Sensorless Position Control And Neural Network Control Of SRM

Switched Reluctance Drive (SRD) system is a novel AC driving system. It has a characteristic of simple structure, robustness, low cost, reliability and control flexibility. It has many control method and can get varity machinery characteristic. Also, it has a high efficiency in a wide speed range. This is super to other adjustable speed driver system.For its high saturation of magnetic circuit and special double salient structure and high saturation of local magnetic circuit, the traditional circuit analysis method is no longer suitable to be used to calculate the distribution of flux of SRM. Magnezation finite element method (FEM) is a powerful tool to calculate the distribution of flux. In this paper, FEM is used to analysis the distribution of flux of switched reluctance motor (SRM). The distribution of magnetic dense and the equipotential lines of the SRM used in this paper are obtained. Also, the flux linkageψ(θ,i) is calculated at different current and different rotor position. This is the base to calculate the performance of SRM accurately. The analysis also provides a reference to design the magnetic circuit.The highly saturation of magnetic circuit and the doubly salient structure of SRM lead to flux linkage is in nonlinear function of both rotor position and phase current. Building up this nonlinear mapping is the base to calculate the property of SRM accurately. Artificial neural networks (ANN) under certain condition can approximate any nonlinear function with arbitrary precision which also has a strong learning ability and adaptive ability. While Takagi-Sugeno (T-S) type fuzzy logic which antecedents are fuzzy sets and consequents are linear combination of the input variables, the output of which are crisp values. So the inference process of it can be simplified. And it can take advantage of the numerical information and language information. So, in this paper, one form of T-S type fuzzy logic– pi-sigma neural networks is adopted to develop the nonlinear model of SRM. By taking advantage of the benefit of neural network and fuzzy logic, a high precision model of SRM with a characteristic of robustness, error tollerance, and high precision is developed. The simulation results proved this.The accurate and real-time rotor position information is very important for high performance operating of SRM. Traditionally, the rotor position information is provided by a mechanical rotor position sensor. But this increases cost, size and manufacture complexity of SRM and reduces the reliability of the system. So the sensorless control method of SRM brcome a hot research region. Adaptive network based fuzzy inference system (ANFIS) is used in this paper to map the nonlinear function of rotor position with respect to flux linkage and phase current. After the flux and current are measured, the rotor position is computed by the ANFIS. The advantage of this method is that it has strong error tolerance ability, high noises restrain ability, high accuracy and robust. The experimental results proved the effectiveness of the proposed method.The high saturation of magnetic circuit leads to the nonlinear of parameter of SRM and under different control method it is variable structure. This makes it hard to get a good performance by using the conventional PI, PID controller to the speed control of SRM. Other control method such as sliding model control, state-space control method can get a good control performance. But they are too complexity. ANN control - one of intelligent control, under certain condition, can approximate any nonlinear function with arbitrary precision. It also has a strong ability of self-learning and adaptive. So it can be used to control nonlinear, uncertain, unknown, variable structure, time varing and methmatical model unknown plant. By combining it with the conventional PID controller, an adaptive PID controller is developed in this paper. Meanwhile nonlinear prediction model based on a radial basis function (RBF) ANN is build up to predict the parameter of the system. This improves the dynamic response of the system. This control method has the advantage of PID controller's high precision and implement easily. It also has the advantage of ANN's characteristic of adaptive. Appling it to the speed control of SRM, a good control performance is got. The system responds quickly with little overshot and is robust.