Adaptive two-degree-of-freedom PI for speed control of permanent magnet synchronous motor based on fractional order GPC. (September 2016)

• Record Type: Journal Article
• Authors: Qiao, Wenjun; Tang, Xiaoqi; Zheng, Shiqi; Xie, Yuanlong; Song, Bao
• Is Part Of: ISA transactions. Volume 64(2016:Sep.)
• Page Start: 303
• Page End: 313
• Publication Date: 2016-09
• Subjects: Permanent magnet synchronous motor -- Two-degree-of-freedom PI -- Just-in-time learning -- Fractional order generalized predictive control -- Adaptive speed control; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2016.06.008
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: In this paper, an adaptive two-degree-of-freedom (2Dof) proportional-integral (PI) controller is proposed for the speed control of permanent magnet synchronous motor (PMSM). Firstly, an enhanced just-in-time learning technique consisting of two novel searching engines is presented to identify the model of the speed control system in a real-time manner. Secondly, a general formula is given to predict the future speed reference which is unavailable at the interval of two bus-communication cycles. Thirdly, the fractional order generalized predictive control (FOGPC) is introduced to improve the control performance of the servo drive system. Based on the identified model parameters and predicted speed reference, the optimal control law of FOGPC is derived. Finally, the designed 2Dof PI controller is auto-tuned by matching with the optimal control law. Simulations and real-time experimental results on the servo drive system of PMSM are provided to illustrate the effectiveness of the proposed strategy. Highlights: An adaptive strategy based on fractional order GPC for 2Dof PI controller is studied The just-in-time learning technique is enhanced by two novel searching engines A general formula is proposed for predicting future speed reference Fractional order GPC is introduced to replace GPC to provide optimal control law Simulation and Experimental results show the superiority of the proposed strategy