Motor system control parameter optimization method based on sliding mode extremum search

Abstract

According to a motor system control parameter optimization method based on sliding mode extremum search, a large amount of iterative computation is not needed, the requirement for hardware is low, and the method has excellent use value and very high cost performance. According to the method, a cost function having the extreme value relation with the system performance is constructed, and the value of the cost function is determined by the control parameter, so that when the cost function is the extreme value, the current control parameter is the optimal control parameter of the permanent magnet synchronous motor system. According to the method, searching of the cost function extreme value is achieved through sliding mode extreme value searching, optimization of the control parameters is achieved, only one control parameter initial value needs to be set, the calculated amount is small, the requirement for hardware is low, and the method has excellent use value and high cost performance.

Description

technical field [0001] The invention relates to the technical field of motor control, in particular to a method for optimizing control parameters of a motor system based on sliding mode extremum search. Background technique [0002] With the maturity of electric energy-related technologies such as power conversion, storage, and transmission, motor drive has become one of the important ways to drive mechanical equipment in the world today. Among them, the permanent magnet synchronous motor is widely used in life and production because of its advantages such as wide speed range, high efficiency, high power density ratio and high torque current ratio. [0003] The system with the motor as the core actuator is a typical multivariable, nonlinear and strongly coupled system, and its good performance is achieved through complex control. A common permanent magnet synchronous motor system based on vector control adopts a double-closed-loop structure of speed and current, and its spe...

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Problems solved by technology

[0004] The existing motor system control parameter optimization methods can be divided into two types: model-based and data-based. Model-based methods such as ZN method and relay feedback method all need to obtain more accurate motor system models and motor parameters. In actual operation The optimization of motor system control parameters is achieved through parameter identification, but it is difficult to obtain more accurate motor system models and motor parameters in actual situations; data-based methods use genetic algorithms and particle swarm algorithms, etc. These methods require a large number of iterative calculations , the hardware requirements are high, and the general speed and current controllers are difficult to implement

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