   
  
• IDESC: a hybrid ESCSO-DDPG algorithm for risk-aware UAV path planning in radar environments  
  by Jingjing Zou, Haojie Yang, Rongxin Xu, Bektur Azimov, Jiale Huang, Peng Geng 
  Abstract: Path planning for unmanned aerial vehicles (UAVs) in radar-dense environments requires efficient navigation under dynamic threats while minimising detection risks. Reinforcement learning offers adaptability but often suffers from poor explorationexploitation balance, local optima, and limited multi-objective optimisation. We propose IDESC, a hybrid algorithm combining improved deep deterministic policy gradient (DDPG) with enhanced cuckoo search (ESCSO) under the interference fluid dynamics system (IFDS). IDESC introduces three mechanisms: dual-flow Q-value adjustment for adaptive exploration, turbulence-inspired deformation operators to escape local optima, and a multi-objective reward function balancing efficiency and risk. Experiments show clear advantages over traditional IDPG. In external threat scenarios, path length decreases by 0.2%, smoothness improves by 9.8%, and deviation control by 5.8%. In internal scenarios, reductions reach 51.33% in length, with 58.7% smoother trajectories and 17.07% stronger deviation control. These findings confirm IDESC’s effectiveness for real-time, risk-aware UAV navigation in adversarial radar environments.
  Keywords: UAV path planning; DDPG; deep deterministic policy gradient; ESCSO; cuckoo search algorithm; IFDS; interference fluid dynamics system; IDESC; IDPG.
  DOI: 10.1504/IJAAC.2027.10074071
   
  
• Reconfiguration of multi-agent system in cyclic pursuit  
  by Sajina Das, Praveen S. Babu 
  Abstract: Formation control focuses on coordinating multiple robots to achieve specific spatial arrangements during task execution. This field encounters several challenges, including the need for robots to continuously adjust their positions relative to neighbours, maintain robust communication, and comply with physical constraints. In formations utilising a cyclic pursuit strategy, each robot pursues the one ahead, creating a dynamic, mobile formation centred around a defined point. This pursuit method eliminates the necessity for a central leader. The ability to reconfigure formations dynamically in response to team composition changes ensures that multi-agent systems remain resilient and effective in uncertain environments. Adapting to variations such as agent failures or new additions, these systems maintain efficient operation and desired collective behaviours. This work addresses the challenge of reconfiguring formations in cyclic pursuit strategy by developing algorithms that enable multi-agent systems to autonomously adapt and reorganise in real-time, ensuring continued functionality and effectiveness despite unforeseen events.
  Keywords: MASs; multi-agent systems; cyclic pursuit; formation control; reconfiguration strategy; distributed control; consensus; stability analysis; addition and removal of agents.
  DOI: 10.1504/IJAAC.2027.10074261
   
  
• High-frequency stick-slip vibrations mitigation in rotary drilling systems based on cascaded sliding mode observer-controller  
  by Ahmed Hichem Zebboudj, Hamza Akroum, Mohamed Zinelabidine Doghmane, Madjid Kidouche 
  Abstract: The main objective of this study is to suppress the torsional vibrations that cause high-frequency stick-slip in petroleum rotary drilling systems, significantly affecting drilling performance and equipment integrity. The proposed solution involves implementing a cascaded sliding mode observer (SMO) and controller (SMC) to mitigate these vibrations robustly. The SMO accurately estimates drill bit angular velocity, whereas the SMC effectively suppresses severe stick-slip oscillations. Simulation studies confirm the efficacy of this approach in both state estimation and vibration reduction. Moreover, the results show that the cascaded SMO-SMC system is better at reducing stick-slip vibrations than other controllers. This reduction makes it an excellent choice for rotary drilling operations. Therefore, this observer-based controller can enhance drilling efficiency, reduce operational costs, and improve equipment safety. Moreover, adopting the SMO-SMC approach can contribute to fully automated rotary drilling systems that improve petroleum drilling performance.
  Keywords: stick-slip; rotary drilling systems; cascaded sliding mode observer based controller; torsional vibrations; drill bit velocity estimation.
  DOI: 10.1504/IJAAC.2026.10074344
   
  
• A GA-optimised supervisory fuzzy-PID for enhanced load frequency control in standalone multi-source power systems  
  by Khaleel Agail Mohamed, Khidir A.K. Mohamed, Muhammad Faizan Mysorewala 
  Abstract: Nominal power system frequencies, such as 50 Hz or 60 Hz, often deviate due to supply-demand imbalances and dynamic load disturbances. This paper presents a comparative evaluation of conventional proportional integral derivative (PID), particle swarm optimisation (PSO)-optimised PID, Genetic Algorithm (GA)-optimised PID, and a novel GA-optimised Supervisory Fuzzy PID controller for load frequency control (LFC) in standalone multi-source power systems. The key contribution lies in integrating a supervisory fuzzy logic layer that adaptively optimises PID gains in real time, with its structure optimised offline using GA. While conventional PID controllers exhibit slower transient responses and higher overshoot, the GA- and PSO-optimised variants show improved adaptability. The proposed GA-optimised Supervisory Fuzzy PID controller achieves rapid stabilisation within 10 s, minimising overshoot, and effectively handling both small (+1%) and large (+50%) load variations. Results confirm that combining fuzzy logic with metaheuristic optimisation improves control system robustness and frequency stability under dynamic and uncertain conditions.
  Keywords: supervisory fuzzy GA; PID; proportional integral derivative; PSO; particle swarm optimisation; GA; genetic algorithm.
  DOI: 10.1504/IJAAC.2025.10074650
   
  
• A contribution to observer based fuzzy logic controller for nonlinear time delay systems environmental model case study  
  by Azeddine Elmajidi, Elhoussine Elmazoudi, Jamila Elalami, Anas Hatim, Assia Saydtahiri 
  Abstract: This paper aims to develop a smart decision-making technique by extending previous work on linear systems to address delay-dependent stability and observer based stabilisation conditions for nonlinear, time-varying delay systems. By employing Takagi-Sugeno fuzzy modelling, we first propose relaxed delay-dependent stability and state-feedback stabilisation conditions in the form of Linear Matrix Inequalities, introducing uncommon free matrices for enhanced relaxation. From this, a full observer-based controller is derived. As an application, the paper focuses on atmospheric carbon dioxide control, using an unforced mathematical model that accounts for CO2 levels, human population, forest biomass, and reforestation efforts. This model is transformed, via a coordinate change, into an interior equilibrium point and then into a Takagi Sugeno fuzzy model. The approach allows us to determine the maximum delay margin under which the system remains stable. The fuzzy model is subsequently transformed into a forced system, with reforestation efforts used as the control input.
  Keywords: delay; fuzzy Takagi-Sugeno; Lyapunov-Krasovskii; observer; control; LMI; linear matrix inequalities.
  DOI: 10.1504/IJAAC.2025.10074744
   
  
• A self-tuning fuzzy inference with robust control for invented pendulum  
  by Daikh Fatima Zohra 
  Abstract: In order to address the issue of nonlinearity, we utilise linearising control, which transforms a nonlinear system into a linear one for easier analysis. However, sensitivity to parametric variations and system stability concerns lead us to introduce sliding mode and backstepping control successively to minimise the influence of these issues. This involves combining the principles of two different controls, backstepping-sliding mode, and then integrating a neuro-fuzzy system to approximate the nonlinearities of the model. The choice of the neuro-fuzzy system, specifically self tuning fuzzy inference system (STFIS), as a control technique is motivated by the fact that it offers great adaptability potential for nonlinear systems. We perform a hybridisation between the hybrid backstepping-sliding mode control and STFIS, aiming to further improve the control performance of nonlinear systems. The proposed control approach was applied to the inverted pendulum. The obtained results are very satisfactory, even in the presence of disturbances and uncertainties.
  Keywords: backstepping; neuro-fuzzy; self tuning fuzzy inference system; nonlinear; inverted pendulum.
  DOI: 10.1504/IJAAC.2027.10075052
   
  
• An online energy management strategy for lithium-ion battery/supercapacitor hybrid energy storage system based on road condition recognition and reinforcement learning  
  by Xianguang Luo, Fu Li, Jinrong Xu, Xuelian Wang, Kaiyu Luo, Rui Pan 
  Abstract: How to achieve optimal energy distribution between lithium-ion batteries and supercapacitors is the key point for improving the performance of hybrid energy storage systems. Due to the real-time dynamic changes in road conditions, the existing energy management strategies suffer from poor adaptability. Therefore, an online energy management strategy for hybrid energy storage system is proposed based on road condition recognition and reinforcement learning. Firstly, key features from multi-driving profiles are extracted using the sliding window method, and principal component analysis and gradient boosting decision tree are used for features reduction and clustering. Secondly, a Q-learning method is proposed for online energy allocation of hybrid energy storage system. Moreover, road condition adaptability and battery’s life cost are considered in Q-learning method, which can achieve optimal energy allocation under different road conditions. The results show that the proposed energy management strategy can achieve good performance in vehicle power, economy, and energy loss.
  Keywords: EMS; energy management strategy; road condition recognition; reinforcement learning; HESS; hybrid energy storage system; lithium-ion battery; supercapacitor.
  DOI: 10.1504/IJAAC.2027.10075109
   
  
• Improved artificial neural network approach for solving constrained optimal control of nonlinear fractional-order systems  
  by Fan Shu, Chengyu Hu, Xuesong Yan, Wenyin Gong, Dongcheng Li 
  Abstract: Efficient control of complex nonlinear fractional-order dynamic systems plays a crucial role in scientific and engineering applications. However, real-world control input constraints coupled with fractional-order dynamics significantly complicate this problem. To address these challenges, this study develops an artificial neural network (ANN)-based approach to the optimal control problem (OCP) of nonlinear fractional-order systems with control constraints. Specifically, we first derive the Karush-Kuhn-Tucker (KKT) optimality conditions. Based on this, we construct a fractional-order artificial neural network (FANN) that transforms the constrained OCP into an equilibrium-solving problem within a nonlinear fractional-order model. By designing a novel objective function, the FANN autonomously converges to its equilibrium, which provides the optimal solution to the studied problem. The proposed methodology uniquely embeds KKT conditions into the FANN architecture, establishing dual guarantees for solution optimality and feasibility. Moreover, the FANN explicitly accounts for the long-term feature dependencies inherent in fractional-order systems, leading to more accurate solutions. To demonstrate the validity of FANN, two numerical examples were provided.
  Keywords: ANN; artificial neural network; FANN; fractional-order artificial neural network; optimal control; nonlinear fractional-order systems; machine learning.
  DOI: 10.1504/IJAAC.2027.10075190
   
  
• GA-optimised fuzzy control for trajectory tracking of a 3-DOF robotic manipulator  
  by Abdullah Abushokor, Asem Abdalhadi, Muhammad Faizan Mysorewala 
  Abstract: This paper investigates the use of three control techniques to improve trajectory tracking of a 3-DOF robotic arm. Specifically, Fuzzy Logic Control, Adaptive Neuro Fuzzy Inference System-based control, and a Genetic Algorithm (GA)-enhanced fuzzy controller are examined, with the objective of improving tracking accuracy and handling disturbances and parameter variations. Models were derived, and controllers’ performance was evaluated using MATLAB/Simulink. Results showed that the GA-optimized fuzzy controller outperformed the others, achieving the highest accuracy and stability. Under external disturbances, the GA-optimized fuzzy controller maintained strong performance, achieving an average RMSE reduction of 89% compared to FBLC, and reductions of 95% and 91% compared to conventional FLC and ANFIS. Moreover, under severe parameter variations, it demonstrated robust resilience, maintaining stable tracking with consistently low RMSE, whereas the FBLC failed. Findings suggest that the GA-optimized fuzzy controller provides an effective solution for applications where precision and resilience to external changes are critical.
  Keywords: intelligent control; trajectory tracking; robotic arm; FLC; fuzzy logic control; Genetic Algorithm.
  DOI: 10.1504/IJAAC.2027.10075191
   
  
• Advanced gain-boosted CMOS LDO regulator for enhanced load regulation in RFSoC and mobile systems  
  by Hatim Ameziane, Mourad Yessef 
  Abstract: This paper introduces an innovative CMOS low-DropOut (LDO) regulator topology aimed at achieving high load regulation performance. The regulator exhibits low sensitivity to supply voltage variations (1.1 V to 2 V) and maintains a constant output voltage. Monte Carlo analysis demonstrates robust output performance under high-load conditions (I_LOAD = 50 mA) with a mean 1 V output voltage and a standard deviation of 2 mV. The regulator showcases excellent load regulation, swiftly responds to load ges, and stabilises the output voltage with a maximum deviation of 1 mV during a (100 µ A-50 mA) load transient. The transition time for the load step from 100 µA to 50 mA is 20 µs. Integration of an innovative circuit significantly improves load regulation, reducing voltage variations from 600 mV to 30 mV with a recovery time of 10 µs. Loop-gain simulations confirm stability under various load conditions. The proposed architecture, designed in a 90 nm CMOS process, strikes a balance between power efficiency and space utilisation, making it ideal for communication systems.
  Keywords: LDO; low-DropOut; CMOS technology; PM-IC; power management IC; load regulation; load transient response; RFSoC; radio frequency system-on-chip.
  DOI: 10.1504/IJAAC.2027.10075192
   
  
• Adaptive cruise control design and implementation for electric vehicles: comparative study and analysis of different controllers  
  by Omnia S.S. Hussian, A.A. Abouelsoud, M. A. Mustafa Hassan 
  Abstract: Currently, the world is moving towards the growth of the electric vehicles (EVs) industry to reduce environmental pollution. This research presents an optimisation of speed control for adaptive cruise control (ACC) of EVs using Proportional-Integral (PI) and model predictive control (MPC). Advanced optimisation algorithms, including ant colony optimisation (ACO) are used to fine-tune parameters of controllers for improved performance. Moreover, an artificial neural network (ANN) is employed based on collected data from the suggested control strategy to enhance system accuracy and adaptability. This research evaluates these strategies across three distinct scenarios, taking into account significant performance metrics such as settling time, percentage of overshoot, minimisation of error, and energy consumption. Results indicate that ANN-based control methodology outperforms other approaches, attaining superior, faster response times and accuracy. This study highlights the significance of incorporating intelligent strategies of control with optimisation algorithms to improve the reliability and efficiency of speed control of EVs.
  Keywords: ACC; adaptive cruise control; ACO; ant colony optimisation; ANN; artificial neural network; autonomous electric vehicle; MPC; model predictive control; PI controller; speed control; environmental pollution; intelligent strategies; optimisation algorithms.
  DOI: 10.1504/IJAAC.2027.10075247
   
  
• Predictive disturbance observer based PI control for uncertain systems with experimental validation  
  by Rakesh Borase, Sushant Pawar, Aniket Khandekar 
  Abstract: This paper presents a predictive disturbance observer-based PI control (DO-PI) method for process control applications. By combining disturbance estimation with PI control and the Smith predictor algorithm, the proposed approach effectively handles higher-order processes with time delays. The proposed DO-PI utilizes a single-loop approximation method to determine the controller gains, enhancing robustness in uncertain environments, distinguishing it from other control techniques discussed. Unlike the Extended State Observer (ESO), the predictive DO-based PI control provides delay-free disturbance estimation, leading to improved system response while maintaining stability, even in the presence of time delays. The effectiveness of the proposed method is validated through simulations and real-world tests, including spherical and conical tank level control applications, and is compared with IMC-PID and ESO for performance evaluation.
  Keywords: disturbance observer (DO); IMC-PID; internal model control-proportional integral derivative; ESO; extended state observer.
  DOI: 10.1504/IJAAC.2027.10075248
   
  
• Boundary exponential stabilisation of atactic hyperbolic systems with spatially varying coefficients  
  by Liangyu Xu, Wei Sun, Jing Li, Yali Pan 
  Abstract: This paper primarily investigates the boundary stabilization problem of a class of hyperbolic systems with zero transport velocity. The presence of zero velocity leads to infinite controller gain when the traditional backstepping method is applied. To overcome this issue, we design the Volterra transformation only for the states with nonzero velocity, while keeping the zero-velocity states unchanged. By employing the backstepping approach, we design a full-state controller to ensure exponential stability of the system. Our proposed method remains effective even when the boundary reflection coefficient is zero. To achieve this, a new target system is also developed. Finally, the effectiveness of the proposed control scheme is demonstrated through a simulation example.
  Keywords: hyperbolic system; backstepping; zero transport velocity; exponentially stable.
  DOI: 10.1504/IJAAC.2027.10075249
   
  
• Stability analysis and actuator delay compensation in lateral vehicle control via recursive least squares  
  by Salma Khatory, Lhoussain El Hajjami, El Mehdi Mellouli, Houcine Chafouk 
  Abstract: Actuator delays in steering systems significantly affect the stability and accuracy of lateral vehicle control, particularly at high speeds. This study proposes a robust control framework to address unknown constant actuator delays by combining real-time delay estimation via recursive least squares (RLS) with a fixed-time non-singular sliding mode controller (FTNSMC). The main contribution lies in integrating online delay estimation with fixed-time sliding mode control, complemented by a linear matrix inequality (LMI)-based stability analysis to determine the maximum tolerable delay and teaching learning-based optimisation (TLBO) for optimal gain tuning. The controller guarantees fast and bounded convergence regardless of initial conditions. The actuator delay is estimated online using RLS and dynamically compensated within the control loop. Simulation results show that the proposed method reduces the mean squared error (MSE) by approximately 97.44% compared to the uncompensated control, significantly improving trajectory tracking and preserving system stability under different delays.
  Keywords: lateral vehicle control; fixed-time convergence; actuator delay; time-delay estimation; RLS; recursive least squares; stability analysis; LMI; linear matrix inequality.
  DOI: 10.1504/IJAAC.2027.10075295
   
  
• CCO-TLI: efficient transformer less inverter for a photovoltaic system using congregative coati optimisation  
  by Suhas Shashikant Khot, Saurabh Krushnatrao Bhise, Neha Ganvir, Amruta Jagadish Takawale 
  Abstract: Photovoltaic transformerless inverters are popular for their reduced size and cost, but they encounter challenges such as safety risks, voltage instability, and complex wiring. To address these issues, this research introduces the congregative coyote optimisation-based transformerless inverter (CCO-TLI). The CCO-TLI utilises the congregative coyote optimisation (CCO) technique, which integrates both individual and collective intelligence for improved optimisation. This approach refines the inverter’s performance by incorporating a DC-DC converter, a modified HERIC topology inverter, and a filtering circuit. The CCO method, inspired by the cooperative traits of congregative coatis and the perceptual abilities of coyotes, enhances the system’s efficiency and stability. The CCO-TLI model demonstrates a substantial performance improvement, achieving a 24.02% increase in parameter values over a 0.2 ms duration compared to existing models. This research highlights the effectiveness of advanced optimisation techniques in overcoming the limitations of conventional transformerless inverters, offering a more reliable and efficient solution for grid-connected photovoltaic systems.
  Keywords: congregative coyote optimisation; transformer less inverter; HERIC topology; grid-connected PV systems; DC-DC convertor.
  DOI: 10.1504/IJAAC.2027.10075512
   
  
• ANN-based model predictive controller to reduce chattering of horizontal milling machine  
  by D.M. Hafez, A.A. Abouelsoud 
  Abstract: This paper considers the chattering phenomenon exhibited by horizontal milling machine which leads to poor surface finish on the work piece, shortened tool life, and possible machine damage, and presents a solution to reduce its effect in the form of feedback control. The inherent delay in the dynamics of the horizontal milling machine is the cause of oscillations under certain conditions. The contribution of this paper is twofold. First the condition of oscillation which causes chattering is derived and presented in the from of stability chart. Second a Model Predictive Controller (MPC) based on Artificial Neural Network (ANN) is proposed to reduce the chattering phenomenon. The ANN model captures the dynamics of the milling machine, based on it the MPC controller is designed. Simulation results using MATLAB Simulink show the effectiveness of the proposed controller and indeed reduces chattering compared to open loop control.
  Keywords: milling machine; horizontal milling machine chattering phenomenon; stability chart; MPC; model predictive control; ANN; artificial neural networks.
  DOI: 10.1504/IJAAC.2027.10075546
   
  
• An elite PID search algorithm with dual chaos mapping and adaptive T-distribution  
  by Wei Li, Zhitao Tu, Ning Yang, Qing Xu, Shuxin Yang, Hongsheng Yan 
  Abstract: Proportional integral derivative (PID) control is one of the earliest control strategies in control science and engineering. This paper proposes an elite PID search algorithm with dual chaos mapping and adaptive T-distribution, denoted as DCTPSA, which could overcome the limitations of the original PID-based search algorithm (PSA), such as insufficient population diversity and vulnerability to local optima. The DCTPSA integrates three key innovations: (1) an elite strategy to guide population evolution by learning from the best individual of the previous generation; (2) a dual chaotic mapping initialisation to enhance population diversity and ensure uniform distribution in the solution space; and (3) an adaptive T-distribution perturbation strategy to help the population escape local optima during the optimisation process. Experimental studies on CEC2017 and CEC2022 benchmarks demonstrate the effectiveness of DCTPSA, showing its competitive advantage over six baseline algorithms in solving global optimisation problems.
  Keywords: global optimisation; proportional integral derivative control; T distribution; chaos mapping; metaheuristic algorithm; population diversity; benchmark functions.
  DOI: 10.1504/IJAAC.2026.10075771
   
  
• Adaptive state augmented backstepping control with NN approximation for 3-DOF robot under backlash like hysteresis  
  by Riya Kumari, Ramesh Kumar, Gagan Deep Meena 
  Abstract: The presence of friction, backlash/hysteresis like non-linearities are quite common in nature. To adapt and compensate for the effect of these unmodelled dynamics and hysteresis, backstepping control with neural network approximation is designed. This paper presents a radial basis neural network with a Gaussian function to approximate the unknown dynamics while a sigmoidal function-based second neural network has been added to approximate the hysteresis effect. With the help of Lyapunov candidate functions, virtual control law for each subsystem and update law for the neural network have been obtained. Simulations have been carried out to validate the effectiveness of the purposed controller under different conditions of friction and hysteresis. The tracking performance has been observed on a three degree-of-freedom manipulator (3-DOF) where the tracking error for joints decreases along with the addition of neural network approximation. Phase portrait analysis and frequency response analysis for the manipulator states were also studied.
  Keywords: robotic manipulator; RBFNN; radial basis function neural network; backstepping control; Lyapunov function; 3-DOF; adaptive control.
  DOI: 10.1504/IJAAC.2027.10075794
   
  
• Design of new IMC parallel cascade control based smith predictor for industrial processes with large dead times using swarm based optimisation technique  
  by Somnath Garai, Neelbrata Roy, Ashoke Sutradhar, Anindita Sengupta, Rajeev Ranjan Pathak 
  Abstract: The series cascade control techniques have been well established in industries for controlling different process variables. In contrast to the series cascade control, parallel cascade control (PCC) has also been used in process engineering to control the process variables in some types of plants and some control structures for these PCC systems are available in literature. In the present work a novel and systematic internal model control based parallel cascade controller (IMC-PCC) design has been proposed. The framework uses a Smith predictor along with an IMC structure. The proposed structure uses four control components: i) a primary setpoint tracking controller; ii) a secondary disturbance rejection controller; iii) a primary stabilising controller; and iv) an IMC controller. The novelty of the proposed control structure is tracking setpoint, fast disturbance rejection and improved performance. Simulations with the proposed PCC structure on diverse process models like first-order integrating type time delay systems, and unstable first-order system with time delay yields improved control performance revealed by its remarkable effectiveness and adaptability compared to earlier reported structures for similar plants.
  Keywords: PCC; parallel cascade control; IMC; internal model control; disturbance rejection; double loop control.
  DOI: 10.1504/IJAAC.2027.10075937
   
  
• Adaptive fuzzy dual-channel event-triggered control for strict-feedback fractional-order nonlinear system  
  by Xiaobing Han, Zhiyao Ma 
  Abstract: This paper investigates the adaptive fuzzy dual-channel event-triggered control problem for strict-feedback fractional-order nonlinear systems with unknown dynamics. In the controller design, fuzzy logic systems (FLSs) are utilized to approximate the unknown nonlinear functions. To reduce the consumption of communication resources, a dual-channel event-triggered mechanism is proposed, which simultaneously activates the triggering conditions for both the filter and the controller. This approach effectively mitigates signal discontinuity after triggering and circumvents the issue of non-differentiability of virtual control signals. By integrating the proposed event-triggered scheme and fractional-order linear filters into the backstepping control framework, an adaptive fuzzy event-triggered control strategy is developed. Through the construction of fractional Lyapunov functions, it is rigorously proved that all closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB), and the tracking error converges to an arbitrarily small neighborhood of the origin by tuning a design parameter. The effectiveness of the proposed control method is validated via a simulation example.
  Keywords: fractional-order nonlinear systems; dual-channel event-triggered mechanism; adaptive fuzzy control; backstepping control; fractional-order linear filters; fractional-order stability criteria.
  DOI: 10.1504/IJAAC.2027.10076034
   
  
• Optimised series solution for input shaping in dual-manoeuvres overhead crane with hoisting  
  by Khalid Alghanim 
  Abstract: This work introduces an input shaping approach designed for overhead cranes involved in hoisting operations. Utilising an optimised convergent series solution, the study addresses the complexities of modelling input functions for dynamic hoisting systems which often challenge non-closed-form analytical methods. This technique enables precise control over crane manoeuvres involving variable cable lengths and hoisting speeds with a single input setup. Critical to crane operational efficiency and accuracy, the proposed method successfully manages diverse manoeuvring requirements while adhering to system constraints such as maximum input acceleration and designated hoisting speeds. Numerical simulations confirm the theoretical framework, demonstrating the method's efficacy in achieving minimal vibration amplitudes and maintaining consistent trolley velocities. The input shaping technique ensures operational precision in diverse conditions, meeting strict convergence criteria and significantly enhancing system performance.
  Keywords: overhead crane; command shaping; input shaping; series solution; hoisting; dual manoeuvre.
  DOI: 10.1504/IJAAC.2027.10076035
   
  
• Parametric optimisation of neural sliding controller for nonlinear coupling tank plant using central force optimisation algorithm  
  by Nguyen Anh Tuan, Ho Pham Huy Anh 
  Abstract: This study introduces a central force optimisation (CFO) approach to optimally identify the parameters of an advanced neural sliding mode controller (ANSMC) for liquid level regulation in a coupled tank (CTS) plant. The CTS represents an uncertain plant with high hysteresis along with nonlinear features, including sensor measured noise, valve outputs disturbance, etc. The proposed control model comprises an SMC control model integrated with an adaptive neural structure to real-time adjust the ANSMC coefficients and to regulate the nonlinear features, regardless the information of the investigated system. The CFO technique is applied to optimally identify the coefficients of the SMC (SMC-CFO) and the ANSMC (ANSMC-CFO), thereby reducing the training time of the neural model and enhancing the overall controller quality. Moreover, to verify the efficiency of the suggested control method, simulation benchmark tests are conducted using different reference liquid level signals and compared with the SMC-CFO scheme and the ANSMC without parameter optimisation. The simulation results show that the suggested ANSMC-CFO achieves higher superiority in comparison with the other advanced control methods.
  Keywords: CFO; central force optimisation method; ANSMC; adaptive neural sliding control algorithm; ANSMC-CFO; proposed ANSMC optimised with CFO; CTS; coupled tank system.
  DOI: 10.1504/IJAAC.2027.10076098
   
  
• Fuzzy tuned PI controller design for 6-DOF motions of electrically actuated Stewart platform manipulator: validation on real time manipulator for modern industrial applications  
  by Mintu Ghosh, Sibsankar Dasmahapatra 
  Abstract: Present real time work includes design, development and control of electrically actuated Stewart platform manipulator. 6-DOF motions of this parallel manipulator precisely controlled by Fuzzy tuned PI controller with various step demands combining transient and steady state responses along with square-wave tracking demand to orient the platform for modern manufacturing applications. Workspace identification with maximum ranges of surge, sway and heave translation about its neutral position are verified using inverse and forward kinematics as 0.135, 0.125 and 0.085 m respectively whereas that of roll and pitch are 22 degree with yaw 32 degree. Real time experimentation with one translational motion as heave and one rotational motion as yaw of the top platform have been explained in details focusing real time industrial applications like slot cutting, automatic welding and other robot based manufacturing techniques. Pay load capacity of 9000 N makes this platform suitable for material handling and flight simulator.
  Keywords: electric Stewart platform; inverse and forward kinematics; fuzzy tuned PI with SIMO system; 6-DOF motions; modern industrial applications.
  DOI: 10.1504/IJAAC.2027.10076099
   
  
• A dynamic masking attention-based reinforcement learning method with multi-start sampling for killweb-oriented operation loop decision-making  
  by Weiqin Ying, Chengqing Tan, Sen Huang, Chong Wang, Keke Tang, Zhishuo Tang 
  Abstract: The killweb-oriented operation loop decision-making problems constitute complex combinatorial optimisation tasks with multiple constraints and strong coupling. Existing reinforcement learning approaches often struggle with the scalability and convergence due to the huge action spaces for these problems. To address these challenges, this paper proposes a dynamic masking attention-based reinforcement learning method with multi-start sampling (DMARL-MSS) for these problems. The method incorporates an attention-based model with a dynamic masking mechanism to constrain the selection of equipment with feasible types and available communication channels at each decision step. Meanwhile, the method adopts a multi-start sampling strategy to enhance the diversity of solutions and training stability by sampling multiple trajectories originating from multiple different enemy targets simultaneously. The experimental results on synthetic operation loop decision-making problem instances indicate that the DMARL-MSS method obviously outperforms several existing metaheuristic algorithms and learning-based baselines in terms of solution quality, convergence speed, and generalisation capability.
  Keywords: combinatorial optimisation; reinforcement learning; attention model; killweb; operation loop.
  DOI: 10.1504/IJAAC.2027.10076224
   
  
• Fractional observer-based model-free controller applied to a cylindrical robot  
  by Mario Alberto Hernandez, Jose De Jesus Rubio , Francisco Javier Rosas, Manuel Olan, Jose Eduardo Hernandez, Luis Enrique Angeles, Victor Viveros, Jaime Pacheco 
  Abstract: Fractional calculus has been a topic of interest in the design of controllers and observers. An observer-based model-free controller is proposed in a recent work and will henceforth be referred to as observer-based model-free controller (OMFC). Based on this result, the fractional calculus is used to propose a fractional OMFC and will henceforth be referred to as fractional observer-based model-free controller (FOMFC) with the main objective to improve its performance. The main characteristic of the proposed FOMFC is that the Riemman-Liouville and Grünwald-Letnikov approaches are used to compute numerical solutions for fractional derivatives. The FOMFC is compared with the OMFC, the model-free proportional derivative controller with gravity compensation, and the model-free sliding mode controller for regulation of a cylindrical robot.
  Keywords: fractional calculus; fractional model-free controller; fractional model-free observer; cylindrical robot; fractional differential equations; approximation; fractional derivative operator.
  DOI: 10.1504/IJAAC.2027.10076247
   
  
• Real time optimisation of controller parameters for hardware implementation of active disturbance rejection control  
  by Shoaib H. Peerzada, Shahkar A. Nahvi, M.A. Bazaz, Majid H. Koul 
  Abstract: This paper proposes a hardware implementation of active disturbance rejection control (ADRC) with real-time parameter optimisation. The objective is to address the challenge of controlling nonlinear dynamic systems where the model is unknown and the load conditions vary significantly. The ADRC controller is designed to compensate for system uncertainties and disturbances, with the Genetic Algorithm (GA) being used for real-time optimisation of the system and observer parameters to achieve desired performance. The proposed approach is implemented on a Raspberry Pi platform, which executes the ADRC-GA algorithm and facilitates on-the-fly parameter optimisation. Experimental evaluations conducted under diverse load conditions, varying set point and settling time requirements demonstrate that the proposed technique achieves significantly improved tracking performance and disturbance rejection compared to conventional ADRC controllers with fixed parameters. The implications of this work extend to mechatronics and other domains requiring robust control of nonlinear and time-varying systems. The hardware implementation offers an accessible pathway for deploying adaptive control strategies in real-world applications, paving the way for enhanced performance in cost-sensitive environments.
  Keywords: control systems; genetic algorithm; ADRC; active disturbance rejection control; parameter optimisation.
  DOI: 10.1504/IJAAC.2027.10076248
   
  
• Gap adjustment strategy of disc refiner based on sliding mode control  
  by Bo Wang, Ruibin Li, Shaomin Huang 
  Abstract: As the core equipment in the pulp refining process of papermaking, the precision of Disc Refiner gap control in Disc Refiners directly affects fiber quality and production efficiency. Gap adjustment faces challenges such as continuous friction, high-frequency disturbances, and parameter perturbations. Traditional PID control, due to issues like excessive overshoot (>3%) and weak anti-interference capability, often leads to uneven fiber fibrillation, surging energy consumption, and accelerated equipment wear. This paper proposes a Sliding Mode Control (SMC) strategy, designing a sliding mode controller via Lyapunov stability theory, integrating nonlinear control and chattering suppression methods to enhance control performance. Simulation and experimental results demonstrate that the SMC strategy achieves an overshoot <1%, reduces disturbance recovery time by 90% compared to PID, and maintains control error fluctuations within 0.07 mm under hydraulic rod stiffness deviations, effectively resolving dynamic gap misalignment. This approach provides a novel pathway for high-precision and robust control of Disc Refiners, advancing the intelligent upgrading of pulp refining processes.
  Keywords: disc refiner gap; hydraulic system; nonlinear control; SMC; sliding mode control; dynamic adjustment.
  DOI: 10.1504/IJAAC.2027.10076280
   
  
• Distributed sliding mode observer-based leader-follower synchronisation for affine nonlinear multi-agent chaotic systems with uncertain dynamics  
  by Reza Ghasemi 
  Abstract: This paper presents a leader-follower consensus procedure for a wide range of multi-agent systems (MAS) characterised by nonlinear dynamics and uncertainties, utilising algebraic graph theory for modelling. The study develops a sliding mode observer-based controller, assuming that each follower agent aligns with the leader. Unlike previous research, which has not enhanced the stability and consensus of sliding mode observer-based controllers for nonlinear MAS, this approach focuses on cooperative controllers designed for nonlinear systems subject to uncertainties and external disturbances. The main novelty of this approach is designing a distributed sliding mode controller based on a nonlinear robust observer with overall stability in the presence of both bounded disturbances and uncertainties. The simulation results show (1) the chattering phenomena decrease at least = 98%, (2) the proposed robust observer compared to Luenberger observer have a promising performance. Simulations demonstrate the accuracy and efficiency of this approach for specific dynamics within multi-agent systems.
  Keywords: disturbance; leader-follower consensus; MASs; multi-agent systems; distributed observer; sliding mode controller.
  DOI: 10.1504/IJAAC.2027.10076281
   
  
• Research on obstacle avoidance method for agricultural robots based on vision and fuzzy logic  
  by Aoao Wang, Dongshu Wang, Yihai Duan 
  Abstract: Natural agricultural environments are complex and dynamic, making traditional predefined-path navigation systems ineffective at perceiving and avoiding unknown obstacles. To solve this, a vision- and fuzzy logic (FL)-based obstacle avoidance system is proposed. It uses an RGB-D camera and YOLOv5 for obstacle recognition/localisation, integrates AHRS heading data to map obstacles to a global coordinate system, and relies on fuzzy inference to output speed control values for agricultural vehicles. Tests in robot operating system (ROS) simulations and real fields, compared with deep Q-network (DQN) and risk index (RI) methods, show the FL system has shorter avoidance time and trajectory length, and more stable velocity control amid farmland uncertainties like uneven terrain and sensor noise.
  Keywords: agricultural machinery; obstacle detection; FLC; fuzzy logic control; obstacle avoidance control.
  DOI: 10.1504/IJAAC.2027.10076283
   
  
• AGWO-tuned hybrid controllers for enhanced frequency stability in renewable-penetrated microgrids  
  by Boris Arnaud Notchum Deffo, Anass Bakouri, Tijani Bounahmidi 
  Abstract: This paper proposes an advanced load frequency control (LFC) strategy for isolated microgrids with high renewable energy penetration. The system includes photovoltaic and wind sources, a thermal plant, a fuel cell, an aqua electrolyser, and hybrid storage units. To address frequency instability caused by low inertia and renewable intermittency, a dual-controller scheme is developed: a siding mode controller (SMC) based on linear matrix inequalities (LMI) for robust secondary control, and a cascade PDn-PI controller to provide virtual inertia via storage systems. Both controllers are optimally tuned using the augmented grey wolf optimisation (AGWO) algorithm. Simulation results in MATLAB/Simulink show significant performance gains over conventional PID controllers, including reduced overshoot, faster settling time, and enhanced robustness under varying loads and generation.
  Keywords: microgrids frequency control; virtual inertia; sliding mode control; cascade PDn-PI; AGWO; augmented grey wolf optimisation.
  DOI: 10.1504/IJAAC.2028.10076603