Adaptive Neural Network Control of Nonlinear MIMO Systems with Unmeasured States and Unknown Control Coefficients

This paper develops an adaptive neural network tracking control scheme for a class of nonlinear multi input and multi output uncertain systems with unmeasured states and unknown control coefficients. In the control scheme, a neural state observer is established to estimate the unmeasured states and Nussbaum function is introduced to compensate the influence of the unknown control coefficients. Meanwhile, the neural network technique is used to approximate the combinational uncertainties at each step of backstepping process. It is proved that the outputs of the system to track the target trajectory within a compact set and all signals of the closed-loop system are bounded. Finally, a simulation example is provided to show the effectiveness of the proposed control method.