Approximation-Based Adaptive Neural Tracking Control of an Uncertain Robot with Output Constraint and Unknown Time-Varying Delays

摘要

This paper presents an adaptive neural control design for an n-link rigid robot with both output constraint and unknown time-varying delays. The main design difficulties caused by both the output constraint and unknown time-varying delayed states. In order to overcome these difficulties, the novel Barrier Lyapunov Functions (BLF) and iterative backstepping procedures are employing to guarantee constraints satisfaction of the position of the robot. The Lyapunov-krasovskii functionals (LKFs) are utilized to eliminate and compensate the effect of unknown functions with time-varying delayed states in communication channels. By using the Lyapunov analysis, the stability of closed-loop systems is proven.