Single current sensor predictive control and parameter disturbance suppression method for permanent magnet synchronous motor

Abstract

The invention provides a single current sensor predictive control and parameter disturbance suppression method for a permanent magnet synchronous motor. The reconstructed phase current based on the single current sensor is applied to the improved deadbeat predictive control, which reduces the cost of the control system and reduces the control cost. The volume of the system avoids the measurement error caused by the difference between the current sensors. Furthermore, good control properties are achieved. For the proposed reconfiguration strategy, the improved sliding mode control is used to effectively suppress the influence of motor parameter changes.

Description

technical field [0001] The invention relates to the technical field of permanent magnet synchronous motor control, in particular to a single current sensor predictive control and parameter disturbance suppression method for a permanent magnet synchronous motor based on a bus current sensor. Background technique [0002] At present, the main methods of permanent magnet synchronous motor control are vector control and direct torque control. These control methods need to realize closed-loop control, and the three-phase current of the motor needs to be collected for feedback to form a closed loop. The accurate collection of three-phase current is particularly important for these control strategies. The traditional three-phase current information acquisition is mainly completed through 2 to 4 current sensors, and the two-phase or three-phase current of the stator is directly collected to complete the control. However, the high-precision current sensor is expensive, which increase...

AI Technical Summary

Problems solved by technology

However, the high-precision current sensor is expensive, which increases the cost of the motor control system and the volume of the electronic control system, and the difference between the sensors brings errors to the current detection, which limits its further development to a certain extent. application

Traditional control strategies such as PI control and model predictive control all need to rely on the system model and motor parameters. When the parameters change, the control effect will drop rapidly. These control strategies relying on the motor model cannot meet the requirements of high-performance control in special occasions such as aerospace vehicles. requirements, limiting its scope of application

Both PI control and model predictive control need to use motor parameters to achieve their control requirements. There is an inherent problem of poor robustness of parameters. In view of the decrease in control performance caused by parameter disturbances, domestic and foreign scholars have proposed some control strategies to suppress disturbances. Common Suppression strategies include sliding mode variable structure control, adaptive control, fuzzy control, neural network, etc. Among them, sliding mode variable structure control has a simple structure, does not require high model accuracy, and has strong robustness to internal parameter disturbances and external disturbances. Its characteristics have been widely used in electronic control systems

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