Sliding Mode Controller Design with Optimized PID Sliding Surface Using Particle Swarm Algorithm

Soon, Chong Chee; Ghazali, Rozaimi; Jaafar, Hazriq Izzuan; Hussien, Sharifah Yuslinda Syed

doi:10.1016/j.procs.2017.01.216

Cited by 47 publications

(29 citation statements)

References 15 publications

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“…In this paper, the particle swarm optimization (PSO) method is used to calculate these parameters. In this way, PSO allows one to obtain the optimal parameters of the controller, improving the transient response and the error in the steady state [39]. Simulation results of the SMPI controller used to regulate the voltage and the input current of a two-level interleaved boost converter were presented in [40].…”

Section: Design Of the Control Lawmentioning

confidence: 99%

Weighted-PSO Applied to Tune Sliding Mode Plus PI Controller Applied to a Boost Converter in a PV System

et al. 2019

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In this paper, a sliding mode plus proportional-integral (PI) controller for a boost converter in a photovoltaic system is proposed. The proposed controller is characterized by being easy to implement and by operating with constant switching frequency. The parameters of the proposed controller are calculated using the weighted particle swarm optimization technique, ensuring low percentage of overshoot and short setting time. The use of this optimization technique allows one to ensure the stability of the controller. A linear lead-leg controller is considered in order to compare the performance of the proposed controller. Finally, experimental results using a solar kit are presented to verify the performance of the proposed controller.

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Section: Design Of the Control Lawmentioning

confidence: 99%

Weighted-PSO Applied to Tune Sliding Mode Plus PI Controller Applied to a Boost Converter in a PV System

et al. 2019

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“…Then the SMC will be applied to the system where the PID will be integrated as the sliding surface of the SMC controller and the system performance monitoring will be performed. SMC with PID sliding surface will be applied to BLDC 2nd order system, thus yielding the following equation [10].…”

Section: B Sliding Mode Controllermentioning

confidence: 99%

Robust Adaptive Sliding Mode Control Design with Genetic Algorithm for Brushless DC Motor

Putra¹,

Has²,

Effendy³

2018

EECSI

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This study aims to design a control scheme that is capable to improve performance and efficiency of brushless DC motor (BLDC) in operating condition. The control scheme is composed of sliding mode controller (SMC) with proportionalintegral-derivative (PID) sliding surface. The PID sliding surface is used to improve the system transient response. Then, the SMC-PID is optimized by genetic algorithm optimization for further improvement on the stability and robustness against nonlinearities and disturbances. Chattering problem that appear in the SMC is minimized by employing an adaptive switching gain for the SMC that is integrated with Luenberger Observer. Lyapunov function candidate is applied to guarantee the stability of the system. Simulation on the proposed work is done in Matlab Simulink. Results of the simulation works indicate that the proposed control scheme can improve the transient response, the stability and robustness of the BLDC motor compared to the conventional SMC in the existence of nonlinearities and disturbances.

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“…PSO is propelled by swarming conduct of winged animal rushing and fish tutoring. This method provides outstanding performance in solving optimization problems . Each particle tries to update its current position and velocity according to its own experience.…”

Section: Introduction About Pso Algorithmmentioning

confidence: 99%

“…This method provides outstanding performance in solving optimization problems. 27,28,30 Each particle tries to update its current position and velocity according to its own experience. In every iteration, best quantity attained by each particle will be spared and later compared to get individual best quantities.…”

Section: Introduction About Pso Algorithmmentioning

confidence: 99%

Development and performance analysis of PSO‐optimized sliding mode controller–based dynamic voltage restorer for power quality enhancement

Jeyaraj

Devaraj

Velusamy

2019

Int Trans Electr Energ Syst

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Summary In the distribution power system, voltage sag and swell are the critical issues. The impact of this issue creates major economic loss and power loss in the distribution system. There are several detection methods available, mainly, connecting the FACTS devices such as Static Compensator (STATCOM), Unified Power Quality Conditioner (UPQC), and dynamic voltage restorer (DVR) at the point of sag or swell. DVR is one of the custom power devices to mitigate the voltage sag and swell. Sliding mode controller (SMC) is proposed in the paper, which is used to regulate the DVR to mitigate the voltage sag issue. Particle swarm optimization (PSO) algorithm is utilized in this work to find the optimum value of SMC parameters, namely, Kp and Ki, with the objective of minimizing the integral square error (ISE). A simple two feeder distribution network with DVR is developed in MATLAB‐SIMULINK to assess the performance of DVR under various faults and disturbed situations. The mitigation level and THD values of PSO‐optimized SMC‐based DVR results are compared with the PI controller–based DVR, PSO‐optimized PI controller DVR, and SMC‐based DVR. At last, in the proposed method, the trial is taken by adding a renewable energy resource, that is, PV farm at 400‐kW power rating. The solar energy is integrated with the existing distribution system, and the results are incorporated. Simulation results show that the PSO‐optimized SMC‐based DVR can compensate the voltage sag and swell and also reduce the load voltages efficiently.

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Sliding Mode Controller Design with Optimized PID Sliding Surface Using Particle Swarm Algorithm

Cited by 47 publications

References 15 publications

Weighted-PSO Applied to Tune Sliding Mode Plus PI Controller Applied to a Boost Converter in a PV System

Weighted-PSO Applied to Tune Sliding Mode Plus PI Controller Applied to a Boost Converter in a PV System

Robust Adaptive Sliding Mode Control Design with Genetic Algorithm for Brushless DC Motor

Development and performance analysis of PSO‐optimized sliding mode controller–based dynamic voltage restorer for power quality enhancement

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