Motion control of mobile manipulator based on neural networks and error compensation

A neural network based controller is derived for a mobile manipulator to track the given trajectories in the workspace. The dynamics of the mobile manipulator is assumed to be unknown completely, and is learned on-line by the radial basis function network (RBFN) with weight adaptation rule derived from the Lyapunov function. Generally, a RBFN can be used to properly approximate a nonlinear function. However, there remains some approximation error inevitably in real application. An additional control input to suppress this kind of error source is also used. The proposed algorithm does not need a priori knowledge about the exact system dynamic parameters. Simulation results for a two-link manipulator on a differential-drive mobile platform are presented to show the effectiveness for uncertain system.