Brushless direct-current motor Q learning-based variable domain fuzzy control method

Abstract

The invention discloses a brushless direct-current motor Q learning-based variable domain fuzzy control method. The method is designed to further improve the dynamic and static performance of a controller. According to the method of the invention, a concept which applies contraction-expansion factors and geometric proportional factors cooperating with one another to adjust a domain is put forwardon the basis of a variable domain adaptive fuzzy control algorithm; and the variable domain adaptive fuzzy PID control is improved on the basis of a Q-learning algorithm in the reinforcement learningtheory, so that the variable domain adaptive fuzzy PID control can have online optimization capacity; and since the two algorithms are combined and supplemented with each other, a better PID control parameter adjustment effect can be achieved, and therefore, a BLDCM (brushless direct-current motor) control system has better dynamic and static performance.

Description

technical field [0001] The invention relates to the field of brushless direct current motor control, in particular to a variable universe fuzzy control method based on Q learning of the brushless direct current motor. Background technique [0002] Brushless DC motor (BLDCM) has many advantages such as simple structure, reliable operation, convenient maintenance, wide speed range, high operating efficiency, and no excitation loss, so it has been widely used in various industrial fields. BLDCM is usually used as a DC servo motor in the AC servo system, but it has the characteristics of multivariable, nonlinear, strong coupling, etc., while the conventional PID control algorithm solves the control problem of linear time invariance, and the parameters have been set in advance. It cannot be adjusted with the change of the controlled object, resulting in low steady-state accuracy and anti-interference of the system, so the conventional PID control algorithm cannot achieve good con...

AI Technical Summary

Problems solved by technology

However, when the external environment of BLDCM changes or its own motor parameters change, the original PID parameters will no longer adapt, which will cause the BLDCM control system to be unstable.

Although the variable universe adaptive fuzzy PID control algorithm can make the BLDCM control system have faster response speed, better stability, smaller overshoot and stronger adaptability, but the variable universe adaptive fuzzy PID As time goes by, the controller will experience "distortion", which will lead to a decrease in control accuracy, a relative decrease in rapidity and anti-interference ability

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