Adaptive fuzzy CMAC control for a class of nonlinear systems with smooth compensation

Abstract

Adaptive fuzzy cerebellar model articulation controller (CMAC) schemes are proposed to solve the tracking problem for a class of nonlinear systems. The proposed method provides a simple control architecture that merges CMAC and fuzzy logic, so that the complicated structure and the input space dimension in CMAC can be simplified. Adaptive laws are developed to tune all of the control gains online, thereby accommodating the uncertainty of nonlinear systems without any learning phase. In particular, smooth compensation is adopted to overcome the chattering problem associated with conventional switching compensation. By Lyapunov stability analysis, it is guaranteed that all of the closed-loop signals are bounded and the tracking errors converge exponentially to a residual set whose size can be adjusted by changing the design parameters. Simulation results for its applications to three examples are presented to demonstrate the performance of the proposed methodology.

Intelligent complementary sliding-mode control with dead-zone parameter modification

“…The system dynamics of the M a n u s c r i p t Accepted Paper (ASOC-D-13-01314R1) 13 one-link robotic manipulator is referred as follows [34] u ml ml…”

Adaptive output recurrent cerebellar model articulation controller for nonlinear system control

“…This network has been already validated that it can approximate a nonlinear function over a domain of interest to any desired accuracy. The advantages of using CMAC over conventional NN in many practical applications have been presented in recent literatures (Gonzalez-Serrano et al 1998;Jan and Hung 2001;Chen et al 2005;Su et al 2006;Wu et al 2006;Peng et al 2005;Li and Leong 2004;Zheng et al 2006;Peng and Chiu 2008). The conventional CMAC uses constant binary or triangular receptive-field basis functions.…”

Intelligent tracking control of a DC motor driver using self-organizing TSK-type fuzzy neural networks

“…However, a trade-off problem between chattering and control accuracy arises. To attack this problem reducing the chattering phenomenon, several compensators were studied [15][16][17][18]. Wu et al [15] presented a smooth compensator to guarantee system stable; however, the tracking error can exponentially converge to a small neighborhood of the trajectory command.…”

“…To attack this problem reducing the chattering phenomenon, several compensators were studied [15][16][17][18]. Wu et al [15] presented a smooth compensator to guarantee system stable; however, the tracking error can exponentially converge to a small neighborhood of the trajectory command. Hsu et al [16] proposed a fuzzy compensator to completely remove the chattering phenomena; however, the adaptive law will make it go to infinity.…”