Adaptive neural network control of a 5 DOF robot manipulator

In this paper, a robust neural network based controller is proposed to steer the joint angles of rigid-link robot manipulators to track the desired trajectories asymptotically. The developed control scheme makes use of a two-layer neural network to learn the behaviors of unknown dynamics of robot. Both the estimation error and external disturbances can be effectively counteracted by employing smooth robust compensators. It is proved that arbitrarily small tracking errors could be achieved by selecting proper design parameters. In the control method derived here, there is no preliminary off-line learning phase, which is time-consuming, for better estimation of unknown nonlinear smooth function. The input weights are chosen randomly in and they are fixed in the whole simulation, and the adjustable output weights of neural network are simply initialized to be zero. The weight tuning algorithm for tunable parameters guarantees both closed loop stability and bounded weights. In the simulation, besides MATLAB, a famous multi-body dynamics analysis software in the world called ADAMS is employed. The combined simulation of ADAMS and MATLAB is able to produce realistic results of the closed loop system behaviors. The co-simulation results validate the effectiveness of the proposed approach.