Multi-scale approximate explicit model predictive control method for brushless dc motor

Abstract

A multi-scale approximate explicit model predictive control method for a brushless DC motor comprises the following steps: (1) modeling a brushless DC motor system to obtain a parameter optimization problem, that is, an object to be approximated below; 2) piecewise linear insertion method to obtain an approximate control law; 3) convert that form of the approximate control law into an adaptive layered function approximation; 4) introducing a barycenter function to obtain an approximate control law based on that barycenter function by use the barycenter interpolation value; and 5) multi-scale approximate explicit model predictive control of the motor control system. The invention improves the real-time property of the motor control, reduces the requirement of the memory capacity of the control, saves the on-line calculation time, and has a good control effect.

Description

technical field [0001] The invention relates to an optimal control method of a brushless motor. Background technique [0002] With the development of society, the role of motors has become increasingly prominent. Among all kinds of motors, the brushless DC motor is a kind of motor that replaces the traditional mechanical commutation by electronic devices. It has been widely used in computers, aviation, military, Industrial and office automation and other fields. The brushless DC motor can not only maintain the superior characteristics of the brushed DC motor, but also avoid a series of problems caused by the brush, including the disadvantages of large mechanical friction loss, high noise and short life, so it expands the DC motor to a certain extent. application range. [0003] For dealing with multivariable constrained optimal control problems, model predictive control method is a very effective method. After years of research and exploration, it has achieved great devel...

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Problems solved by technology

[0004] Explicit Model Predictive Control (Explicit Model Predictive Control) introduces the theory of multi-parameter programming, divides the state area of ​​the system convexly, and establishes the explicit relationship between the optimal control law and the state corresponding to the optimization problem on each state partition. Functional relationship (linear control law for the state); this method also has its limitations, it is only suitable for constrained systems, and the complexity will increase exponentially with the increase of the problem size, that is, when the number of inputs increases or When the control time domain becomes longer, a large storage space is required, which makes it more difficult to find the control law and deal with the problem

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