Research On Intelligent Control Theory And Applications Of Sliding Mode Variable Structure

Many control programs have been proposed for the fast-changing nonlinear complex systems like the AC drives which have solved some problems of these systems, but there are still some shortcomings in those programs. Therefore, It is necessary to develop a practical and efficient controller for the fast-changing complicated industrial systems. The sliding mode variable structure is able to response quickly, invariant to systemic parameters and external disturbance, and able to keep the system asymptotic a stable state. What is more precious is that its algorithm is simple and it is easy to be realized engineering. But it has such shortcomings as chattering arid the requirement of the knowledge of the upper and lower bounds of uncertain parameters. The fuzzy control requires no mathematical model arid its mechanics are agreeable to people's logical thinking and people's direct description of the process control, but, it is not systematic in design, its control rule selection is conducted with the trial approximation method. The neural network is powerful in its self-learning abilities. It is able to approximate fully any complicated nonlinear state, but it is difficult to be applied to the control of comparatively complicated objects because of its slow learning speed. It can be concluded that the variable structure control, fuzzy control, and neural network control are greatly complementary. The aim of this research is to go into the complementarities of the three and realize the intelligent control of sliding mode variable structures. The content of this research falls into the following five parts: 1. The characteristics of the fuzzy systems and the neural network are studied. It gives a detailed comment on the present development of the sliding mode variable structure and analyzes the controlling strategies in AC drive. 2. Directed to the alternating speed regulation controller composed of the current source inverter motor(CSIM), this paper first analyzes the problems of CSIM at a low speed, infers the simplified mathematical model of CSIM on the basis of the insensitivity of the sliding mode variable structure to parameter variations, and then designs a CSIM controller in light of the routine sliding mode variable structure theory. Finally, it realizes the control of the system on the microcomputer and shows the experimental and simulation results. 3. A sliding mode variable structure control is introduced on the basis of the fuzzy neural network. The essay first adjusts on-line the value of the symbolic function terms with the fuzzy neural network, then infers the learning rate range of the fuzzy neural network in which the stability of the controller is warranted by using the Lyapunov stability theorem, and then develops a fuzzy neural network sliding mode controller capable of all-parameter adjustment for the great parameter change, and introduces a design method for a neural network sliding mode robust controller which keeps the system in a sliding state in any initial conditions and realizes the omnidistance sliding mode control over the movement of the system. 4. In view of the slow convergence of learning speed of the neural network, this research lays its emphasis on the development of a method to raise the converging speed of the neural network. It first uses a fast scale-change optimal learning algorithm to approximate the uncertain system and propose a new sliding mode variable structure control method based on systemic identification. Then, in combination with...