Adaptive output tracking of unknown mimo nonlinear systems using radial basis function neural networks

In this paper, a neural-network-based adaptive control is presented to solve the output tracking problem for a class of nonlinear continuous-time feedback linearizable MIMO systems with unknown nonlinearities. The adaptive control adopted in this paper ingeniously combines the conventional sliding control technique and the approximation scheme of the radial basis function (RBF) neural networks to perform approximating input-output linearization. The sliding control is used to compensate the network approximation errors and the neural network parameters are updated according to the Lyapunov principle. It is shown that the outputs of the closed-loop system asymptotically track the desired output trajectories while maintaining the boundedness of all signals within the system. The effectiveness and robustness of the proposed control scheme are demonstrated in the case of two-DOF robotic manipulator.