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A robust self-learning PID control system design for nonlinear systems using a particle swarm optimization algorithm

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Abstract

This study presents a robust self-learning proportional-integral-derivative (RSPID) control system design for nonlinear systems. This RSPID control system comprises a self-learning PID (SPID) controller and a robust controller. The gradient descent method is utilized to derive the on-line tuning laws of SPID controller; and the \( \, H_{\infty } \, \) control technique is applied for the robust controller design so as to achieve robust tracking performance. Moreover, in order to achieve fast learning of PID controller, a particle swarm optimization (PSO) algorithm is adopted to search the optimal learning-rates of PID adaptive gains. Finally, two nonlinear systems, a two-link manipulator and a chaotic system are examined to illustrate the effectiveness of the proposed control algorithm. Simulation results show that the proposed control system can achieve favorable control performance for these nonlinear systems.

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Authors and Affiliations

  1. Department of Electrical Engineering, Yuan Ze University, No. 135, Yuan-Tung Road, Chung-Li, Tao-Yuan, 320, Taiwan, ROC

    Chih-Min Lin, Ming-Chia Li, Ang-Bung Ting & Ming-Hung Lin

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  1. Chih-Min Lin
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  2. Ming-Chia Li
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  3. Ang-Bung Ting
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  4. Ming-Hung Lin
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Correspondence to Chih-Min Lin.

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Lin, CM., Li, MC., Ting, AB. et al. A robust self-learning PID control system design for nonlinear systems using a particle swarm optimization algorithm. Int. J. Mach. Learn. & Cyber. 2, 225–234 (2011). https://doi.org/10.1007/s13042-011-0021-4

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  • DOI: https://doi.org/10.1007/s13042-011-0021-4

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