Robust adaptive tracking control via CPBUM neural network for MIMO nonlinear systems

The design of robust adaptive tracking control for MIMO unknown nonlinear systems, which is the combination of the approximation method of Chebyshev polynomial based unified model (CPBUM) neural network, adaptive control algorithm and H^∞ control technique, have been successfully proposed. The proposed robust adaptive tracking control structure uses the good approximation ability, fast learning speed, and systematic design method of CPBUM neural network in controller with rough tuning and the H^∞ disturbance attenuation technique in fine tuning. Hence, this proposed structure has good adaptability and robustness for the close-loop system. Finally, a two-degree of freedom robotic manipulator is given to demonstrate the validity of proposed method.