Controller Design For Three Kinds Of Nonlinear Uncertain Systems

As everyone knows, the control system often suffer interference in various complex environments, and this interference is difficult to completely avoid, this is the uncertainty of the system.Caused by factors of the uncertainty of the system include: random factors and fuzzy factors,grey factors are the main sources of uncertainty: measurement error parameters, the parameter identification error and parameters of the actual value and the nominal error etc. This paper mainly studies the random factors of parameters and the actual value and the nominal value of the error due to the problem of uncertain system. For three classes of uncertain nonlinear systems. Design a feedback controller,controller,controller. The main contents are as follows:1. The state feedback controller and the output feedback controller are designed for a class of system problems caused by the actual value of the parameters and the nominal value error. By using Lyapunov-Krasovskii functional method, Schur complement lemma and Lyapunov stability theorem, the sufficient conditions for the asymptotic stability of the system are given. Then, the conditions of the existence of the two controllers are obtained, and two methods are proposed to solve the controller gain. The numerical example is used to verify the effectivenessof the method.2.The design problem of a class of passive fault-tolerant controllers is studied. Two fault-tolerant controllers are given for the same system. The sufficient conditions for the closed-loop stability of the system are obtained in the robust controller. For the second controllers, the model tracking algorithm is mainly used to recover the performance of the system by the method of re calculating the gain. A numerical example was used to verify the validity of the condition.3.In this chapter mainly introduces a kind of parameter uncertain system of stabilization controller design problem. First of all, the delay partitioning and the free weighting matrix system in a robust state feedback control conditions for the asymptotic stability is obtained. The optimal solution obtained through iteration, introducing new variables,finally obtained the method of stabilizing control system. Through the numerical superiority compared with the verification system.4.Finally, the main results of the dissertation are concluded, and the issues of future investigation are proposed.