To solve the problem of the sensorless control method of a permanent magnet synchronous motor, based on the study of a mathematical model for a permanent magnet synchronous motor and model adaptation theory, a reference model equation and adjustable model equation are derived according to the stator current equation. The correctness of the selected linear compensator matrix is strictly proved. Then, Popov’s super-stability theory is used to derive the speed adaptive law and prove its asymptotic stability. Based on the voltage closed-loop feedback MTPA weak magnetic control strategy, a simulation model of a MRAS control system based on stator current is built and combined with the principle of MRAS. Aiming to investigate the problem that the PI adaptive law in the traditional MRAS algorithm is not robust, super-twisting sliding mode control is introduced to replace the PI adaptive law. The observer based on STSM−MRAS is designed. The simulation model of the MRAS control system based on the super-twisting sliding mode is established. Under certain working conditions, the STSM−MRAS algorithm and the traditional MRAS algorithm are simulated and compared. The results show that the STSM−MRAS algorithm can improve the steady-state performance and robustness of a sensorless control system.

/pdf/adaptive-control-method-of-sensorless-permanent-magnet-1077bs4g.pdf

Adaptive Control Method of Sensorless Permanent Magnet Synchronous Motor Based on Super-Twisting Sliding Mode Algorithm

In the real applications, we found that it is difficult to achieve good control performance through manually tuning proportional–integral (PI) parameters of phase locked loop (PLL) and speed-loop of Luenberger observer (LO) for the PMSM sensorless control system. Therefore, this paper is to use the particle swarm optimization (PSO) algorithm to optimize the PI parameters of PLL and speed-loop of Luenberger observer of the system. Firstly, the ranges of PLL parameters are obtained by analyzing the PLL subsystem stability. Then, the ranges of PI parameters of PLL and speed-loop are set based on theoretical estimation and empirical values. The control system model is realized in MATLAB/Simulink that considers the constraints such as the saturation. The integral time absolute error is the objective function, and the PSO with different topologies is used to optimize the PI parameters. The simulation and experimental results show that the proposed method is feasible, and the optimized parameters can effectively improve the precision of position estimation and speed estimation. Moreover, the simulations and experiments are carried out to verify the robustness of the proposed method, and the results show that the optimized system can achieve good performance when there are uncertainties or disturbances.

https://downloads.hindawi.com/journals/mse/2022/3328719.pdf

Optimized Luenberger Observer-Based PMSM Sensorless Control by PSO

In order to save the environment and produce green electricity, wind energy has become one of the most appropriated renewable energies (REs). Actually, the large-scale wind turbines are more expensive compared to others REs systems. Therefore, the main objective of this research is to produce an economical technology for the wind power market offering low-cost of a direct drive wind turbine. In this paper, an optimization procedure is developed in order to reduce the cost of PMSG integrated in a 1.5 MW wind turbine. So, evolutionary optimisation methods have become a very popular technique for solving optimization problems in REs systems notably wind power conversion systems. Among the many proposed methods, Particle Swarm Optimization (PSO) is very similar and popular so it is used under the Software MATLAB to find the minimum cost value.

Application of PSO in the Design of Permanent Magnet Synchronous Generator for 1.5MW wind turbine

In order to save the environment and produce green electricity, wind energy has become one of the most appropriated renewable energies (REs). Actually, the large-scale wind turbines are more expensive compared to others REs systems. Therefore, the main objective of this research is to produce an economical technology for the wind power market offering low-cost of a direct drive wind turbine. In this paper, an optimization procedure is developed in order to reduce the cost of PMSG integrated in a 1.5 MW wind turbine. So, evolutionary optimisation methods have become a very popular technique for solving optimization problems in REs systems notably wind power conversion systems. Among the many proposed methods, Particle Swarm Optimization (PSO) is very similar and popular so it is used under the Software MATLAB to find the minimum cost value. 

Optimum 2-DOF PI Controller Design in PMSM Using Improved RAO Algorithm

https://robots.iopscience.iop.org/article/10.1088/1757-899X/717/1/012021/pdf

Design of PI Controller for PMSM using Chaos Particle Swarm Optimization Algorithm

This work proposes a way to achieve the sensorless control of permanent-magnet synchronous motor using sliding mode controller (SMC) and Sliding Mode Observer(SMO). In this Paper, the SMC is designed to replace the speed control loop PI controller from the original permanent magnet synchronous motor control system with the sliding mode variable structure control theory. Combined with the modified SMO, the "SMC and modified SMO" motor control system is proposed. Compared with the "PI and modified SMO" mode, the performance of the motor control is increased substantially, the interference torque speed attenuation is decreased by 87.5%, and the speed control error was also reduced by 67.7%. There is no overshoot with the reaction speed, and significant improvement in response speed, anti-interference performance, control accuracy and observation precision are achieved. Overall, both the system control and observation performance are significantly improved.

The Research on Sensorless Control of Permanent Magnet Synchronous using SMC and SMO

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Design of PI Controller for PMSM using Chaos Particle Swarm Optimization Algorithm

The Research on Sensorless Control of Permanent Magnet Synchronous using SMC and SMO