Finite control set model predictive control method for multi-motor control system

Abstract

The invention discloses a finite control set model predictive control method for a multi-motor control system. The finite control set model predictive control method is applied to the multi-motor control system which is composed of m permanent magnet synchronous motors. The finite control set model predictive control method for the multi-motor control system comprises the steps that the electrical quantity of the multi-motor control system is acquired; virtual reference voltage vectors are predicted and a value function is optimized, wherein for each motor, the corresponding virtual reference voltage vector existing at the k moment is calculated, and the value function is established; the process of optimizing the value function is simplified according to the vector zoning method; the common mode voltage of the multi-motor control system is restrained by adding the common mode voltage constraint to the method. Compared with a traditional finite control set model predictive control (FSC-MPC) method, the FSC-MPC method for the multi-motor control system has the advantages that on the basis that the control effect of the traditional method is maintained, the method implementing time is effectively shortened; meanwhile, cooperative control over the speeds of the multiple permanent magnet synchronous motors is achieved, and the purpose of restraining the common mode voltage is achieved.

Description

technical field [0001] The invention relates to a finite state model predictive control method. In particular, it relates to a finite state model predictive control method applied to a multi-motor control system. Background technique [0002] The AC motor system based on the finite state model predictive control method (finite control set model predictive control, FCS-MPC) has the advantages of no modulation algorithm, fast dynamic response, complete internal decoupling, and the ability to add various constraints to achieve optimal control, etc. , with great development prospects. FCS-MPC is derived from the traditional model predictive control (model predictive control, MPC) algorithm. The traditional MPC algorithm uses the system model to predict the future state, and at the same time considers the different control objectives of the system, conducts global optimization through the cost function (cost function), rolling optimization, and finally achieves the optimal effe...

AI Technical Summary

Problems solved by technology

Therefore FCS-MPC requires a shorter program computation time, however, it is impossible to achieve this in practice

When a single processor is used to realize the control of multiple motors, the calculation amount of FCS-MPC increases with the increase of circuit complexity, and the control cycle may not be set small enough

Moreover, since FCS-MPC of

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