Reinforcement Learning-Based Control of Nonlinear Systems Using Lyapunov Stability Concept and Fuzzy Reward Scheme

Ming Chen, Hak Keung Lam*, Qian Shi, Bo Xiao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)
171 Downloads (Pure)

Abstract

In this brief, a reinforcement learning-based control approach for nonlinear systems is presented. The proposed control approach offers a design scheme of the adjustable policy learning rate (APLR) to reduce the influence imposed by negative or large advantages, which improves the learning stability of the proximal policy optimization (PPO) algorithm. Besides, this brief puts forward a Lyapunov-fuzzy reward system to further promote the learning efficiency. In addition, the proposed control approach absorbs the Lyapunov stability concept into the design of the Lyapunov reward system and a particular fuzzy reward system is set up using the knowledge of the cart-pole inverted pendulum and fuzzy inference system (FIS). The merits of the proposed approach are validated by simulation examples.

Original languageEnglish
Article number8871158
Pages (from-to)2059-2063
Number of pages5
JournalIEEE Transactions on Circuits and Systems II: Express Briefs
Volume67
Issue number10
DOIs
Publication statusPublished - Oct 2020

Keywords

  • adjustable policy learning rate (APLR)
  • cart-pole inverted pendulum
  • fuzzy reward system
  • Lyapunov reward system
  • Proximal policy optimization (PPO)

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