New PID Parameter Autotuning for Nonlinear Systems Based on a Modified Monkey-Multiagent DRL Algorithm

Journal article

Authors/Editors

WUDHICHAI ASSAWINCHAICHOTE

Strategic Research Themes

Other (Strategic Research Themes)

Publication Details

Author list: Zhang H., Assawinchaichote W., Shi Y.

Publisher: Institute of Electrical and Electronics Engineers

Publication year: 2021

Journal: IEEE Access (2169-3536)

Volume number: 9

Start page: 78799

End page: 78811

Number of pages: 13

ISSN: 2169-3536

eISSN: 2169-3536

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107381321&doi=10.1109%2fACCESS.2021.3083705&partnerID=40&md5=ae34662ab160b0fba5c97eac0fd6bdf1

Languages: English-Great Britain (EN-GB)

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Abstract

Proportional-integral-derivative (PID) control is the most widely used control law in industrial processes. Although various new controllers continue to emerge, PID controllers are still in a dominant position due to their simple structure, easy implementation, and good robustness. In the design and application of PID controllers, one of the core issues is parameter tuning. Accurately and effectively selecting the best tuning parameters of the PID is the key to achieving an effective PID controller. Therefore, this paper proposes a novel modified monkey-multiagent DRL (MM-MADRL) algorithm and uses it to tune PID parameters to improve the stability and performance of automatic parameter optimization. The MM-MADRL algorithm is a new version of the basic monkey group algorithm (MA) and the multiagent reinforcement learning algorithm known as the multiagent deep deterministic policy gradient (MADDPG). This paper selects a typical nonlinear quadcopter system for simulation; its principle and data are given below. MM-MADRL, the genetic algorithm (GA), particle swarm optimization (PSO), the sparse search algorithm (SSA) and differential evolution (DE) are used to adjust the parameters. The simulation results show that the overall performance of the MM-MADRL algorithm is better than that of the other algorithms. © 2013 IEEE.

Keywords

modified monkey-multiagent DRL (MM-MADRL) algorithm, monkey algorithm (MA), multiagent deep deterministic policy gradient (MADDPG)