Adaptive neuro-fuzzy PID controller based on twin delayed deep deterministic policy gradient algorithm

Qian Shi, Hak Keung Lam*, Chengbin Xuan, Ming Chen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

50 Citations (Scopus)

Abstract

This paper presents an adaptive neuro-fuzzy PID controller based on twin delayed deep deterministic policy gradient (TD3) algorithm for nonlinear systems. In this approach, the observation of the environment is embedded with information of a multiple input single output (MISO) fuzzy inference system (FIS) and have a specially defined fuzzy PID controller in neural network (NN) formation acting as the actor in the TD3 algorithm, which achieves automatic tuning of gains of fuzzy PID controller. From the control perspective, the controller combines the merits of both FIS and PID controller and utilizes reinforcement learning algorithm for optimizing parameters. From the reinforcement learning point of view, embedding the prior knowledge into the fuzzy PID controller incorporated in the actor network helps reduce the learning difficulty in the training process. The proposed method was tested on the cart-pole system in simulation environment with comparison of a linear PID controller, which demonstrates the robustness and generalization of the proposed approach.

Original languageEnglish
Pages (from-to)183-194
Number of pages12
JournalNEUROCOMPUTING
Volume402
DOIs
Publication statusPublished - 18 Aug 2020

Keywords

  • Cart-pole system
  • Fuzzy PID controller
  • Reinforcement learning
  • Twin delayed deep deterministic policy gradient algorithm

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