Trajectory tracking improvement using neural networks

A neural network-based robust adaptive tracking controller is proposed for a class of nonlinear multi-input multi-output systems. The nonlinear system is treated as a partially known system. The known dynamic is used to design a nominal feedback controller based on the well-known feedback linearization method, and a neural network-based adaptive compensator is designed to compensate the effects of the system uncertainties and to improve the tracking performance. By this scheme, both strong robustness with respect to unknown dynamics and asymptotic convergence to zero of the output tracking error are obtained.