Model-free prediction current control method based on super-local model

Abstract

The invention provides a model-free prediction current control method based on a hyper-local model, and the method comprises the steps: A, employing indirect magnetic field directional control according to the hyper-local model of an asynchronous motor, and simplifying current control of a hyper-local model of the asynchronous motor into a first-order system through complex vector description; B,according to the simplified first-order system in the step A, combining the first-order system with the intelligent PI to obtain the input of a closed-loop system; C, estimating F in a short time by adopting a differential algebraic method, wherein F is a variable containing structural information of the system, an unknown part of the system and interference; and D, combining super-local model-free control with asynchronous motor indirect magnetic field directional prediction current control, and solving an estimated value of F according to the complex vector mathematical model and the super-local model under the two-phase static coordinate system of the asynchronous motor. According to the method, the robustness of model predictive control (MPC) to motor parameters is greatly improved.

Description

technical field [0001] The invention relates to a hyperlocal model design strategy for an asynchronous motor, in particular to a model-free predictive current control method based on a hyperlocal model. Background technique [0002] Model predictive control (MPC) is a kind of computer control algorithm that appeared in the field of industrial engineering control in the late 1970s, and has been widely used in process control industries such as chemical industry. MPC is a control strategy that has emerged in recent years. Compared with vector control (VOC), it does not require current inner loop and parameter setting, and directly generates inverter drive signals without pulse modulation. It is easy to deal with system constraints or add other control objectives. It has the advantages of simple structure, fast dynamic response and easy expansion. Compared with direct torque control (DTC), MPC optimizes and selects the best voltage vector by predicting the state of the motor, ...

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Problems solved by technology

[0003] However, MPC uses a large number of motor parameters for calculation in the prediction process and control process. When the motor is subjected to external disturbances and motor parameters change, the optimal voltage vector generated by the prediction will deviate, which will affect the overall control performance of the motor.

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