Intelligent Optimal Control with Critic Learning for a Nonlinear Overhead Crane System
Date of Original Version
In this paper, for achieving the discounted optimal feedback stabilization of a nonlinear overhead crane system, we establish an intelligent control strategy to obtain the solution of the corresponding Hamilton-Jacobi-Bellman equation. Specifically, neural networks are employed to serve as a necessary component to the control system, which exhibits strong online learning ability. A novel updating rule compared to the traditional adaptive critic algorithms is developed, which eliminates the requirement of the initial stabilizing controller and brings in unique advantages to the adaptive critic control design. Stability analysis of the closed-loop system based on the well-known Lyapunov approach and experimental simulation considering the nonlinear overhead dynamics with different case studies are performed to verify the effectiveness of the present control method both in theory and applications.
IEEE Transactions on Industrial Informatics
Wang, Ding, Haibo He, and Derong Liu. "Intelligent Optimal Control with Critic Learning for a Nonlinear Overhead Crane System." IEEE Transactions on Industrial Informatics 14, 7 (2018): 2932-2940. doi:10.1109/TII.2017.2771256.