With the advent of the era of intelligence and the Internet of Things, the control requirements of the stepper motor are becoming more accurate. In order to improve the accuracy and reliability of the stepper motor system, the control methods of the stepper motor are described from four directions:
1. PID control: According to the given value r(t) and the actual output value c(t), the control deviation e(t) is constituted, and the proportion, integral and differential of the deviation are constituted by a linear combination to control the controlled object.
2, adaptive control: with the complexity of the control object, when the dynamic characteristics are unknowable or unpredictable changes, in order to obtain a high-performance controller, a globally stable adaptive control algorithm is derived according to the linear or approximately linear model of the stepper motor. Its main advantages are easy to implement and fast adaptive speed, can effectively overcome the influence caused by the slow change of motor model parameters, is the output signal tracking reference signal, but these control algorithms are heavily dependent on the motor model parameters
3, vector control: vector control is the theoretical basis of modern motor high-performance control, which can improve the torque control performance of the motor. It divides the stator current into excitation component and torque component to control by magnetic field orientation, so as to obtain good decoupling characteristics. Therefore, vector control needs to control both the amplitude and the phase of the stator current.
4, intelligent control: it breaks through the traditional control method that must be based on the framework of mathematical models, does not rely on or not completely rely on the mathematical model of the control object, only according to the actual effect of control, in the control has the ability to consider the uncertainty and accuracy of the system, with strong robustness and adaptability. At present, fuzzy logic control and neural network control are more mature in application.
(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. The system is advanced Angle control, the design does not need mathematical model, the speed response time is short.
(2) Neural network control: Using a large number of neurons according to a certain topology and learning adjustment, it can fully approximate any complex nonlinear system, can learn and adapt to unknown or uncertain systems, and has strong robustness and fault tolerance.
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Post time: Jul-21-2023
