Adaptive speed control based on just-in-time learning technique for permanent magnet synchronous linear motor

Abstract In this paper, an adaptive two degrees of freedom (2Dof) PI controller based on a just-in-time learning (JITL) method is proposed for predictive speed control of permanent magnet synchronous linear motor (PMSLM). Firstly, to guarantee the high identification accuracy and high real-time performance simultaneously, an improved JITL method is proposed to estimate the controlled model parameters of speed control system. Then, based on the dynamic controlled model, a simplified generalized predictive control (GPC) supplies a 2Dof proportional integral (PI) controller with suitable control parameters to follow a sinusoid-type speed command in operating conditions. The main motivation of this paper is the extension of the predictive controller to replace traditional PI controller in industrial applications. Finally, the efficacy and usefulness of the proposed controller are verified through the experimental results.

[1]  Y.A.-R.I. Mohamed.  Adaptive Self-Tuning Speed Control for Permanent-Magnet Synchronous Motor Drive With Dead Time , 2006, IEEE Transactions on Energy Conversion.

[2]  T. Oki,et al.  A design of neural-net based predictive PID controllers , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[3]  Shiqi Zheng,et al.  Gpc‐Based Self‐Tuning PI Controller for Speed Servo System of PMSLM , 2013 .

[4]  Xin Yang,et al.  Adaptive decentralized PID controllers design using JITL modeling methodology , 2012 .

[5]  Bruno Allard,et al.  A Comparative Study of Predictive Current Control Schemes for a Permanent-Magnet Synchronous Machine Drive , 2009, IEEE Transactions on Industrial Electronics.

[6]  M. Chiu,et al.  A new data-based methodology for nonlinear process modeling , 2004 .

[7]  Sang-Min Kim,et al.  Induction motor servo drive using robust PID-like neuro-fuzzy controller , 2006 .

[8]  Guillaume Krebs,et al.  Modeling and control of a 2Dof PM synchronous actuator using Hall effect sensors , 2010 .

[9]  Ramon Vilanova,et al.  Model-reference robust tuning of 2DoF PI controllers for first- and second-order plus dead-time controlled processes , 2012 .

[10]  Paolo Rocco,et al.  Force Ripple Compensation in Linear Motors Based on Closed-Loop Position-Dependent Identification , 2010, IEEE/ASME Transactions on Mechatronics.

[11]  Silverio Bolognani,et al.  Design and Implementation of Model Predictive Control for Electrical Motor Drives , 2009, IEEE Transactions on Industrial Electronics.

[12]  Saptarshi Das,et al.  Tuning of an optimal fuzzy PID controller with stochastic algorithms for networked control systems with random time delay. , 2011, ISA transactions.

[13]  Min-Sen Chiu,et al.  Partitioned model-based IMC design using JITL modeling technique , 2007 .

[14]  Min-Sen Chiu,et al.  Adaptive generalized predictive control based on JITL technique , 2009 .

[15]  Marco Tursini,et al.  Real-time gain tuning of PI controllers for high-performance PMSM drives , 2002 .

[16]  Chun-Jung Chen,et al.  Motion control for a two-wheeled vehicle using a self-tuning PID controller , 2008 .

[17]  M. R. Katebi,et al.  Predictive PID control: a new algorithm , 2001, IECON'01. 27th Annual Conference of the IEEE Industrial Electronics Society (Cat. No.37243).

[18]  Shihua Li,et al.  Speed Control for PMSM Servo System Using Predictive Functional Control and Extended State Observer , 2012, IEEE Transactions on Industrial Electronics.

[19]  T. Sato,et al.  Design of a GPC-based PID controller for controlling a weigh feeder , 2010 .

[20]  A. Visioli,et al.  A two-degree-of-freedom PI controller based on events , 2011 .

[21]  David W. Clarke,et al.  Generalized predictive control - Part I. The basic algorithm , 1987, Autom..

[22]  Toru Yamamoto,et al.  Design of a Data-Driven PID Controller , 2009, IEEE Transactions on Control Systems Technology.

[23]  M. Birattari,et al.  Lazy learning for local modelling and control design , 1999 .

[24]  Marko Bacic,et al.  Model predictive control , 2003 .

[25]  Toru Yamamoto,et al.  Improvement of tracking performance in designing a GPC‐based PID controller using a time‐varying proportional gain , 2006 .