Design of RBF Neural Controller with Differencial Reconstruction and PID Conpensation for a Class of Nonlinear System with Linear Input Unmodeled Dynamics

Considered both the situation with unknown control function matrices and the situation with linear unmodeled input dynamics, adaptive neural robust controller was designed by using adaptive backstepping method for a class of multi-input to multi-output nonlinear systems which could be turned to "standard block control type". Furthermore, it is possible to make the network more stable and make the selection of simulation parameter more easy due to the introduction of differential reconstruction which increased the damp of the system. It was proved by constructing Lyapunov function step by step that all signals of the system are bounded and exponentially converge to the neighborhood of the origin globally. Finally, simulation study is given to demonstrate that the proposed method is effective and the known information of system was made use of as maximally as possible by introducing the PID control