Adaptive neural network control of uncertain robotic manipulators with external disturbance and time-varying output constraints.

This paper presents an adaptive neural network control scheme for a class of uncertain robotic manipulators with external disturbance and time-varying output constraints. A system transformation technique is applied to convert a constrained system into an equivalent unconstrained one for solving the time-varying output constraint problem. A model-based (MB) control scheme and an adaptive neural network (NN) control scheme, respectively, are designed by using backstepping technique. All the signals in the closed-loop system are proved to be uniformly ultimately bounded (UUB) via Lyapunov synthesis. In the adaptive control scheme, neural networks are employed to approximate the unknown closed-loop dynamics and external disturbance. A planar two degrees of freedom rigid robotic manipulator is used to be an illustrative case, where the robotic manipulator is controlled respectively by the proposed schemes and an existing robust adaptive NN control method without considering time-varying output constraints. Simulation results verify that the proposed adaptive NN controller yields better control performances in comparison to the robust adaptive NN controller without considering time-varying output constraints.