Xiu Jie

Fuzzy Logic Based Adaptive PID Control of Switched Reluctance Motor Drive

The severe nonlinearity of switched reluctance motor (SRM) make it hard to get a good control performance with the conventional PID controller. Therefore, in this paper, fuzzy logic based adaptive PID control strategy is developed. According to the error and change-in-error in the transient period, parameters of the adaptive PID controller are tuned on online according to fuzzy logic tuning rules which are summarized from the experts control knowledge and operators experience. Fuzzy logic based adaptive PID controller has the merit of both fuzzy controller and PID controller. It has the advantage of flexibility, adaptive, expert knowledge based, robustness, model free and high control precision. Experimental results demonstrate that a good control performance is achieved. The system responds quickly with little overshoot. There is no error at steady state. The system shows a strong ability to reject disturbance and a strong characteristic of robustness.

Fuzzy gain based adaptive fuzzy logic controller for BLDCM drive

An fuzzy gain based adaptive fuzzy logic controller (FLC) is developed for the speed control of brushless DC motor (BLDCM) drive in this paper. In a FLC, the way to improve the performance of the FLC is to tune the fuzzy rules, membership function and scaling gains of inputs and output. The scaling gains of the inputs and output of the proposed fuzzy controller are parameters that can be tuned conveniently. So, in this paper, an adaptive FLC with the tuning of scaling gains by fuzzy logic is proposed. The advantage of this method is that the universe of discourse of the fuzzy sets can be tuned by adjusting the scaling gains according to the speed error. This significantly improves the dynamic response and steady state performance of the system under the control of the proposed adaptive FLC. In this paper, the principles of tuning scaling gains of the inputs and output are described. The proposed adaptive FLC has the characteristics of robustness, adaptive and facility to take advantage of human control knowledge. The experimental tests are carried out for the proposed adaptive FLC. The experimental results demonstrate that the proposed adaptive FLC presents a superior performance than the conventional FLC.

An Application of Fuzzy Logic Controller for Switched Reluctance Motor Drive

This paper develops a fuzzy logic controller (FLC) for the speed control of switched reluctance motor (SRM) drive. The advantage of this method is that the proposed FLC has the characteristics of robustness, nonlinearity and facility to take advantage of human control knowledge. In this paper, the inputs and output of the FLC are described. Also, the principles of the fuzzy logic control are given. The universe of discourse of error, error in change and output are given. The control rule base in the form of linguistic rule is given. The control rule surface is given. The experimental tests are carried out for the proposed FLC. The experimental results demonstrate that the proposed FLC presents a better performance than the conventional PID controller.

GA based PI control of BLDCM Drive

The brushless DC motor (BLDCM) has a good control performance. The classical PI controller is widely used controller. But the determining of its parameter needs the mathematical model of the system. Usually the exact mathematical model of system is hard to known. This places a restriction on the applying of the commonly used Z-N method to determine the parameters. Also the parameter determined by the Z-N method is not the optimum one. GA is an optimization technique that performs a parallel, stochastic, but directed search to evolve the most fit population. And it dose not need the gradient information of object function. But only dependent on the evaluation function, it can guide the optimum process. Except this, the GA algorithm also has the property of robustness and parallel computation. Therefore, in this paper, the GA algorithm is applied to optimum the parameters of PI controller. Through this method, the response of the system is improved and overshoot is reduced and steady state error is eliminated. The determining of parameters of PI controller is simplified and the optimum response of system can be reached. Experimental results demonstrate that a good control performance is achieved. The system responses quickly with little overshoot. There is no error in steady state. The system shows a strong ability to reject disturbance and a strong characteristic of robustness.

DSPM radial force static characteristic analysis and RBF NN modeling under rotor eccentric

The rotor eccentric is a normal state when DSPM running. When the rotor is eccentric, the air gap magnet density under relative pole is not equal. So, this causes large radial force acting on rotor and stator. Which causes vibration and noise. So, the analysis of radial force under rotor eccentric is the base to reduce vibration and noise. In this paper, FEM is used to calculate the static characteristic of radial force under rotor eccentric. The change rule of radial force with different rotor position, winding current, eccentric degree and eccentric angle is analyzed. The nonlinear mapping among radial force, rotor position, winding current, eccentric degree and eccentric angle is realized by improved PSO optimized RBF NN. This work forms the base to radial force control and parameters optimization.

The single neuron adaptive PI control of SRM based on IPSO

Switched reluctance motor (SRM) has a strong nonlinear characteristic. This makes the traditional PI controller hard to get a good control effect. Single neuron has the simplest structure and fastest calculation speed. It is suitable to be applied on-line. Under certain condition, it can approach any nonlinear function with arbitrary precision and has strong study ability and adaptive ability. So, by combining it with traditional PI controller, the parameters of PI controller can be adaptively adjusted on-line. It is suitable to control nonlinear SRM. To get a super performance, the scale factor is set as a variable varied with dynamic response and improved PSO algorithm is proposed to optimum parameters of single neuron in this paper. This improved the convergence speed and precision of single neural controller. The scale factor K is also treated as a variable. Experimental results show that the system respond quickly, there is little overshot, the precision of stable state is high, also has a strong disturbance reject ability under the control of the proposed control scheme.