Neural Network Based Adaptive Passive Control for a Class of MIMO Nonlinear Uncertain Systems

An adaptive passive control problem is studied for a class of multi-input multi-output nonlinear systems with unknown nonlinearities and unknown parameters. A neural network is used to identify unknown nonlinearities, and an adaptive law of weight parameters is proposed. The design methods of the adaptive passive controllers for this class of systems are discussed under two different conditions that the unknown constant parametric matrixes of control input are symmetric positive definite matrixes and revertible matrixes, respectively. The corresponding adaptive passive controllers and parametric adaptive laws are designed and presented under the two kinds of conditions, respectively. It is proved that the closed-loop system composed of the original system and the designed controller is stable by the Lyapunov method, and the controller designed can render the system adaptive passive. Finally, a simulation example is given to prove the effectiveness and feasibility of the proposed method.