KEYWORDS: Control systems, Switching, Power grids, Switches, Power supplies, Design and modelling, Device simulation, Electromagnetism, Fuzzy logic, Control systems design
At present, the control of permanent magnet synchronous motor is mostly driven by single set of controller. When the controller loses power, the motor will stop running. To solve this problem, a double controller redundancy switching system for permanent magnet synchronous motor is designed in this paper. Firstly, a first-order nonlinear active disturbance rejection controller and a second-order linear active disturbance rejection controller are designed, and sliding mode control is added into the error feedback control law of the ADRC. The compound control of active disturbance rejection control and sliding mode control can effectively eliminate chattering of sliding mode control and improve the observation performance of active disturbance rejection control. In order to further improve the robustness of the system, fuzzy control is introduced to select only the value of parameter q in the sliding mode controller. Through MATLAB/Simulink simulation, it is verified that the improved sliding mode active disturbance rejection algorithm based on fuzzy control can effectively suppress the speed drop of permanent magnet synchronous motor when the bus loses power, and has the advantages of anti-bus voltage drop and anti-loop switching instantaneous interference. At the same time, the ability to resist sudden change load is also prominent in normal operation.
To address the issue of large torque ripple in traditional model predictive torque control for permanent magnet synchronous motors, we propose a torque-flux hysteresis-based model predictive voltage control approach. Firstly, a mathematical model is formulated in a two-phase rotating reference frame, and the computation process for the suggested model predictive voltage regulation technique grounded on the torque-flux hysteresis is delineated. To reduce the switching frequency, a spatial vector modulation technique and a hysteresis-based predictive torque-flux control are combined. To minimize the impact of parameter variations on model predictions, the least squares method is used for motor parameter identification. The simulation results indicate that the proposed method is capable of efficiently decreasing both switching losses and torque ripple, while simultaneously ensuring satisfactory system performance.
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