A Motor Speed ​​Tracking Control Method Based on Adaptive Fuzzy Neural Network

Abstract

The invention relates to a motor rotating-speed tracking control method based on a self-adaptive fuzzy neural network, wherein rotating-speed and current double closed-loop control is adopted, an outer ring is a rotating-speed ring, a sliding-mode control theory-based fuzzy neural-network controller (SMFNN) is designed, an inner ring is a current ring, and a PI (Proportional-Integral) controller is adopted; a fuzzy neural-network rotating-speed controller comprises two parts, wherein one part is a PID (Proportional-Integral-Derivative) controller, and the other part is the fuzzy neural network, online real-time learning is carried out through the fuzzy neural network by utilizing a parameter correcting method designed on the basis of the sliding-mode control theory, and the two parts jointly act to obtain the output ir of the rotating-speed controller, i.e. a difference obtained by subtracting the output iFNN of the fuzzy neural network from the output iPID of the PID controller is used as the output ir of the rotating-speed controller. The control precision and the anti-interference performance of a motor speed-adjusting system can be improved through the control strategy of the motor rotating-speed tracking control method.

Description

Technical field

[0001] The invention relates to a motor speed regulation method, in particular to the field of motor intelligent speed regulation control. Background technique

[0002] The motor speed control system is widely used in elevators, medical equipment, inverter air conditioners, electric vehicles and many other fields, and people's requirements for the control accuracy of the motor speed control system are also getting higher and higher. Therefore, the motor speed tracking control method has become a research hotspot in the scientific research circle.

[0003] At present, the motor speed control system usually adopts the proportional-integral (PI) control strategy. However, in practical applications, the time-varying, nonlinear, and strong coupling characteristics of the motor make the traditional control strategy unable to meet the high-precision dynamic and static conditions of the system. Performance. The latest achievements such as optimal control, model ref...

Images