Actor-Critic Reinforcement Learning Control of Non-Strict Feedback Nonaffine Dynamic Systems

• Published in: IEEE access Vol. 7; pp. 65569 - 65578
• Main Author: Bu, Xiangwei
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: actor network; Adaptive systems; Aerodynamics; Artificial neural networks; continuous nonaffine systems; Control stability; Control systems; Dynamical systems; Feedback; fuzzy critic network; Fuzzy systems; Learning; Reinforcement learning; Reinforcement learning control; Simulation
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2917141

The most focuses of the existing actor-critic reinforcement learning control (ARLC) are on dealing with continuous affine systems or discrete nonaffine systems. In this paper, I propose a new ARLC method for continuous nonaffine dynamic systems subject to unknown dynamics and external disturbances. A new input-to-state stable system is developed to establish an augmented dynamic system, from which I further get a strict-feedback affine model that is convenient for control designing based on a model transformation approach. The Nussbaum function is connected with a fuzzy approximation to devise an actor network whose tracking performance is further enhanced via strengthening signals generated by a fuzzy critic network. The stability of the closed-loop control system is guaranteed by the Lyapunov synthesis. Finally, the comparison simulation results are presented to verify the design.