Research On Intelligent Optimization Control Algorithm For AC Speed Regulation System

In the AC drive system,the AC motor is essentially a kind of non-linear controlled object,and many nonlinear factors with elasticity or gap are included in the drive loads. For the existence of the parameters change and the linearity of the controlled object, the PID regulator with linear constant parameters can not attend to one thing without losing track of another thing.Thus, the system can not maintain the desireable performance in each kind of operating mode, in other words ,the robustness of the system can not entirely as desired. The intelligent control may fully use its non-linearity, the changing structure, automaticaly seeking the excellent parameters to overcome the parameters change and the non-linear factors of the electricity drive system, thus enhances the system robustness.Currently, in the SVPWM asynchronous motor vector-controlled systems ,conventional PI controllers are generally used to regulate the d-axis and q-axis currents. The main problem is the prominent contradictory between the overshoot and the rapidity. The practical d-axis current is easily affected by q-axis current and does not have strong anti-disturbance ability.In this paper, a kind of fuzzy PID current controller based on fuzzy logic is designed to implement on-line self-adjusting of the parameters of the conventional PID controllers. In order to enhance the anti-disturbance ability of the d-axis current loop, here a kind of fuzzy PID current controller is applied to d-axis current and a conventional PI controller is applied to q-axis current; In order to improve the dynamic and steady state performance of q-axis current, a fuzzy PID current controller is applied to q-axis current loop and a conventional PI controller is applied to d-axis current loop. The simulation results shown in the following part demonstrate that the two schemes taken here are satisfied with expected goals and can achieve better control performance of the current loops.On the other hand, for the fuzzy controller, the selection of input and output quantification factors and the output proportionality factor needs to consume massive time and energy,what is more, it is not certain that the factors are the best ones.Therefore, the genetic algorithms and the fuzzy control are unified to propose a way of optimizing quantification factors of fuzzy controller in the thesis, and thorough research on the fitness functions of the genetic algorithms is done. One kind of fitness function is selected for the optimization of the quantification factors of the fuzzy controller.The m documentrealization programmed in the MATLAB language is applied to implement floating number genetic algorithm, which is used to carry on the quantification factors proportionality factor optimization of the fuzzy PI controller , the working efficiency is improved. And this kind of fuzzy controller based on the floating number genetic algorithm is applied in the speed regulation loop .The genetic algorithm validity and superiority has been proven through lots of MATLAB simulation.