Adaptive neural network tracking control of robot manipulators including motor dynamics: Dynamic surface backstepping methodology

To solve the trajectory tracking control problem for rigid-link robot manipulators including actuator dynamics, a novel neural network (NN)-based adaptive algorithm is discussed. In the proposed control algorithm, radial basis function neural network (RBFNN) is adopted to approximate the nonlinear dynamics of the robot manipulators' electromechanical system. Moreover, the key features are that, firstly, the unmatched & uncertainties of the system are overcame, secondly, the problem of “explosion of complexity” inherent in the conventional backstepping method is avoided due to combing with “dynamic surface control” (DSC) approach. Finally, simulation results are included to demonstrate the tracking performance and the effectiveness of proposed algorithm.