Output-Feedback Based Simplified Optimized Backstepping Control for Strict-Feedback Systems with Input and State Constraints

• Published in: IEEE/CAA journal of automatica sinica Vol. 8; no. 6; pp. 1119 - 1132
• Main Authors: Zhang, Jiaxin, Li, Kewen, Li, Yongming
• Format: Journal Article
• Language: English
• Published: Piscataway The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 01.06.2021; College of Science, Liaoning University of Technology, Jinzhou 121001, China%Institute of Automation, Qufu Normal University, Qufu 273165, China
• Subjects: Adaptive control; Adaptive systems; Algorithms; Artificial neural networks; Backstepping; Backstepping design; Closed loop systems; Control methods; Dynamical systems; Feedback control; immeasurable states; Liapunov functions; Machine learning; Neural networks; neural-networks (NNs); Nonlinear systems; Observers; Optimal control; Output feedback; Reference signals; Reinforcement learning; state constraints; State observers; optimal control; Backstepping design; immeasurable states; state constraints; neural-networks (NNs)
• ISSN: 2329-9266; 2329-9274
• DOI: 10.1109/JAS.2021.1004018

In this paper, an adaptive neural-network (NN) output feedback optimal control problem is studied for a class of strict-feedback nonlinear systems with unknown internal dynamics, input saturation and state constraints. Neural networks are used to approximate unknown internal dynamics and an adaptive NN state observer is developed to estimate immeasurable states. Under the framework of the backstepping design, by employing the actor-critic architecture and constructing the tan-type Barrier Lyapunov function (BLF), the virtual and actual optimal controllers are developed. In order to accomplish optimal control effectively, a simplified reinforcement learning (RL) algorithm is designed by deriving the updating laws from the negative gradient of a simple positive function, instead of employing existing optimal control methods. In addition, to ensure that all the signals in the closed-loop system are bounded and the output can follow the reference signal within a bounded error, all state variables are confined within their compact sets all times. Finally, a simulation example is given to illustrate the effectiveness of the proposed control strategy.