Adaptive neural boundary control for state constrained flexible manipulators

This article discusses the adaptive neural tracking control issue for a flexible manipulator system with time-varying full-state constraints. First, the flexible manipulator system is modeled using partial differential equations with boundary conditions. Second, neural network techniques are used to deal with unknown nonlinear functions. Based on the backstepping technique, an adaptive neural boundary controller is developed that effectively suppresses the effects of input saturation. Moreover, the construction of the asymmetric time-varying barrier Lyapunov function guarantees that the full-state constraints of the system are met and that the closed-loop system signals remain bounded. Finally, simulations are performed, and the results demonstrate the efficacy of the proposed approach.