Approximation-based tracking control of uncertain MIMO nonlinear systems with input saturation

In this paper, an approximation-based robust adaptive tracking control is developed for a class of uncertain multiple-input and multiple-output (MIMO) nonlinear systems with unknown disturbances and input saturation. Radial basis function neural networks (RBFNNs) are used to approximate the function uncertainties of MIMO nonlinear systems. An auxiliary design system is introduced to analyze the constraint effect of the input saturation and its states are used to design tracking control scheme. In the proposed tracking control, the nonsingular assumption of neural network (NN) approximation of completely unknown control coefficient matrix and boundary restriction between NN approximation error and control input are eliminated for uncertain MIMO nonlinear systems. Rigorous stability analysis shows that semiglobal uniform boundedness of all signals is guaranteed by appropriately choosing design parameters.