Observer-based neural adaptive control for a class of MIMO delayed nonlinear systems with input nonlinearities.

An adaptive output-feedback control method is developed for a class of multi-input and multi-output (MIMO) delayed nonlinear systems which are subject to input saturated nonlinearities, modeling uncertainties and time delays. Because state variables are unobtainable, state observers are constructed first. And radial basis function (RBF) neural networks (NNs) are used as approximators to identify the unknown nonlinearities. Adaptive technique is applied to estimate the optimal weight vectors of the approximators. Backstepping method is used to construct the desired controllers. Based on Lyapunov stability theory, the proposed tracking control strategy ensure that all signals in the closed-loop systems are bounded and the target trajectories can be tracked within a enough small error as well. At last, numerical simulations demonstrate the effectiveness of the presented control method. [ABSTRACT FROM AUTHOR]