Fuzzy Adaptive Control Of Asynchronous Motor Based On Reduced-Dimensional Observer

In recent years, the Induction Motor is widely used in AC drive control. The characteristics of stable performance, simple structure and the adaption to complex environment are the key factor. With the development of the motor speed control, the advanced control strategy based on the power electronic technology and the control theory is put forward. To obtain the speed information of the motor rotor, usually adopt the way that the sensor installed on the motor shaft. Lead to the improvement of the system cost, make the simple asynchronous motor system complicated, the reliability of the motor operation is affected. At the same time, the asynchronous motor's own characteristics of strong coupling and high nonlinear further increases the difficulty to control. In order to improve the dynamic and static performance of asynchronous motor system, the sliding mode control, adaptive control and backstepping control methods have been applied to the field of motor control, the control result is remarkable.In this paper, based on the structural characteristics of asynchronous motor, in order to solve the problems above, design a reduced-order observer and fuzzy logic system. The reduced-order observer model is used to estimate the motor shaft angular velocity, in order to avoid the use of the motor shaft, sensor. The nonlinear unknown functions of asynchronous motor model, use the fuzzy logic system to deal with, eventually the controller is constructed by the backstepping control.Paper main research results are as follows:Firstly, this paper introduces the research background and significance. This paper studies the development current of asynchronous motor control strategy, and lists several commonly used advanced control strategy.Second, the control of a class of single input single output strict feedback nonlinear systems based on observer and fuzzy logic is proposed. The reduced-order observer model is used to estimate state virables, fuzzy logic systems are used to approximate the unknown nonlinear functions in the systems, then backstepping is employed to design adaptive fuzzy controllers. Finally, Lyapunov method is used to analyze the stability of the system.Thirdly, for the model for asynchronous motor, the new fuzzy adaptive position controller is designed. The reduced-order observer model is used to estimate the motor shaft angular velocity, Fuzzy logic systems are used to approximate the unknown nonlinear functions in the induction motor systems, backstepping is employed to design controllers. The simulation results are provided to demonstrate the effectiveness.Fourthly, for the dynamic model of induction motors including iron losses, backstepping technology is used to design the new fuzzy adaptive position controller based on observer and fuzzy control. Fuzzy logic systems are used to approximate the unknown nonlinear functions in the induction motor systems. The reduced-order observer model is used to estimate state virables. Furthermore, backstepping is employed to design controllers. It should be pointed that the proposed controller has a simple structure and only one adaptive parameter. So it will be easy to be implemented in practice. And also the controllers can guarantee that the boundedness of all the signals in the closed-loop system. The simulation results are provided to demonstrate the effectiveness and robustness against the parameter uncertainties and load disturbances.