Output Feedback Controller for a Class of Unknown Nonlinear Discrete Time Systems Using Fuzzy Rules Emulated Networks and Reinforcement Learning

Abstract

A model-free adaptive control for non-affine discrete time systems is developed by utilising the output feedback and action-critic networks. Fuzzy rules emulated network (FREN) is employed as the action network and multi-input version (MiFREN) is implemented as the critic network. Both networks are constructed using human knowledge based on IF–THEN rules according to the controlled plant and the learning laws are established by reinforcement learning without any off-line learning phase. The theoretical derivation of the convergence of the tracking error and internal signal is demonstrated. The numerical simulation and the experimental system are given to validate the proposed scheme.

Keywords:

• Model-free adaptive control
• reinforcement learning
• nonlinear discrete time systems
• fuzzy neural network
• DC motor current control

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work has been supported by Fundamental Research Funds for CINVESTAV-IPN and Mexican Research Organization CONACyT [grant number 257253].

Notes on contributors

C. Treesatayapun

C. Treesatayapun received the Ph.D. in elec- trical engineering from Chiang-Mai University, Thailand, in 2004. He was a production engineer at SAGA Elec- tronics (JRC-NJR) from 1998-2000 and was a head of electrical engineering program at North Chiang-Mai University, Thailand from 2001- 2007. He is currently a senior researcher at Department of robotic and advanced manufac- turing, Mexican Research Center and Advanced Technology, CINVESTAV-IPN, Saltillo campus, Mexico. His current research interests include automation and robotic system control and optimization, adaptive and learning algorithms and electric machine drives.