Adaptive Neural Tracking Control for an Uncertain State Constrained Robotic Manipulator With Unknown Time-Varying Delays

This paper presents an adaptive neural control strategy for an {n} -link rigid robotic manipulator with both state constraints and unknown time-varying delayed states. The design difficulties cause by the state constraints and unknown network-induced time-varying delays which appear in the {n} -link rigid robot simultaneously. In order to overcome these difficulties, the novel Barrier Lyapunov functions and an iterative backstepping technique are employed to guarantee constraint satisfaction of the position of the robot, the opportune Lyapunov-Krasovskii functionals and separation techniques are utilized to eliminate the effect of unknown functions with time-varying delayed states in communication channels. As the universal approximator, the neural networks are used to estimate the unknown functions of systems. By using the Lyapunov analysis, we can achieve that all the closed-loop signals are semiglobal uniformly ultimately bound, the tracking errors converge to a small set about zero and the good tracking performances of the system output. The feasibility of the proposed control algorithm can be demonstrated by providing simulation results.