Forthcoming and Online First Articles

International Journal of Automation and Control

International Journal of Automation and Control (IJAAC)

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International Journal of Automation and Control (24 papers in press)

Regular Issues

  • Error-based ADRC Approach of lower Knee Exoskeleton System for Rehabilitation   Order a copy of this article
    by Nasir Alawad, Amjad Jaleel Humaidi, Ahmed Alaraji 
    Abstract: In this study, active disturbance rejection control (ADRC) has been designed to control exoskeleton system for rehabilitation at knee level and to replace the exercises made by physicians with systematic training device. The time derivative of reference input and feed-back signals is an evitable in most ADRC schemes. To alleviate the burden due to derivative actions, the idea of proposing error-based ADRC (EADRC) has been introduced. In conventional ADRC scheme, the extended state observer (ESO) is the core element of controller to estimate both the states of the system and the exerted disturbance. The EADRC utilises the estimates in the error sense rather than the actual states. The EADRC technique is compared to traditional ADRC and the numerical results showed that the proposed EADRC outperforms the conventional ADRC in terms of tracking errors, noise and load rejection capabilities for the system subjected to noise and load uncertainties.
    Keywords: Exoskeleton system; ADRC; robustness; stability; disturbance rejection.
    DOI: 10.1504/IJAAC.2025.10061844
  • Exploring Reinforcement Learning Techniques in the Realm of Mobile Robotics   Order a copy of this article
    by Zeeshan Haider, Muhammad Zeeshan Sardar, Ahmad Taher Azar, Saim Ahmed, Nashwa Ahmad Kamal 
    Abstract: Mobile robots are intelligent machines that can move and perform tasks in different environments. They have gained massive popularity across a variety of applications, including healthcare, agriculture, hospitality, exploration, surveillance, transportation, entertainment, and even military deployments. The key factor enabling the autonomy of mobile robots lies in the reliability, safety, and robustness of their navigation systems, without the need for human intervention. Achieving such a high level of autonomy has required extensive research and development efforts, encompassing both classical approaches and the latest advancements in artificial intelligence (AI) techniques. This review paper specifically focuses on the deep reinforcement learning (DRL) techniques employed for mobile robots. It provides a comprehensive look into the most significant DRL-based navigation and control algorithms for mobile robots. Sub-components of mobile robot navigation perception, mapping, localization, and motion planning are well delineated under the lens of DRL and conventional methods.
    Keywords: Mobile robots; deep reinforcement learning; navigation; control; path planning; machine learning.
    DOI: 10.1504/IJAAC.2024.10062261
  • Contamination Detection in the Cultivation of Leukocyte Based on Image Sparsity Evaluation   Order a copy of this article
    by Lianghong Wu, Zhiyang Li, Liang Chen, Cili Zou, Hongqiang Zhang 
    Abstract: The contamination in the cultivation of cell seriously affects the reliability and reproducibility of experimental results. In this paper, it proposes a sparse matrix clustering (SMC) method based on the principle of matrix sparsity to automatically detect the contamination in leukocyte. Firstly, the image segmentation and local adaptive binarization techniques are used to eliminate the noise points and shadows. Then, a scoring map of image sparsity based on the pixel distribution of segmented images is proposed to index the pollution degree of the leukocyte. By dynamically determining the threshold for evaluating image sparsity based on the maximum distributed pixels on the scoring map, the image sparsity is used as a feature for classification. Experimental results show that this method achieves an accuracy of 98.8% for detecting contamination in leukocyte culture images with fast detection speed, which can be used as an efficient cell contamination detection approach in biomedical field.
    Keywords: Keywords: image sparsity evaluation; leukocyte contamination detection; image segmentation; local adaptive binarization.
    DOI: 10.1504/IJAAC.2025.10062262
  • Modifier-adaptation-based RTO Scheme for Water Distribution Networks under Demand and Parametric Uncertainties   Order a copy of this article
    by Jaivik Mankad, Nitin Padhiyar, Balasubramaniam Natarajan, Babji Srinivasan 
    Abstract: The goal of pressure management in a water distribution network (WDN) is to avoid losses due to excessive pressure while meeting the minimum target pressure at nodes. Since nodal demands can fluctuate, real-time control of nodal pressures is critical for normal network operation. Optimising the operation of WDN using a model with uncertain parameters and unaccounted nodal demands generates solutions that are not truly optimal and may even be infeasible. This work aims to achieve real-time optimal operation of a WDN in the presence of various uncertainties. A modifier-adaptation (MA)-based real-time optimisation (RTO) strategy is used to drive the WDN to its optimal point. However, the MA-based RTO scheme assumes knowledge of key variables which may not be available in practice. Therefore, a Bayesian matrix completion approach for robust state estimation is used to impute unknown model parameters with limited measurements. Simulation results demonstrate the ability of this approach.
    Keywords: real-time optimisation; modifier-adaptation; plant-model mismatch; water distribution networks; uncertain demand; pressure control.
    DOI: 10.1504/IJAAC.2024.10062336
  • Research on steelmaking-continuous casting cast batch planning based on improved surrogate absolute-value Lagrangian relaxation framework   Order a copy of this article
    by Congxin Li, Liangliang Sun 
    Abstract: Cast batch planning (CBP) is the bottleneck of batch planning in the steelmaking-continuous casting-hot rolling (SM-CC-HR) section. With the rapid development of the market-oriented demand of steel enterprises to multiple species, small batches, and on-time delivery, the batch planning integrated production process has dramatically increased the flexibility of the CBP as well as the functional requirements of the time dynamic balance. Therefore, it is of great significance to research the method of CBP to improve production efficiency and reduce material and energy consumption. In this paper, based on the improved surrogate absolute-value Lagrangian relaxation (ISAVLR) framework, the heuristic method based on a multiplier iteration strategy with controllable gradient direction combined with a local search (LS) algorithm is proposed. The 'zigzagging' problem in the traditional Lagrangian relaxation (LR) is overcome and the solution efficiency is improved while the original problem is provided with tighter lower bounds.
    Keywords: steelmaking-continuous casting; ISAVLR; improved surrogate absolute-value Lagrangian relaxation; CBP; cast batch planning; heuristic.
    DOI: 10.1504/IJAAC.2025.10062754
  • Model predictive control with constraints based on PSO and fuzzy logic applied to the control of coupled longitudinal-lateral dynamics of the autonomous vehicle   Order a copy of this article
    by Rachid Alika, El Mehdi Mellouli, Tissir Elhoussaine 
    Abstract: In this paper, a strategy for controlling the longitudinal and lateral dynamics of an autonomous vehicle is developed. This strategy is based on the model predictive control (MPC) with constraints combined with LPV form. The three degrees of freedom (3DOF) model of the autonomous vehicle is used. The cornering stiffness is approximated by a fuzzy logic type Takagi-Sugeno, with the aim of finally approximating the nonlinear lateral forces. In order to improve the systems performance, constraints for controller inputs and also for systems outputs are defined. The MPC weights are determined using the particle swarm optimisation (PSO). The objective of this strategy is to follow the reference trajectory of the autonomous vehicle while reducing the lateral and longitudinal displacement error. The steering angle and the longitudinal acceleration are the control inputs, the outputs of this system are the longitudinal velocity, the yaw angle, the longitudinal and lateral displacement. The system is multi-input and multi-output (MIMO) and has non-linear dynamics. Simulation results show some improvements over the literature.
    Keywords: autonomous vehicles; MPC; model predictive control; MPC constraints; LPV system; PSO; particle swarm optimisation; MIMO system; nonlinear dynamic; path planning; fuzzy logic.
    DOI: 10.1504/IJAAC.2025.10062896
  • FPGA-based performance evaluation of backstepping control and computed torque control for industrial robots   Order a copy of this article
    by Arezki Fekik, Hocine Khati, Ahmad Taher Azar, Mohamed Lamine Hamida, Hakim Denoun, Nashwa Ahmad Kamal 
    Abstract: In this paper, a comparative study is conducted on two nonlinear control techniques: state feedback control through backstepping and computed torque control. The study focuses on their application to the industrial robot PUMA 560. The primary goal is to assess the trajectory tracking accuracy and speed achieved by these methods. To achieve this objective, both control techniques are employed on the Zed board Zynq FPGA platform, encompassing both simulation and hardware systems. Subsequently, the experimental results are thoroughly analyzed and compared, aiming to accentuate the unique advantages and constraints associated with each method.
    Keywords: field-programmable gate array; FPGA; backstepping control; computed torque control; CTC; Zed board Zynq; PUMA 560.
    DOI: 10.1504/IJAAC.2025.10062960
  • Kharitonov-Hurwitz analysis based robust decentralized PID controller for benchmark industrial processes using nonlinear constraint optimization   Order a copy of this article
    by K.R. Achu Govind, Subhasish Mahapatra, Soumya Ranjan Mahapatro 
    Abstract: The interconnected systems featuring a combination of interrelated variables present significant challenges in the control and stability aspects of industrial chemical processes. These systems demonstrate complex interactions, and time delays, contributing to sub-optimal performance. Traditional controllers struggle with variable interactions, resulting in poor disturbance rejection and imprecise tracking. To overcome these limitations, a robust decentralised proportional-integral-derivative (PID) controller is proposed, exploiting nonlinear constraint optimisation. The key objective is to enhance the robustness and performance of the PID controller through a comprehensive Kharitonov-Hurwitz analysis. The controller is formulated to minimise overshoot, meeting predefined design specifications. Validation of the proposed approach is conducted on benchmark industrial processes. Simulation results demonstrate its superior performance across various metrics, including settling time and overshoot, surpassing existing methodologies in the field. Rigorous assessments under multiplicative input uncertainty and stability evaluations using the Kharitonov-Hurwitz theorem validate the approach.
    Keywords: FOPDT System; Decentralized Control; PID; Robust control; Model Uncertainty.
    DOI: 10.1504/IJAAC.2024.10063220
  • A comparison among conventional and unconventional proposed PID control structures   Order a copy of this article
    by Mohamed Jasim Mohamed  
    Abstract: In this paper, different proportional-integral-derivative (PID) control structures were proposed and derived mathematically from the equation of the conventional PID control structure. Some of these PID control structures are rarely used in the literature, but the rest of them are not mentioned. The purpose of this paper is to explore and examine the usefulness of these unconventional PID control structures by comparing them with the conventional PID control structure. Initially, simple and well-known control objectives are used in comparisons like integral time absolute error (ITAE) and integral time square error (ITSE). So, these different PID control structures are used in four different examples. The particle swarm optimisation (PSO) algorithm is used to find the optimal values of the parameters for each control structure. From these comparisons, the results show that other unconventional PID control structures outperform the conventional PID control structure and the choice of the conventional PID control structures is not always the best.
    Keywords: linear systems; PSO; particle swarm optimisation; PID controller structure; time domain performance index; windup techniques.
    DOI: 10.1504/IJAAC.2025.10063741
  • Design of IMC-PID controller with PSO-based fractional filter for SOPTD processes   Order a copy of this article
    by Parikshit Kumar Paul, Chanchal Dey, Rajanikanta Mudi, Pubali Mitra Paul 
    Abstract: Over the decades PID controllers are the main choice for industrial applications. Second Order plus Time Delay (SOPTD) models are considered in industry because of better process dynamics than other processes. The internal model control (IMC) technique in a PID form is mostly designed for SOPTD processes. But in this scheme, limited numbers of tuning rules are available to provide satisfactory performances. An IMC-PID controller with cascading fractional filter can offer better control for the SOPTD processes. But, choosing its appropriate fractional order value is a crucial task. So, PSO based fractional IMC filter with a PID controller is proposed. The effectiveness of the proposed controller is performed on several types of SOPTD processes both in set-point tracking and load variations. The robustness and sensitivity functions are compared for the proposed scheme with the other reported control strategy toward achieving the best possible controller structure for SOPTD processes.
    Keywords: IMC; internal model control; fractional filter; PSO; particle swam optimization; second order plus time delay process; PID controller.
    DOI: 10.1504/IJAAC.2025.10063907
  • Multi-objective optimisation for AGV and machine integrated scheduling problem considering battery consumption rate   Order a copy of this article
    by Bin Wu, Yuchao Ding 
    Abstract: With the advancement of eco-friendly manufacturing and smart production, researchers have increasingly focused on the AGV and machine integrated scheduling problem, considering energy consumption. However, the current research overlooks the varying battery consumption rates of AGV under different operational conditions. This paper addresses this gap by dissecting AGV battery usage into load and no-load scenarios and develops a multi-objective optimization model for the integrated scheduling problem. An improved Non-Dominated Sorting Genetic Algorithm (I-NSGA-II) is presented to solve the model. In the algorithm, a novel two-segment real number encoding approach for machine/AGV assignment and process operations is proposed. The Taguchi analysis was used to discuss the key parameters of the algorithm, and experiments were conducted to perform sensitivity analysis on the model. Simulation results demonstrate that the proposed algorithm outperforms three other widely recognized algorithms in the benchmark.
    Keywords: multi-objective optimisation; scheduling; automatic guide vehicle; flexible job shop; NSGA-II.
    DOI: 10.1504/IJAAC.2025.10064156
  • Modified single phase sliding mode control for a class of mismatched uncertain systems   Order a copy of this article
    by Viet Anh Duong  
    Abstract: This paper presents a new sliding mode control concept for guaranteeing the invariance of a linear time varying uncertain system to mismatched uncertainties over all time. Two sets of exponential-type switching surfaces are proposed to eliminate the effect of the mismatched uncertainties in the sliding mode. By nature of these surfaces, the reaching phase is eliminated to avoid the non-robustness associated with that phase, at the same time, the system states are always confined to the new sliding mode from the initial time. Moreover, necessary and sufficient invariance conditions are given such that mismatched uncertainties completely vanish from the entire response of the system at the very beginning time. A linear matrix inequalities-based design method for the switching surfaces is also given. In addition, a control law is constructed to maintain the sliding mode. Finally, numerical results are presented to demonstrate the effectiveness of the proposed concept.
    Keywords: invariance condition; SMC; sliding mode control; matched and mismatched uncertainty; LMIs; linear matrix inequalities; invariance property.
    DOI: 10.1504/IJAAC.2025.10064448
  • A multi-threaded parallel iterative greedy algorithm for distributed flowshop group scheduling problems with preventive maintenance   Order a copy of this article
    by Xiaobin Sun, Hongyan Sang, Wanzhong Wu, Yasheng Zhao, Qiuyang Han 
    Abstract: In actual production, factories not only pursue productivity, but also pay attention to the reliability and stability of the production process. For the continuity of production machines, this paper investigates the distributed flow shop group scheduling problem with preventive maintenance (DFGSP/PM). In order to minimize the makespan, a mathematical model of DFGSP/PM is developed and a multi-threaded parallel iterative greedy algorithm (MPIG) is proposed. A greedy NEH method is designed to generate the initial solution. In order to couple the two subproblems of DFGSP/PM, a two-stage destruction and reconstruction is designed. A multi-threaded parallel local search strategy (MPLS) is introduced to improve the search efficiency of the MPIG, so that the optimal insertion positions of the groups in the sequence can be searched faster and the two subproblems can be coupled effectively. Effectiveness analysis has demonstrated that the proposed MPIG significantly reduces computation time and expands the search space.
    Keywords: distributed flowshop scheduling; preventive maintenance; iterative greedy algorithm; group scheduling; parallel computing.
    DOI: 10.1504/IJAAC.2025.10064456
  • Reduced-order modelling-based FOPID controller design for interval-model Zeta converter using Bode envelope   Order a copy of this article
    by V.P. Meena, Preeti Meena, V.P. Singh, Ahmad Taher Azar, Saim Ahmed 
    Abstract: This article proposes the design of a fractional-order-proportional-integral-derivative (FOPID) controller for an interval-modeled Zeta converter, employing the Bode envelope method. The approach employs reduced-order modeling, employing direct truncation and Routh-Pad{'e} approximation for numerator and denominator polynomials, respectively. The Zeta converter's mathematical model, derived via state-space averaging, is initially a fourth-order interval model, subsequently reduced to a first-order interval model. The primary objective is to design an FOPID controller meeting specific performance criteria, such as desired gain crossover frequency and phase margin. To achieve this, the teacher-learning-based optimization (TLBO) algorithm minimizes the objective function, determining optimal controller parameters. Step and Bode responses are provided to demonstrate the controller's effectiveness and applicability.
    Keywords: reduced-order modelling; FOPID controller; interval model; Zeta converter; Bode envelope; TLBO; teacher-learning-based optimisation; SSA; state-space averaging.
    DOI: 10.1504/IJAAC.2025.10064457
  • Deep learning optimisation for spatial wind power forecasting: a data driven approach to grid stability enhancement   Order a copy of this article
    by Nashwa Ahmad Kamal, Mohamed ElSobky, Ahmed M. Ibrahim, Zeeshan Haider 
    Abstract: While wind power has surged as a clean energy source in recent decades, its inherently unstable nature poses a challenge to grid stability. However, forecasting challenges remain, including inconsistent historical data for individual turbines and growing errors in multi-step predictions. This paper presents a novel solution to tackle the intricate problem of spatial dynamic wind power forecasting, leveraging the latest advancements in deep learning-based forecasting models. To achieve the best possible settings for the wind power forecasting model, we prepared the solution after exploring different dimensions including deep learning models, features selection, scaling methods, look-back window size, and optimizers. We selected 6 state-of-the-art forecasting models, 3 scaling methods, 8 optimizers, and a look-back window size ranging from 1 to 14 days. Our findings demonstrate the effectiveness of the proposed framework and establish a foundation for further advancements in wind power forecasting accuracy and grid stability.
    Keywords: wind power forecast; forecast; deep learning; SDWPF; spatially dynamic wind power forecasting; turbine.
    DOI: 10.1504/IJAAC.2025.10064572
  • Improving the diagnosis of partial shading faults by utilising artificial neural networks optimised with the whale optimisation algorithm   Order a copy of this article
    by Saliha Sebbane, Nabil El Akchioui 
    Abstract: This paper introduces a hybrid approach combining an Artificial Neural Network (ANN) with the Whale Optimization Algorithm (WOA) to diagnose partial shading in photovoltaic (PV) systems. It includes two models: WOA-ANN-Classification, which detects and classifies shading, and WOA-ANN-Localization, which identifies the shading location. The approach was tested against other algorithms like Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Differential Evolution (DE), using metrics such as mean square error, CPU time, and accuracy. Results showed the WOA-ANN models outperformed others, with the classification model achieving 99.99% accuracy and the localization model 99.96%. These findings highlight the approach's potential to improve fault diagnosis in PV systems, enhancing energy efficiency.
    Keywords: photovoltaic (PV) system; partial shading fault; ANN; artificial neural network; WOA; whale optimisation algorithm; fault detection; classification;localisation.
    DOI: 10.1504/IJAAC.2025.10064965
  • Elevator fault classification based on multi-grained cascade forest with variable importance measure   Order a copy of this article
    by Yijin Ji, Haoxiang Sun, Xu Zhou 
    Abstract: Current deep learning based elevator fault classification models are mostly elevator-specific due to the lack of a general fault dataset. The performance is also significantly limited if the training data is insufficient or the hyper-parameter is not well tuned. In this work, a new elevator fault dataset is firstly proposed for different elevators based on massive fault recordings from 96,333 elevator emergency disposal service platform and the basic parameters of elevators. Variable importance is then evaluated using random forest with mean decrease accuracy (MDA) for better feature understanding. Using the variables with high importance as input features, a multi-grained cascade forest model is finally proposed for more accurate and faster elevator fault classification. Experiment results validate the superior performance of the proposed model, including an easy training on relatively less training data, a higher classification accuracy than traditional models, and a higher running efficiency than not using variable importance measure.
    Keywords: elevator fault; fault classification; variable importance; importance measure; random forest; mean decrease accuracy; multi-grained cascade forest.
    DOI: 10.1504/IJAAC.2025.10065314
  • Deep learning rotor fault detection using the Gramian angular field and the Markov transition field encoding approaches   Order a copy of this article
    by Aroui Tarek, Marmouch Sameh 
    Abstract: Using deep learning techniques for diagnostic purposes has garnered considerable interest and demonstrated encouraging outcomes across diverse fields. Convolutional neural networks (CNN) are extensively employed in image-based diagnostic tasks. This paper proposes an approach that transforms the temporal signal of the stator current into visual representations. The combined utilization of the Gramian angular field (GAF) and the Markov transition field (MTF) allows for a comprehensive analysis of stator current data. This combined approach is associated with two deep learning models (VGG19 and RESNET50) for rotor broken bars detection. We have used an experimental database acquired under varied load conditions and with different types of rotor faults to demonstrate the reliability of our approach. The results obtained demonstrate the efficacy of the proposed strategy.
    Keywords: rotor faults; diagnosis; GAF; Gramian Angular Field; Markov Transition Field; VGG19; RESNET50.
    DOI: 10.1504/IJAAC.2025.10065499
  • Bio-inspired evasion strategies under variable evader speed   Order a copy of this article
    by Lairenjam Obiroy Singh, Devanathan Rajagopalan 
    Abstract: The pursuit evasion game (PEG) is a natural phenomenon with implications for civilian and military applications. Researchers have developed methods to analyse PEG dynamics under various bio-inspired strategies, providing valuable insights into possible applications. Studies assume constant speeds for both the pursuer and evader, but this can result in less agile pursuit. This paper explores the trade-off between higher speed and less agility when the evader increases speed as the pursuer approaches. By simulating PEG trajectories, nine combinations of bio-inspired pursuer-evader strategies were tested using computer simulations. Results showed that a higher average speed may delay the evader's capture or even result in their escape in some instances. However, computer simulations indicate a mixed outcome for evader escape performance in cases of sudden turns and non-reactive evader strategies. The paper's results contrast existing results that rely solely on numerical computation of PEG outcomes under varying evader speeds.
    Keywords: bio-inspired; closed-loop control; differential game theory; feedback laws; pursuit evasion game; PEG.
    DOI: 10.1504/IJAAC.2024.10061771
  • Real-time order picking of a robotic put wall: a simulation-based metaheuristic optimisation   Order a copy of this article
    by Jianbin Xin, Ziyuan Kang, Andrea D'Ariano, Lina Yao 
    Abstract: A robotic put wall has the potential to significantly enhance picking productivity in the logistics industry. This paper introduces a new computational method for scheduling a robotic put wall system that processes randomly arriving items. The method comprises a simulation-based model and a customised metaheuristic that optimises performance at regular intervals. The simulation model is developed using advanced discrete-event software that can include operational details of the picking process. The genetic algorithm with a new encoding scheme is tailored to solve the combinatorial optimisation problem of determining the appropriate destinations. To evaluate the proposed method, case studies based on real-world applications in a put wall manufacturing company were used. The method outperforms three rule-based real-time scheduling methods, as demonstrated by the results. Moreover, the integrated approach can determine the minimum number of vehicles required.
    Keywords: order picking system; robotic put wall; real-time scheduling; simulation-based optimisation.
    DOI: 10.1504/IJAAC.2024.10061126
  • Decentralised event triggered receding horizon online charge management of electric vehicles   Order a copy of this article
    by Maryam Amirabadi Farahani, Mohammad Haeri 
    Abstract: In this work, energy management of home customers with electric vehicles and renewable resources is modelled in the form of multi-agent systems. The agent decisions affect the others and the mean field game theory could provide a good solution for decision-making and control in multi-agent systems with a large number of agents. Due to uncertainties in the number of cars, power consumption, and production, online optimisation process is proposed by using the receding horizon concept of predictive control. The main problem in such processes is the calculations each agent should perform every hour. Hence, an event-based optimisation is employed to reduce the computational load. The main contribution of the present work is to optimise electric vehicles charging level in a decentralised and online manner in order to keep the load profile smoother in certain interval while reducing the computational complexity.
    Keywords: decentralised control; predictive control; electric vehicles charging; mean field game; computation load.
    DOI: 10.1504/IJAAC.2024.10061047
  • Design of integral sliding mode control with performance comparison for uncertain TITO process   Order a copy of this article
    by Vijaykumar S. Biradar, Gajanan M. Malwatkar 
    Abstract: In this paper, integral sliding mode control (I-SMC) law is designed and discussed for the two-input two-output (TITO) process. In the first step, the TITO process has been decoupled using ideal decoupler and each of the decoupled subsystem is reduced into first-order-plus-delay-time model. The reduced FOPDT model of each of the decoupled subsystems is extensively used to obtain decentralised sliding reaching laws. The designed I-SMC is used in decentralised fashion with decoupler to achieve the regulation performance of the process. The presented decentralised PID is used for performance comparison, in addition to prevalent methods. In the modified PID method, a second order plus delay time model is obtained by analytical method and the parameters of single-input-single-output decentralised controllers are obtained using reduced SOPDT model. The performance I-SMC method is compared with decentralised PID controllers and other prevalent methods and seems to be suitable for TITO process applications.
    Keywords: integral sliding mode control; decoupler; decentralised PID controller; model reduction; performance comparison.
    DOI: 10.1504/IJAAC.2024.10061269
  • Research on autonomous operation method for minimally invasive surgical robot   Order a copy of this article
    by Longwang Yue 
    Abstract: Autonomous minimally invasive surgical (MIS) robot is an important research content of surgical robot. In order to improve the intelligent operation level of the MIS robot, the authors made the research on autonomous operation method for the MIS robot. The autonomous operation method includes the following four steps: recording the manual surgical operation, extracting the surgical information, constructing the autonomous operation strategy performing autonomous surgical operation. With the designed symmetrical cable-driven MIS robot, which can record and extract the manual surgical skills, an autonomous control strategy was constructed for the MIS robot based on the back-propagation (BP) neural network. The feasibility of the control method was verified with the symmetrical cable-driven MIS robot platform. The research of this paper can not only improve the intelligent level of MIS by realising autonomous operation of complex operations, such as suturing, knotting and so on, but also improve the safety of MIS, the quality and efficiency of surgery, and reduce the burden on doctors and patients.
    Keywords: minimally invasive surgical; MIS; autonomous operation; control strategy; motion trajectory.
    DOI: 10.1504/IJAAC.2024.10061191
  • Earthwork allocation optimisation based on cut-fill matching and transportation path planning   Order a copy of this article
    by Jing Yu, You Huang, Lining Xing, Zizhou Zhao, Mingshun Li 
    Abstract: Earthwork allocation is a critical component of engineering construction projects, with the objective of reducing costs and shortening the construction period. While previous research has focused on solving the cut-fill matching problem, there is a lack of study on mechanical transportation path planning. This study introduces a matching model for cut-fill that minimises construction costs and mechanical transfer distance. Moreover, a hybrid ant colony-greedy model and algorithm are proposed to address the transportation path planning problem. To demonstrate the effectiveness of the model and algorithm, an earthwork allocation project is examined using the earthwork allocation model and its solution algorithm. Experimental results show that the two-stage allocation model and algorithm successfully address earthwork allocation challenges. Additionally, the AC-GA algorithm provides a superior earthwork allocation scheme.
    Keywords: earthwork allocation; cut-fill matching; path optimisation; linear programming; ant colony-greedy algorithm.
    DOI: 10.1504/IJAAC.2024.10062009