Control Strategies For Speed Control Of Permanent Magnet Synchronous Motors

Permanent magnet synchronous motors (PMSMs) are widely used in speed control systems since1980s due to their excellent features such as small size, high efficiency, high reliability and easy maintenance. As the applications of PMSM speed control systems are more diverse and complex than before, control algorithms used by speed control systems need improving to meet higher performance requirement. Therefore, an in-depth study on control algorithms of PMSM speed control systems is completed in this paper, which is expected to promote and expand the high-performance applications of PMSM speed control systems. The main contents are listed as follows.1. The research background is provided. Firstly, the history of control algorithms of AC speed control system is presented. Among the algorithms, fuzzy logic control (FLC), active disturbance rejection control (ADRC) and model prective control (MPC) are highlighted and their latest research results are surveyed. Secondly, the PMSM model and two most-used PMSM control systems are introduced. After that, the performance of the two control systems is compared.2. The working principle and features of zero voltage space vectors in PMSM fuzzy direct torque control (DTC) systems are analysed, based on which the rule set, torque fuzzy sets and voltage fuzzy sets of fuzzy DTC are improved to give full play to hold torque feature of zero voltage space vectors. The effectiveness of proposed algorithm is verified by simulations and experiments.3. ADRC adopts discrete time-optimal control synthesis (fhan) function that includes three adjustable parameters as the nonlinear state feedback controller (NLSEF). To simplify parameters tuning procedure, the control mechanism of NLSEF in ADRC is analyzed based on the characteristics of fhan function, and then a modified ADRC structure is proposed. In this structure, the meaning of parameters is clear and so need not turning. When time delay exists or more stability margin is required, tune the value of only one parameter a little larger than its calculated value can get satisfactory results. The effectiveness of proposed algorithm is verified by simulations and experiments.4. An optimization problem is required to solve online in MPC, which has heavy computational burden. On the other hand, the sample interval of PMSM control system is too short for a MPC algorithm. To apply MPC in PMSM control system, a recursive MPC (RMPC) algorithm is proposed in this thesis. In RMPC, iterative learning control (ILC) is used to obtain the first term of manipulated variables, and then recursive Levenberg Marquardt algorithm (RLMA) is adopted to solve the online optimization problem. In this control algorithm, the first term can be used as an effective control input, and the other terms guarantee a positive effect of the input to future behaviors of the system. Furthermore, the convergency of RLMA is analysed. Then, to testify that RMPC has less computational burden, time complexity of MPC and RMPC are computed. Simulation results show the effectiveness of proposed method.