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A Hybrid PSO–GWO Fuzzy Logic Controller with a New Fuzzy Tuner

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Abstract

A hybrid PSO–GWO PID-type fuzzy logic controller (FLC) with a new online fuzzy tuner is proposed in this study to control a second-order system with varying parameters. Initially, the output scaling factors and the universe of discourse of membership functions for the PID-type FLC are optimized employing a hybrid particle swarm optimization (PSO) and grey wolf optimization (GWO) technique. The superiority of the hybrid PSO–GWO has been demonstrated by comparing with other methods, namely PSO, GWO, and hybrid CS (cuckoo search)–GWO. Additionally, a new online fuzzy tuner structure is proposed by tuning a single output scaling factor to overcome the major disadvantages of the previous approach using the relative rate observer and fuzzy parameter regulator (RRO–FPR). Simulation results show that all the given optimal PID-type FLCs with the proposed new fuzzy tuner produce a better system performance and exhibit a shorter settling time/rise time than the RRO–FPR approach. The proposed optimal PID-type FLC with a fuzzy tuner, which is optimized by the hybrid PSO–GWO method, proves to be superior to others given in this study by exhibiting the shortest settling time/rise time and the lowest overshoot in a practical application to the speed control of a nonlinear DC motor system. Therefore, utilization of the proposed, improved controller to nonlinear systems in dealing with dead-zone and unexpected disturbance is highly feasible.

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Acknowledgements

This work was supported by the Ministry of Science and Technology (Taiwan) under Contract Nos. MOST 109-2218-E-008-003 and MOST 108-2218-E-008-019.

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Authors and Affiliations

  1. Department of Mechanical Engineering, National Central University, Jhong-Li District, Tao-Yuan City, 32001, Taiwan

    Dinh-Tu Nguyen, Jeng-Rong Ho, Pi-Cheng Tung & Chih-Kuang Lin

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Correspondence to Chih-Kuang Lin.

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Nguyen, DT., Ho, JR., Tung, PC. et al. A Hybrid PSO–GWO Fuzzy Logic Controller with a New Fuzzy Tuner. Int. J. Fuzzy Syst. 24, 1586–1604 (2022). https://doi.org/10.1007/s40815-021-01215-6

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