Abstract
The advantages of Permanent Magnet Synchronous Motor (PMSM) are high efficiency, high power density, small size, and so on. With the development of science and technology, customers ask for higher quality productions and many products put forward higher requirements for motor control and energy efficiency, which causes the widespread adoption of PMSM in industries like home appliance, industrial engineering, auto industry and so on. PMSM mainly adopts the Field Oriented Control (FOC) technology. In the FOC system, it is necessary to obtain the information of rotor position and speed. In order to obtain the rotor position, the position sensors (such as Hall sensor, photoelectric encoder, rotary converter, etc.) are used to detect the rotor position of the motor. However, the existence of the sensor increases the complexity and cost of control systems, and reduces the reliability of the system. In order to solve the series of problems brought by position sensors, this research used the Luenberger observer and sliding mode observer respectively to study the PMSM sensorless control. However, it is hard to achieve good control performance by manual adjustment since the PMSM sensorless control systems include several control parameters. To achieve good control performance of PMSM sensorless control system, in this research, Particle swarm optimization (PSO) algorithm is used to optimize PI parameters of PLL and speed-loop in PMSM sensorless control based on Luenberger observer and PMSM sensorless control based on sliding mode observer.
Firstly, the method of PMSM sensorless control is investigated, and the core PI parameters to be optimized are chosen. Set the range of PI paraments of PLL and speed-loop based on theoretical estimation and empirical value. We calculated the optimal KP and KI values of PLL and speed-loop by PSO online simulation. Secondly, the optimal KP and KI parameters are applied to the PMSM sensorless control based on Luenberger observer for simulation analysis and comparative analysis. The simulation results show that the PSO algorithm can effectively reduce the design difficulty and calculation error of PLL and speed loop. At same time, it can improve the position and speed estimation accuracy, speed control accuracy, and dynamic response capability of the PMSM sensorless control. Finally, in order to verify the performance for proposed method in different PMSM sensorless control system, the PSO algorithm is used to optimize the PMSM sensorless control based on sliding mode observer. Also, the simulation result is positive, and it is almost same as the simulation result of PMSM sensorless control based on Luenberger observer.
In order to verify the actual operation of the system, we designed a PMSM sensorless control system based on the Luenberger observer, and conducted physical experiments. The experimental results are consistent with the simulation results. Therefore, the results of simulation and experiment prove that the proposed PSO algorithm to optimize PMSM sensorless control is feasible, and the performance is significantly improved, and has good robust performance.
Key Words：Permanent Synchronous Motor (PMSM), Luenberger Observer, Sliding mode observer, Phase Locked Loop, Sensorless Control，Particle Swarm Optimisation (PSO).