International Journal of Modelling, Identification and Control (23 papers in press)
Designing Route Guidance Strategy with Travellers Stochastic Compliance: A Bi-level Optimal Control Procedure
by Wei-li Sun, Ling-long Hu, Ping Li, Hui Wang
Using self-constructing recurrent fuzzy neural networks for identification of nonlinear dynamic systems
by Qinghai Li, Ye Lin, Rui-Chang Lin
Abstract: In this paper, the self-constructing recurrent fuzzy neural network (SCRFNN) is applied for nonlinear dynamical system identification (NDSI). The SCRFNN is a novel fuzzy neural network (FNN) by adding a recurrent path in each node of the hidden layer of self-constructing fuzzy neural network, which contains two learning phases. Specifically, the structure learning is based on partition of the input space and the parameter learning is based on the supervised gradient descent method using a delta adaptation law. This simple and efficient FNN can decrease the minimum firing strength in each learning cycle and the number of hidden neurons, and is able to generate a FNN with high accuracy and compact structure compared with several other neural networks. The performance of SCRFNN in NDSI is further verified in simulation.
Keywords: self-constructing recurrent fuzzy neural network; nonlinear dynamic system identification; supervised gradient descent method.
A biotech measurement scheme and software application for the level determination of persons functional reserve based fuzzy logic rules
by Riad Taha Al-kasasbeh, Nikolay Korenevskiy, Adnan Mukattash, Altin Aikeeva, Dmitry Titov, Maksim Jurievich Ilyash
Abstract: The level of apersons functional reserve is determined by a number of different indicators, such as power imbalance of meridian structures, psycho-emotional tension, intellectual and physical exhaustion, parameters of pulse and arterial pressure at the impact of the dosed intellectual and physical activities on the basis of heterogeneous fuzzy model usage. The functional reserve helps to diagnose a lot of diseases. The theoretical results obtained in the form of fuzzy models are recommended in medical practice with cofficient of confidence at least 95%.
Keywords: level of functional reserve; psycho-emotional pressure; intellectual exhaustion; physical exhaustion; heterogeneous fuzzy models.
U-model enhanced MIMO decoupling control of thickness and plate type of cold rolling temper mill
by Pengwei Li, Weicun Zhang
Abstract: This paper presents a new design procedure for control of thickness and plate type of cold rolling temper mill. A novel concept for control system design, U-model-based design methodology with expanded Multi-Input and Multi-Output (MIMO) formulation for this application, makes separation of specifying closed loop performance-controller from process models. In contrast to classical and methodologies, this takes two parallel formations: 1) designs an invariant virtual controller with specified closed loop transfer function in a feedback control loop; 2) determines real controller output by resolving the inverse of the plant U-model. Further, this study analyses some of the associated properties for assurance and guidance for ongoing theoretical development and applications. To validate the design procedure, it takes several computational experiments to generate simulation results, the bench tests show the efficiency and effectiveness of the proposed MIMO U-model-based control system design.
Keywords: cold rolling temper mill; plate type; plate thickness; U-model; U-control; model independent control system design.
A new chaotic dynamical system with a hyperbolic curve of rest points, its complete synchronisation via integral sliding mode control and circuit design
by Sundarapandian Vaidyanathan, Aceng Sambas, Chang-Hua Lien, Babatunde A. Idowu
Abstract: A new 3-D dynamical system exhibiting chaos is introduced in this work. The proposed nonlinear plant with chaos has a hyperbola of rest points. Thus, the new nonlinear plant exhibits hidden attractors. A detailed dynamic analysis of the new nonlinear plant using bifurcation diagrams is described. The new nonlinear plant shows multi-stability and coexisting attractors. The synchronisation of the new nonlinear plant with itself is achieved using Integral Sliding Mode Control (ISMC). Finally, a circuit model using MultiSim of the new 3-D nonlinear plant with chaos is carried out for practical use.
Keywords: chaos; chaotic systems; circuit design; synchronisation.
Towards improving constrained robust model predictive control with free control moves
by Xianghua Ma, Shuang Fang
Abstract: An existing robust model predictive control (RMPC) algorithm parameterises the infinite horizon control moves into a set of free control moves over a fixed horizon and a state feedback law in the terminal region. This paper is towards further improving the feasibility and optimality of this kind of RMPC. The improvement is by introducing an extended parameter-dependent Lyapunov function to substitute the original parameter-dependent Lyapunov function. Correspondingly, the terminal-weighting matrix is extended parameter-dependent, the local control law is a non-parallel distributed compensation, and the terminal constraint set is an intersection of more ellipsoids. The new technique is demonstrated by a simulation example.
Keywords: model predictive control; polytopic description; parameter-dependent Lyapunov function; non-parallel distributed compensation law; linear matrix inequality.
Modelling of multi-timescale demand response for power markets
by Dan Zhou, Huiwen Dai
Abstract: Demand response as an important part of smart grid makes the consumers participate in market operation that can lead to the benefit of both the utility and the consumers. In this paper, a multi-timescale demand response model that accounts for short-term and long-term demand characteristics is established. The short-term demand response and long-term demand response are combined by the electricity price elasticity matrix. The objective of optimisation is to minimise the peak-valley load difference in the total load，and particle swarm optimisation is adopt as the optimisation algorithm. The simulation results of IEEE-30 node distribution network show the feasibility and effectiveness of the proposed demand response model and pricing strategy. The research of this paper will provide technical support for the application of demand response mechanism after the new electric power reform.
Keywords: demand response; power demand elasticity matrix; multi-timescale model; peak-valley difference optimisation; pricing strategy.
Application of multivariate adaptive regression in soft-sensing and control of UCG
by Jan Kacur, Marek Laciak, Milan Durdan, Patrik Flegner
Abstract: The technology of underground coal gasification (UCG) is still under development and provides an alternative to conventional coal mining. Process monitoring is the necessary part of a complex control system because it provides essential information for control level. Monitoring and control improve the behaviour and effectiveness of the technological process. This paper introduces a novel approach to soft-sensing in UCG based on multivariate adaptive regression splines (MARS). This technique can support monitoring the process variable that is inaccessible for standard measuring hardware. The MARS method was applied for modelling of underground temperature from the syngas composition. The paper also presents advanced approaches to control based on an adaptive regression model. Proposed control can increase or maintain the syngas calorific value during UCG operation. Proposed methods have shown interesting results and can be applied to industrial automation devices or implemented as support algorithms for the monitoring system. Methods were verified in experimental coal gasification on an ex-situ reactor.
Keywords: UCG; monitoring; control; soft-sensing; regression; adaptation; gasification; coal.
A welding manipulator path planning method combining reinforcement learning and intelligent optimisation algorithm
by Junhua Zhang, Lianglun Cheng, Tao Wang, Wenya Xia, Dejun Yan
Abstract: We present DDPG-AACO, a hierarchical method for welding manipulator path planning of complex components welding tasks that combines reinforcement learning (RL) with the intelligent optimisation algorithm. The RL agent, trained with the deep deterministic policy gradient (DDPG), learns local path planning policies that control the welding manipulator to safely move between two welding seam endpoints. Next, on a distance matrix constructed by the lengths of local paths between every two welding seam endpoints,the adaptive ant colony optimisation (AACO) algorithm with artificially changed value of pheromones is adopted to realise the global path planning that the welding manipulator traverses all welding seams under a welding direction constraint and has the shortest path length. The simulation results show the effectiveness of the method. The DDPG is better than the deep Q-learning based methods when performing local path planning. Moreover, the length of the global path with direction constraint can converge to the minimum.
Keywords: complex component; welding manipulator; path planning; direction constraint; DDPG; AACO.
Active disturbance rejection control of three-phase grid-connected photovoltaic systems
by Hezheng Li, Jing Bai, Shunshoku Kanae, Yanxi Li, Lin Yue
Abstract: In this paper, a three-phase grid-connected photovoltaic system based on active disturbance rejection controller (ARDC) is proposed to solve the problem of voltage instability that is caused by many internal uncertainties and external disturbances. A tracking differentiator is used to arrange the transient process to reduce the initial error, which solves the problem of large overshoot. The extended state observer is used to estimate the internal and external total disturbances in real-time, and adopts a linearisation technique via a dynamic compensation process, which can reject internal and external disturbances effectively. The simulation experiments show that ADRC has better performance than PI controller in reducing power loss and rejecting disturbances.
Keywords: three-phase grid-connected photovoltaic system; tracking differentiator; extended state observer; internal and external disturbances.
Incentive Stackelberg game based path planning for payload transportation with two quadrotors
by Yaser Alothman, Wenzhong Zha, Dongbing Gu
Abstract: This paper proposes a path planning approach to the task of transporting a cable-suspended payload with two quadrotors. The transporting system is provided with a desired payload path which is predefined by the user without the consideration of system dynamics. The path planning problem is to find the optimal paths for the quadrotors, which can make the payload to move along the closest path to the desired one under the constraint of system dynamics. This problem is formulated as an incentive dynamic Stackelberg game where one quadrotor plays a leader role while the other plays a follower role, and each of them is defined with an individual cost function. By using an incentive strategy, a game solution can be found based on the linearized dynamic system. The benefit of using two cost functions over one cost function is that the whole system operates more efficiently in terms of individual costs. In the paper, the nonlinear system dynamics is derived first, and then linearized into a linear system. Then an incentive strategy is applied to the linearized system to find the optimal paths for the quadrotors. Some simulation results are provided to show the performance of proposed path planning approach. The planned paths are also used in real experiments where the paths are tracked by two quadrotors using PID controllers.
Keywords: UAV path planning; incentive dynamic Stackelberg game; aerial transportation; multiple quadrotor systems.
Advanced control of three-phase battery electric vehicle charger with V2X technology
by Aziz Rachid, Hassan El Fadil, Abdellah Lassioui, Fouad Giri
Abstract: The bidirectional electric vehicle (EV) charging, so-called vehicle-to-everything (V2X), has a double interest: economic and ecological. It promotes low carbon and cheap electricity because its readily available. To ensure this functionality, electric vehicles use bidirectional chargers with single- or three-phase topologies. One of the main features of three-phase structures is the capability to avoid the problem of oscillating energy between the single-phase power grid and the dc bus. In this paper, the control of bidirectional three-phase battery electric vehicle charger with vehicle-to-grid functionality is addressed. The charger structure is composed of two power converters: a bidirectional three-phase ac-dc power converter and a bidirectional dc-dc power converter associated with an EV battery. The principal control objectives are the following: (i) estimation of the battery state of charge; (ii) unity power factor during the grid-to-vehicle operating mode; (iii) adjusting the reactive power injected into the power grid during vehicle-to-grid operating mode; (iv) dc-bus voltage regulation; and (v) ensuring and safeguarding the battery charging and discharging process. To this end and based on the system modelling into dq coordinates, a nonlinear backstepping controller is designed. The fact is that the battery state of charge is not accessible to measurement. Hence, a partial nonlinear observer is designed to estimate all state variables on the battery-side. The performances of the proposed output feedback controller are highlighted using theoretical analysis and numerical simulations, which clearly illustrate that all control objectives are achieved.
Keywords: bidirectional three-phase ac-dc converter; bidirectional half-bridge dc-dc converter; BEV charger; reactive power compensation; nonlinear output feedback control; nonlinear observer; V2X charger.
Adaptive neuro-fuzzy system based maximum power point tracking for stand-alone photovoltaic system
by Ahmad Azar, Ali Malek, Toufik Bakir, Arezki Fekik, Ahmad Taher Azar, Khaled Mohamad Almustafa, Dallila Hocine, El-Bay Bourennane
Abstract: The Maximum Power Point Tracker (MPPT) plays a very important role to extract the maximum power of the photovoltaic (PV) system by ensuring its optimal production under sunshine and temperature variations. This study presents an algorithm-based MPPT named an Adaptive Neuro Fuzzy Inference System (ANFIS) which is built with the combination of the Artificial Neural Network (ANN) and the Fuzzy Logic Controller (FLC). The efficiency of the ANFIS algorithm is tested under Matlab/Simulink and compared with the fixed step conventional Perturb and Observe (P&O) and the gradient descent techniques under temperature and irradiance change. The obtained results showed a significant improvement in performances of the PV system using the ANFIS-MPPT technique, which provides also faster convergence and stability in the steady state, fewer oscillations around the MPP, and higher efficiency to track the maximum power from the PV system compared with other techniques under different operating conditions.
Keywords: photovoltaic system; perturb and observe; maximum power point tracking; gradient descent; adaptive neuro fuzzy inference system; ANFIS.
A new hyperjerk dynamical system with hyperchaotic attractor and two saddle-focus rest points exhibiting Hopf bifurcations, its hyperchaos synchronisation and circuit implementation
by Sundarapandian Vaidyanathan, Irene M. Moroz, Aceng Sambas
Abstract: A new 4-D dynamical hyperjerk system with hyperchaos is reported in this work. The proposed nonlinear mechanical system with hyperchaos has two saddle-focus rest points exhibiting Hopf bifurcations. A detailed bifurcation analysis of the new hyperjerk plant with theory and simulations is discussed. As a control application, an integral sliding mode controller has been designed for the global hyperchaos synchronisation of the new hyperjerk system with itself. Finally, a circuit model using MultiSim of the new hyperjerk system with hyperchaos is designed for practical implementation.
Keywords: hyperchaos; hyperjerk; sliding mode control; synchronisation; circuit design.
Prediction model based on XGBoost for mechanical properties of steel materials
by Jinxiang Chen, Feng Zhao, Yanguang Sun, Lin Zhang, Yilan Yin
Abstract: At present, the existing existing methods for designing, preparing and testing metal materials are mainly the 'local sample experimental test' method, which has insufficient accuracy, versatility and economy, long development cycle, insufficient knowledge acquisition, and no Lenovo's deduction and self-learning capabilities and other shortcomings. Aiming at these shortcomings, based on the existing steel mechanical performance prediction methods, this paper proposes a prediction model based on XGBoost algorithm based on big data analysis and machine learning methods. The model takes the prediction of mechanical properties of hot-rolled steel as the research background. The field production data of 17710 groups of a steel plant is taken as the sample data set, 90% of which is used as training sample and 10% of the data is used as test sample. Training and evaluation have yielded fairly good prediction accuracy. The results show that the prediction accuracy (R2 score) of the model against tensile strength, yield strength and elongation is 0.99895, 0.99576, 0.96260, respectively, which are superior to the prediction accuracy of BP neural network model. Basically, it can be concluded that this prediction model can predict the mechanical properties of steel more accurately.
Keywords: intelligent prediction; XGBoost; machine learning; metal materials design; mechanical properties.
Quasi-bilinear modelling and control of directional drilling
by Isonguyo Inyang, James Whidborne
Abstract: A Quasi-Bilinear Proportional-plus-Integral (QBPI) controller is proposed for the attitude control of directional drilling tools for the oil and gas industry; and it is designed based on the proposed quasi-bilinear model of the directional drilling tool. The quasi-bilinear model accurately depicts the nonlinear characteristics of the directional drilling tool to a greater extent than the existing linear model, thus extends the scope of appropriate performance. The proposed QBPI control system is an LTI system and it is shown to be exponentially stable. The proposed QBPI controller outstandingly diminishes the deleterious impact of disturbances and measurement delay regarding to performance and stability of the directional drilling tool, and it yields invariant azimuth responses. Drilling cycle scheme which captures the drilling cycle and toolface actuator dynamics of the directional drilling tool, is developed. The servo-velocity and servo-position loops of the toolface servo-control architecture are proven to be robustly stable using Kharitonov's theorem.
Keywords: directional drilling; time delay; disturbances; attitude control; quasi-bilinear; drilling cycle.
A new switching table based neural network for direct power control of three-phase PWM-rectifier
by Arezki Fekik, Hakim Denoun, Mustapha Zaouia, Mohamed Lamine Hamida, Sundarapandian Vaidyanathan
Abstract: Direct power control (DPC) is one of the newest techniques to control the pulse width modulation converter without network voltage sensors. This control technique is built on the idea of direct torque control (DTC) for an induction motor, which is applied to eliminate the harmonic of the line current and to compensate the reactive power. The principle of this control is based on instant active and reactive power loops. This article proposes an intelligent control approach to improve this control technique, such as artificial neural network (ANN), applied to the switching table. The comparison with conventional DPC shows that the use of DPC-ANN ensures smooth control of active and reactive power in all sectors and reduces current ripple. Finally, the developed DPC was tested by simulation. The results proved the excellent performance of the proposed DPC scheme in comparison with the conventional DPC.
Keywords: artificial neural network; direct power control; instantaneous active and reactive power; pulse width modulation; switching table; unity power factor.
Prediction model with optimal matching parameters for a dynamic track stabiliser during railway maintenance
by Bo Yan, Bin Hu, Yayu Huang
Abstract: Nowadays, high-speed and heavy duty trains make ballasted track extremely busy, and thus it is necessary to solve the conflict between the traffic density and the maintenance workload. However, since the mechanical properties of discrete ballast bed are complex, there is a lack of in-depth investigation into the working performance of large-scale railroad maintenance machinery. In this paper, we take the WD-320 dynamic track stabiliser as the research object, to study the effect of operation parameters on the quality state of the ballast bed. Based on the field test data, a prediction model for optimal matching of operation parameters has been constructed, which can be used to estimate, compare and determine the optimal operation parameter combination for the operation process. By operating according to the optimal operation parameter combination, the optimum quality state of the ballast bed can be quickly reached, to solve the conflict between the traffic density and the necessary maintenance window.
Keywords: dynamic track stabiliser; sleeper lateral resistance; operation parameters; optimal matching; prediction model.
Actuator fault diagnosis for interconnected system via invertibility
by Mei Zhang, Ze-tao Li, Qin-mu Wu, Boutaieb Dahhou, Michel Cabassud
Abstract: This paper deals with the problem of actuator fault diagnosis for a class of interconnected invertible nonlinear systems. For that, the actuator is viewed as an independent dynamic subsystem in series with the plant dynamic subsystem, thus forming an interconnected system. The invertibility of the interconnected system in both normal and fault mode is investigated. An interconnected observer is proposed to monitor the performance of the interconnected system and provide fault information of the actuator subsystem. Then a local fault filtering algorithm is triggered to identify the root faulty parameter causing the detected actuator fault. According to the real situation of the industry, it is assumed that the output of the actuator is not available for measurement and should be reconstructed by the global output of the interconnected system. A method capable of supervising the actuator subsystem at both local and global levels is provided.
Keywords: interconnected system; actuator fault diagnosis; fault distinguishability; invertibility; root faulty parameter.
System identification of proportional solenoid valve dynamics
by Bakir Hajdarevic, Jacob Herrmann, Andrea da Cruz, David Kaczka
Abstract: As devices that convert electrical signals into mechanical forces and motions, proportional solenoid (PSOL) valves allow for modulation of flow or pressure in many industrial processes and medical applications. PSOL valves may also be incorporated into pneumatic or hydraulic systems utilizing feedback loops, to allow for precise control of pressure or flow. Accordingly, the design process of any physical system utilizing a PSOL mandates complete and accurate characterization of its linearity and dynamic response. In this paper, we present a system identification technique for the characterization of the linearity and dynamic response of a PSOL valve and its corresponding electronic control unit (ECU in the frequency-domain, using bandlimited white noise as well as pseudo random “non-sum non-difference” (NSND) signals. The NSND waveforms consist of mutually prime frequencies over ranges from 0.195 to 37.4 Hz, to mitigate the effects of nonlinear distortions on the estimated linear system response. The parameters of several transfer function models, with varying numbers of poles and zeros, were simultaneously estimated from the voltage-flow frequency response of the system using a nonlinear gradient descent technique. The resulting candidate transfer function models were then assessed using the mean squared residual criterion (MSR) and the corrected Akaike Information Criterion (AICc). The MSR yielded a “best fit” transfer function consisting of 10 poles and 9 zeros, while the AICc yielded a simpler transfer function consisting of 5 poles and 3 zeros. Uncertainty analysis using a Monte Carlo simulation demonstrated fragile stability for the MSR-selected model with respect to varying parameter values within estimated uncertainties, yet a robust stability for the AICc-selected model. We conclude that our system identification technique for estimating linear transfer functions of ECU-PSOL systems will be useful robust modeling, simulation, and design of pneumatic or hydraulic processes and applications.
Keywords: proportional solenoid valve; mean squared residuals; Akaike information criterion; system identification; transfer functions; model optimization; pneumatic systems
A real-time power-split strategy for a hybrid marine power plant using MPC
by Nikolaos Planakis, George Papalambrou, Nikolaos Kyrtatos
Abstract: In this work, the problem of energy management strategies in hybrid diesel-electric marine propulsion systems is investigated with the implementation of Model Predictive Control. The system behavior is described by models based on system identification from experimental data. These models were used for the design of predictive controllers. The controllers were designed to tackle with physical and operating
constraints of the hybrid system. Different MPC designs were considered, in order to
evaluate the capabilities of the proposed control concept. The controllers were successfully tested at the test bed of the Laboratory evaluating diverse strategies for disturbance rejection, system stability, and operation of the plant within operator’s desirable limits.
Keywords: predictive control, hybrid marine propulsion, diesel-electric, data-based modeling,real-time control, power-split control
Oxygen Therapy in Chronic Obstructive Pulmonary Disease: Insight from Convex Optimization
by Tanmay Pal, Pranab Kumar Dutta, Srinivasu Maka
Abstract: Application of additional oxygen for managing acute exacerbation of Chronic Obstructive Pulmonary Disease (COPD) have an associated risk of oxygen toxicity. The aim of this work is to determine the appropriate level of oxygen for managing such condition using mathematical model. In this approach, a noted respiratory regulation model is modified by impaired diffusion, dead space and variable blood flow to simulate COPD condition. This condition is manifested as variation of the equilibrium point of the model. As variation of these quantities happen over a long time, the steady-state model obtained from the dynamic model is used for further analysis. Simulation of the model shows, alveolar oxygen partial pressure reduces to 67.37 mmHg in COPD condition from 96.6 mmHg in normal condition. Similarly, alveolar carbon dioxide partial pressure increases to 54.73 mmHg in COPD condition from 40.03 mmHg in normal condition. Consequently, minute ventilation become 10.85 L/min in COPD condition, which is 97.63% higher than normal condition. Using the model, it is established that higher inspired oxygen increases alveolar oxygen, as well as alveolar carbon dioxide. It is also shown that higher inspired oxygen increases oxygen saturation and lowers the mismatch of ventilation-perfusion ratio. Using quadratic combination of alveolar oxygen and carbon dioxide pressure, an objective function is proposed to calculate the optimal level of inspired oxygen, which is 186.84 mmHg. Results obtained from this analysis methodology are in agreement with clinical data.
Keywords: Mathematical Model ; Respiratory Regulation ; COPD ; Oxygen Saturation ; Ventilation-Perfusion ratio; Convex Optimization ; Oxygen Therapy
Special Issue on: ICMIC 2019 Mathematical Modelling and Advanced Control Approaches of Nonlinear Complex Dynamics
Estimation of inhalation exposure to metals among welders of a Steel Company using MEASE model: As a screening tool for estimates of occupational exposure
by sara Karimi Zeverdegani, younes mehrifar, Masoud Rismanchian
Abstract: Estimation and assessment of substance exposure for metals is a tool to estimate inhalation exposures for metals substances.The aim of present study was estimation of exposure to welding fumes using MEASE model and also comparison of the estimated data with exposure levels by NIOSH 7300 in different welding processes.The metal fumes including Cu, Fe,Ca,Al,Mg,Mn,K and Na in the MIG,MAG and SMAW processes were measured according to NIOSH 7300.The occupational exposure estimation to the metal fumes was performed by MEASE model.There was fair agreement between measured and estimated values in terms of different metal fumes and welding processes(r=0.131 to 0.933).A significant strong correlation was found between measured and estimated levels for Mg(r=0.933;P=0.001),Ca(r= 0.896;P=0.001),K(r=0.805;P=0.001),Na(r=0.716;P=0.001),and Al(r= 0.756;P=0.032).There was a significant strong correlation between measured and estimated values for SMAW(r=0.708;P= 0.003) and MIG(r=0.635;P=0.036).The authors expect that the MEASE model can not be wholly successfully applied for semiquantitative inhalation exposure assessment in occupational health surveys.
Keywords: Estimation, inhalation exposure, MEASE model , metal fumes, welding processes