Hybrid robust neural network control of rigid-link electrically driven robot manipulators

A new controller design approach is developed for the trajectory tracking of rigid-link electrically driven robot manipulators with uncertainties. Robust and neural network(NN) controller is incorporated into the backstepping scheme. The main advantage of the proposed approach is that a complicated differential computation is avoided and no joint angle acceleration measurement is needed. The components of the controller and the NN weight learning algorithm are given. The uniformly and ultimately bounded (UUB) stability of tracking errors and the estimates of NN weights and NN approximation error are guaranteed. Simulation results show the effectiveness of the proposed controller.