Robust adaptive critic control of nonlinear systems using fuzzy basis function networks: An LMI approach |
| |
Authors: | Chuan-Kai Lin |
| |
Affiliation: | Department of Electrical Engineering, Naval Academy, 669 Chun Hsiao Road, Kaohsiung, Taiwan 813, Taiwan, ROC |
| |
Abstract: | This paper proposes an adaptive critic tracking control design for a class of nonlinear systems using fuzzy basis function networks (FBFNs). The key component of the adaptive critic controller is the FBFN, which implements an associative learning network (ALN) to approximate unknown nonlinear system functions, and an adaptive critic network (ACN) to generate the internal reinforcement learning signal to tune the ALN. Another important component, the reinforcement learning signal generator, requires the solution of a linear matrix inequality (LMI), which should also be satisfied to ensure stability. Furthermore, the robust control technique can easily reject the effects of the approximation errors of the FBFN and external disturbances. Unlike traditional adaptive critic controllers that learn from trial-and-error interactions, the proposed on-line tuning algorithm for ALN and ACN is derived from Lyapunov theory, thereby significantly shortening the learning time. Simulation results of a cart-pole system demonstrate the effectiveness of the proposed FBFN-based adaptive critic controller. |
| |
Keywords: | Adaptive critic control Fuzzy basis function network (FBFN) Robust control |
本文献已被 ScienceDirect 等数据库收录! |
|