Disturbance Observer-based Adaptive Neural Control for a class of Uncertain MIMO Systems with State and Input Constraints

In this paper, an integral barrier Lyapunov function combined with the RBF neural network are introduced for a class of MIMO nonlinear systems with unknown nonlinearities. Under the premise of the system output and its first derivative of the time being measurable, by applying the integral barrier Lyapunov function, we have designed a controller based on the adaptive RBF neural network which uses a disturbance observer to compensate the error. The system is proved to be semi-globally uniformly ultimately bounded and the tracking error is convergent and bounded. Finally, we have made a simulation on a 2-DOF robotic manipulator systems to examine the effcetiveness of the proposed design.