Showing papers in "International Journal of Automation and Control in 2022"

Design and optimisation of a fuzzy-PI controlled modified inverter-based PMSM drive employed in a light weight electric vehicle

Abstract: In this paper, a pulse width modulation (PWM) controlled permanent magnet synchronous motor (PMSM) is designed and illustrated using fuzzy logic control. The traditional PI controller is modified as a fuzzy-PI controller for faster dynamic response. The fuzzy logic operated speed controller ensures an almost zero steady-state speed error in this proposed approach. In addition, a stochastic optimisation tool such as the updated particle swarm optimisation (PSO) technique is used to optimise the performance of the proposed fuzzy logic controller. The economical as well as robust design procedure for the buck converter fed VSI is also proposed. With the configuration of the inverter, an improved over-current protection method is developed for the permanent magnet motor in a real-time light electric vehicle. Later, the control strategy of the developed prototype is tested and verified using a low-cost microcontroller. Furthermore, to improve the robustness of the drive, sensorless speed estimation procedure is also adopted in the real-time setup. Finally, simulations and test results reveal the efficacy of this proposed approach over the existing control topologies.

Control of the rated production power of DFIG-wind turbine using adaptive PSO and PI conventional controllers

Abstract: Production of rated power and protection of generator and power converter from an overload are necessary. Therefore, measuring the accurate value of the pitch angle of the doubly fed induction generator (DFIG) wind turbine is essential. An assessment study between the adaptive particle swarm optimisation (PSO) technique and conventional proportional integral (PI) controller of the pitch control system in limiting the electrical output power at the rated value of DFIG is introduced in this study. Pitch control with PSO is designed by solving the nonlinear equation of pitch angle at each wind speed higher than rated wind speed. The PI controller gains of the pitch system are evaluated to keep the power limited at rated value. Accuracy in measuring pitch angle is essential because a small difference in pitch angle value results in an overload on the generator. The performance of each parameter of DFIG with a detailed analysis is studied. The simulation shows that the pitch control system with PSO technique gives better results in regulating the DFIG output power compared to PI controller method under wind speed variation.

Fuzzy adaptive finite-time sliding mode controller for trajectory tracking of ship course systems with mismatched uncertainties

Abstract: In this paper, a new finite-time robust controller is designed for the ship heading (course) control systems with unknown mismatched external disturbances and uncertainties to fulfil trajectory tracking task. The adaptive method is utilised to estimate the upper bound of these mismatched disturbances and uncertainties. Meanwhile, the fuzzy logic is employed to enhance the tracking performance. In short, the fuzzy controller, adaptive, finite-time stability concepts and sliding mode control (SMC) scheme have been incorporated to propose fuzzy adaptive finite-time SMC (FAFSMC) scheme to utilise their benefits and to compensate the shortages of applying them individually. The finite-time stability analysis is investigated by utilising the Lyapunov stability theory. The simulation results are carried out for the proposed fuzzy scheme and five other existing non-fuzzy schemes including simple, classical, cubic, hexagonal, and switching to reveal the effectiveness of the proposed scheme compared to the other five schemes.

Parameter settings in particle swarm optimisation algorithms: a survey

Abstract: In swarm intelligence, 'fair comparison' is critical for the performance evaluation of algorithms. In this paper, the setting of parameters in particle swarm optimisation (PSO) algorithms, which include the population size S, topology structure (number of neighbours k), inertia weight w, acceleration coefficient c1, c2, velocity constraint Vmax, and the boundary constraint strategy, are reviewed and analysed. Based on the analysis and discussion of parameters and the variants of PSO algorithms, a list of parameter settings of PSO algorithms and a recommendation of PSO comparison are given. To compare variants of PSO algorithms, a recommended solution maybe that all compared algorithms have the same number of population size and the maximum number of fitness evaluations, and the inertia weight w, acceleration coefficient c1, c2 are the same settings as its original version.

Power control of a stand-alone electric generation hybrid system using integral sliding mode controller

Abstract: This paper proposes a novel strategy for output power control of an electric generation hybrid system (EGHS), composed of a photovoltaic generation system, a storage battery bank and a variable load. According to different atmospheric conditions and load changes, a robust control based on sliding mode control (SMC) is designed to satisfy the total power demand in different power system operation modes. Thus, the proposed controller is modified by introducing the integral action in the switching surface, in order to improve transient response with minimum steady state error. Numerical simulations are presented and discussed to demonstrate the performance of the proposed method, using a nonlinear model of the plant. Finally, the simulation results show that the proposed integral sliding mode control (ISMC) strategy ensures better response speed and smaller steady-state error compared to standard SMC.

Modified sliding mode control for a universal active filter-based solar microgrid system

Abstract: This manuscript presents a larger signal model by combining the universal active filter (UAF) and the solar system with a vector switching operation (VSO) in a novel γθ frame. The detailed modelling of the S-UAF using VSO and modified sliding mode control (MSMC) is proposed for achieving better power quality (PQ) operation. MSMC-based novel γθ frame is used to estimate the accurate reference signals for SHAF during dynamic state conditions such as sag/swell, change in irradiance, fault and sensitive load condition. SEAF control is based upon the conventional dq control strategies and it adjusts the load voltage during dynamic conditions. SHAF is used to balance the grid side current and reduces the harmonic distortion by injecting the appropriate current in quadrature with the load current and facilitates fast transient response during sudden load change by providing better tracking capability and reduction in the switching losses. To validate the proposed controller and S-UAF approach with different test conditions, it is tested in MATLAB/Simulink environment and the related results are discussed.

Power control of a stand-alone electric generation hybrid system using integral sliding mode controller

Abstract: This paper proposes a novel strategy for output power control of an electric generation hybrid system (EGHS), composed of a photovoltaic generation system, a storage battery bank and a variable load. According to different atmospheric conditions and load changes, a robust control based on sliding mode control (SMC) is designed to satisfy the total power demand in different power system operation modes. Thus, the proposed controller is modified by introducing the integral action in the switching surface, in order to improve transient response with minimum steady state error. Numerical simulations are presented and discussed to demonstrate the performance of the proposed method, using a nonlinear model of the plant. Finally, the simulation results show that the proposed integral sliding mode control (ISMC) strategy ensures better response speed and smaller steady-state error compared to standard SMC.

Adaptive sliding mode controller design for the bipartite consensus tracking of multi-agent systems with actuator faults and disturbances

Abstract: This paper studies the bipartite consensus problem for second-order nonlinear multi-agent systems in the presence of actuator faults, unknown control gains and unknown external disturbances. The actuator faults are considered as partial loss of effectiveness fault and bias fault. For design controller, the control gains and disturbances only need to have unknown upper bounds. Also a signed bipartite directed graph is used for describing the communication topology of the multi-agent system. An adaptive sliding mode controller is developed for bipartite consensus tracking of the multi-agent system. The proposed adaptive sliding mode controller ensures the uniformly ultimately bounded cooperative tracking of the multi-agent system. Finally, the correctness and effectiveness of the proposed control method is verified via simulation results.

Closed-loop thickness control and sensor placement in extrusion blow moulding

Abstract: Extrusion blow moulding (EBM) is a polymer forming technique used to manufacture hollow plastic parts, such as fuel tanks. In this work, the feasibility of using closed-loop control in EBM is explored to compensate for machine drift and disturbances. A control system is proposed to regulate the extrusion process in EBM. The extrusion controller aims to increase process consistency by minimizing part thickness deviations. The thickness of the extrudate is measured during the extrusion cycle and any deviation from the desired thickness profile is compensated by changing the die gap in real time. The controller, which offers flexibility in thickness sensor placement, features a Smith predictor configuration embedding an H∞ controller. It compensates for the input-dependent polymer transport delay, the nonlinear steady-state swell, and nonminimum phase necking effects affecting the extrudate. This extrusion control technique may help reduce the production rate of off-specification parts and improve product quality.

Stable optimal self-tuning interval type-2 fuzzy controller for servo position control system

Abstract: An optimal self-tuning interval type-2 fuzzy controller for servo position control systems is reported here. To achieve precise positioning of the actuator, input scaling factors of an interval type-2 fuzzy proportional-integral controller are updated online depending on the latest operating conditions in terms of closed loop tracking error and change of error. To ensure desired performance, input scaling factors are obtained by adaptive cuckoo search-based optimisation algorithm. Efficacy of the proposed scheme is substantiated through performance comparison with recently reported peak observer based and online self-tuning based interval type-2 fuzzy PID, interval type-2 fuzzy PI, and also type-1 fuzzy PI controllers through simulation study along with real-time validation on a DC servo position control system. Lyapunov function-based stability analysis for the proposed controller is also provided.

Residual-based fault detection isolation and recovery of a greenhouse

Abstract: This paper is concerned with fault detection, isolation and recovery (FDIR) of the greenhouse whose temperature is regulated by the model predictive controller (MPC). The residual generation approach is adopted for fault detection and isolation. The new considerations in the proposed FDIR approach are the residual generation for actuator faults, regulation failure detection as the indication of inappropriate regulation by the controller, below threshold actuator fault detection strategy, and recovery operation updating model used by MPC once the FDIR isolates the actuator fault. The proposed control strategy FDIR with MPC was compared with fixed model information MPC for simulated scenarios of the actuator fault. It has been shown that FDIR successfully detects, isolates and updates model information with low computation burden for non-delayed control evaluation.

A Multi-Objective Criterion and Stability Analysis for Neural Adaptive Control of Nonlinear MIMO Systems: An experimental validation

Abstract: This paper presents a multi-objective indirect neural adaptive control design for nonlinear square multi-variable systems with unknown dynamics. The control scheme is made of an adaptive instantaneous neural emulator, a neural controller based on fully connected real-time recurrent learning (RTRL) networks and an online parameter updating law. A multi-objective criterion that takes into account the minimisation of the control energy is considered. The contribution of this paper is to develop a new controller parameter optimisation based on the Lyapunov stability analysis while ensuring control issues with environmental and economical objectives. Performance of the proposed approach in terms of regulation, tracking and minimisation of the control energy is evaluated by numerical simulations of a disturbed nonlinear multi-variable system. The obtained control scheme is then applied in real time to a disturbed MIMO thermal process.