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Evolutionary optimization of sliding mode controller for level control system.
ISA Trans 2018; 83:199-213IT

Abstract

This paper accords the level control of single-input-single-output (SISO) level control system based on the fusion of sliding mode control (SMC) and evolutionary techniques or bio-inspired techniques. The non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective particle swarm optimization (MOPSO) are considered as two evolutionary techniques. Here, a comparative analysis of performances of an optimal proportional-integral (PI) controller, proportional-integral-derivative (PID) controller, conventional SMC, NSGA-II based tuned SMC and SMC parameter tuning using MOPSO algorithm has been carried out through MATLAB/SIMULINK. The objective functions, integral absolute error (IAE), integral squared error (ISE) and an integration of weighted objective function aggregated approach of the error performance indices, IAE and ISE are considered. Realistic conditions are used in a plant for testing the robustness of controller. The stability of the controller is successfully obtained which satisfies the Lyapunov stability criteria. Reduction in long settling time with tiny magnitude variations about an equilibrium point is achieved using bio-inspired techniques. The simulation as well as experimental results reveal that SMC parameter tuning based on NSGA-II algorithm gives a better performance as compared to the other design strategies.

Authors+Show Affiliations

Dr. Vithalrao Vikhe Patil College of Engineering, Ahmednagar, Savitribai Phule Pune University, Pune, India. Electronic address: ajitlaware2003@gmail.com.College of Engineering, An Autonomous Institute of the Government of Maharashtra, Pune, India. Electronic address: talanged@gmail.com.College of Engineering, An Autonomous Institute of the Government of Maharashtra, Pune, India. Electronic address: v_bandal@rediffmail.com.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

30144980

Citation

Laware, A R., et al. "Evolutionary Optimization of Sliding Mode Controller for Level Control System." ISA Transactions, vol. 83, 2018, pp. 199-213.
Laware AR, Talange DB, Bandal VS. Evolutionary optimization of sliding mode controller for level control system. ISA Trans. 2018;83:199-213.
Laware, A. R., Talange, D. B., & Bandal, V. S. (2018). Evolutionary optimization of sliding mode controller for level control system. ISA Transactions, 83, pp. 199-213. doi:10.1016/j.isatra.2018.08.011.
Laware AR, Talange DB, Bandal VS. Evolutionary Optimization of Sliding Mode Controller for Level Control System. ISA Trans. 2018;83:199-213. PubMed PMID: 30144980.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Evolutionary optimization of sliding mode controller for level control system. AU - Laware,A R, AU - Talange,D B, AU - Bandal,V S, Y1 - 2018/08/17/ PY - 2017/11/24/received PY - 2018/07/02/revised PY - 2018/08/10/accepted PY - 2018/8/27/pubmed PY - 2018/8/27/medline PY - 2018/8/27/entrez KW - An optimal proportional–integral controller KW - Integral absolute error KW - Integral square error KW - Multi-objective particle swarm optimization KW - Non-dominated sorting genetic algorithm-II KW - Proportional–integral–derivative controller KW - Sliding mode controller SP - 199 EP - 213 JF - ISA transactions JO - ISA Trans VL - 83 N2 - This paper accords the level control of single-input-single-output (SISO) level control system based on the fusion of sliding mode control (SMC) and evolutionary techniques or bio-inspired techniques. The non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective particle swarm optimization (MOPSO) are considered as two evolutionary techniques. Here, a comparative analysis of performances of an optimal proportional-integral (PI) controller, proportional-integral-derivative (PID) controller, conventional SMC, NSGA-II based tuned SMC and SMC parameter tuning using MOPSO algorithm has been carried out through MATLAB/SIMULINK. The objective functions, integral absolute error (IAE), integral squared error (ISE) and an integration of weighted objective function aggregated approach of the error performance indices, IAE and ISE are considered. Realistic conditions are used in a plant for testing the robustness of controller. The stability of the controller is successfully obtained which satisfies the Lyapunov stability criteria. Reduction in long settling time with tiny magnitude variations about an equilibrium point is achieved using bio-inspired techniques. The simulation as well as experimental results reveal that SMC parameter tuning based on NSGA-II algorithm gives a better performance as compared to the other design strategies. SN - 1879-2022 UR - https://www.unboundmedicine.com/medline/citation/30144980/Evolutionary_optimization_of_sliding_mode_controller_for_level_control_system_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S0019-0578(18)30311-2 DB - PRIME DP - Unbound Medicine ER -