What are the control strategies of stepping motor?

1, PID control

As a simple and practical control method, PID control has been widely used in stepping motor drive. It forms the control deviation e(t) according to the given value r(t) and the actual output value c(t), and forms the control quantity through the linear combination of the proportion, integral and differential of the deviation to control the controlled object. In this paper, the integrated position sensor is applied to the two-phase hybrid stepping motor. Based on position detector and vector control, an automatically adjustable PI speed controller is designed, which can provide satisfactory transient characteristics under off-design conditions. According to the mathematical model of stepping motor, the PID control system of stepping motor is designed, and the control quantity is obtained by PID control algorithm, so as to control the motor to move to the specified position. Finally, the simulation results show that the controller has good dynamic response characteristics. Using PID controller has the advantages of simple structure, strong robustness and high reliability, but it can not effectively deal with the uncertain information in the system.

At present, PID control is more combined with other control strategies to form a new type of intelligent compound control. This intelligent compound control has the ability of self-learning, self-adaptation and self-organization, and can automatically identify and adjust the parameters of the controlled process to adapt to the changes of the parameters of the controlled process. At the same time, it has the characteristics of conventional PID controller.

2. Adaptive control

Adaptive control is a branch of automatic control field developed in 1950s. With the complexity of the controlled object, when the dynamic characteristics are unknown or unpredictable, a high-performance controller is produced. Its main advantages are simple implementation and fast adaptive speed, which can effectively overcome the influence caused by the slow change of motor model parameters. It is an output signal for tracking reference signals. According to the linear or approximate linear model of stepping motor, literature researchers have derived globally stable adaptive control algorithms, which depend heavily on motor model parameters. This document combines closed-loop feedback control with adaptive control to detect the position and speed of the rotor. Through feedback and adaptive processing, the driving pulse train is automatically sent out according to the optimized lifting operation curve, which improves the traction torque characteristics of the motor and enables the motor to obtain more accurate position control and higher and more stable speed.

At present, many scholars combine adaptive control with other control methods to solve the shortcomings of simple adaptive control. The robust adaptive low-speed servo controller designed in the literature ensures the maximum compensation of rotor torque and the low-speed and high-precision tracking control performance of the servo system. The adaptive fuzzy PID controller realized in the literature can adjust PID parameters online through fuzzy reasoning according to the change of input error and error change rate, and realize the adaptive control of stepping motor, thus effectively improving the response time, calculation accuracy and anti-interference ability of the system.

3. Vector control

Vector control is the theoretical basis of modern motor high performance control, which can improve the torque control performance of motor. It divides the stator current into excitation component and torque component through magnetic field orientation and controls them separately, thus obtaining good decoupling characteristics. Therefore, vector control needs to control both the amplitude of stator current and the phase of current. Because the stepping motor not only has the main electromagnetic torque, but also the reluctance torque caused by the biconvex structure, and the internal magnetic field structure is complex, the nonlinearity is much more serious than that of the general motor, and its vector control is also more complicated. The mathematical model of d-q axis of two-phase hybrid stepping motor is derived in reference [8]. The vector control system is realized by PC, with the rotor permanent magnet flux linkage as the directional coordinate system, the direct axis current id=0, and the electromagnetic torque of the motor is proportional to iq. In this system, the sensor is used to detect the winding current and rotation position of the motor, and PWM is used to control the winding current of the motor. The model of two-phase hybrid stepping motor based on magnetic network is derived, and the structure of its vector control position servo system is given. The neural network model references the adaptive control strategy to compensate the uncertain factors in the system in real time, and the maximum torque/current vector control controls the motor efficiently.

4, the application of intelligent control

Intelligent control does not depend on the mathematical model of the controlled object, but only controls according to the actual effect. It has the ability to consider the uncertainty and accuracy of the system in control, breaking through the framework that traditional control must be based on mathematical model. At present, the application of intelligent control in stepping motor system is the integration of fuzzy logic control, neural network and intelligent control.

4. 1 fuzzy control

Fuzzy control is a method to realize system control based on the fuzzy model of the controlled object and the approximate reasoning of the fuzzy controller. As a control method that directly simulates the results of human thinking, fuzzy control has been widely used in the field of industrial control. Compared with conventional control, fuzzy control does not need accurate mathematical model, has strong robustness and adaptability, and is suitable for the control of nonlinear, time-varying and time-delay systems. Document [16] gives an application example of fuzzy control in speed control of two-phase hybrid stepping motor. The system is advanced angle control, and the design does not need a mathematical model, so the speed response time is short.

4.2 Neural Network Control

Neural network is a method that uses a large number of neurons to adjust and learn according to a certain topological structure. It can completely approach any complex nonlinear system, learn and adapt to unknown or uncertain systems, and has strong robustness and fault tolerance, so it has been widely used in stepping motor systems. In the literature, neural network is used to realize the optimal subdivision current of stepping motor, Bayesian regularization algorithm is used in learning, and the use right adjustment technology is used to avoid the multi-layer forward neural network from falling into local minimum, which effectively solves the problem of equal step angle subdivision.