Optimal PID Controller Autotuning Design for MIMO Nonlinear Systems Based on the Adaptive SLP Algorithm

Journal article

Authors/Editors

WUDHICHAI ASSAWINCHAICHOTE

Strategic Research Themes

Other (Strategic Research Themes)

Publication Details

Author list: Pongfai J., Angeli C., Shi P., Su X., Assawinchaichote W.

Publisher: Springer

Publication year: 2021

Journal acronym: IJCAS

Volume number: 19

Issue number: 1

Start page: 392

End page: 403

Number of pages: 12

ISSN: 1598-6446

eISSN: 2005-4092

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091088938&doi=10.1007%2fs12555-019-0680-6&partnerID=40&md5=dbcd05ab74341bebcfae580ac135fe56

Languages: English-Great Britain (EN-GB)

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Abstract

In this paper, an adaptive swarm learning process (SLP) algorithm for designing the optimal proportional integral and derivative (PID) parameter for a multiple-input multiple-output (MIMO) control system is proposed. The SLP algorithm is proposed to improve the performance and convergence of PID parameter autotuning by applying the swarm algorithm and the learning process. The adaptive SLP algorithm improves the stability, performance and robustness of the traditional SLP algorithm to apply it to a MIMO control system. It can update the online weights of the SLP algorithm caused by the errors in the settling time, rise time and overshoot of the system based on a stable learning rate. The gradient descent is applied to update the weights. The stable learning rate is verified based on the Lyapunov stability theorem. Additionally, simulations are performed to verify the superiority of the algorithm in terms of performance and robustness. Results that compare the adaptive SLP algorithm with the traditional SLP, a neural network (NN), the genetic algorithm (GA), the particle swarm and optimization (PSO) algorithm and the kidney-inspired algorithm (KIA) based on a two-wheel inverted pendulum system are presented. With respect to performance and robustness, the adaptive SLP algorithm provides a better response than the traditional SLP, NN, GA, PSO and KIA. © 2021, ICROS, KIEE and Springer.

Keywords

Autotuning, inverted pendulum, multiple-input/multiple-output (MIMO), swarm algorithm