Study On Some Control Problems For Uncertain Composite Systems

Modern society faces studying and dealing with the system problems with large dimension and complicated structure (such as manufacturing process, economy management, environment, electrical net control, traffic transport, aviation ect), which make the theory of large scale systems become a special field. However, because of the complication of problems, there is no series of intact theory and method. Therefore, it may be an effective way to make full use of the structure of systems, and adopt special method for systems with special structure, then study the general large-scale systems. In the theory of the large scale systems, it is important to study the theory of the composite systems because of its special structure (composed of a series of interconnected subsystems) and uncertainty.In this dissertation, by means of the techniques of adaptive neural networks control, the tracking problem and robust stabilization of a class of nonlinear composite systems are studied. Nonlinear composite system is identified using neural networks, the control law and adaptive law are designed to guarantee the uniform ultimate boundedness property for the states of closed systems, and the weights of neural networks are updating on-line. In additon, the problem of robust decentralized stabilization is discussed for a class of uncertain delay composite systems, and the designed controller has strong robustness for uncertainty.The main work is as follows:1. Adaptive neural networks controller is designed to realize the tracking control for a class of nonlinear composite systems and uncertain composite systems. Firstly, nonlinear composite systems and uncertain composite systems are identified using dynamical neural networks, secondly the controller and updating law are designed to guarantee the uniform ultimate boundedness property for the tracking error and other...