Robust Mixed-Sensitivity Gain-Scheduled H∞ tracking control of a nonlinear Time-Varying IPMSM via a T-S fuzzy model

This article presents a robust Mixed-Sensitivity Gain-Scheduled H∞ controller based on the Loop-Shaping methodology for a class of MIMO uncertain nonlinear Time-Varying systems. In order to design this controller, the nonlinear parameter-dependent plant is first modeled as several linear sub systems by Takagi and Sugeno's (T-S) fuzzy approach. Both Loop-Shaping methodology and Mixed-Sensitivity problem are then introduced to formulate frequency-domain specifications, which will be used to devise a systematic design for choosing properly the weighting matrices. Furthermore, for each linear subsystem a H∞ controller is designed by using linear matrix inequality(LMI) approach. Such controllers are said to be scheduled by the Time-Varying parameter measurements in real time. The Parallel Distributed Compensation (PDC) is then used to design the controller for the overall system and the total linear system is also obtained by using the weighted sum of the local linear subsystems. Several results show that the proposed method can effectively meet the performance requirements like robustness, good load disturbance rejection and tracking responses, and fast transient responses for the 3-phase interior permanent magnet synchronous motor (IPMSM). Finally, the superiority of the proposed control scheme is approved in comparison with the feedback linearization controller, the H2/H∞ Controller and the H∞ Mixed-Sensitivity controller methods.

[1]  Cheng-Kai Lin,et al.  Adaptive backstepping PI sliding-mode control for interior permanent magnet synchronous motor drive systems , 2011, Proceedings of the 2011 American Control Conference.

[2]  S. S. Yang,et al.  Robust speed tracking of permanent magnet synchronous motor servo systems by equivalent disturbance attenuation , 2007 .

[3]  Zhizheng Wu,et al.  Mixed-Sensitivity $H_\infty$ Control of Magnetic-Fluid-Deformable Mirrors , 2010, IEEE/ASME Transactions on Mechatronics.

[4]  Tian-Hua Liu,et al.  Nonlinear position controller design with input-output linearisation technique for an interior permanent magnet synchronous motor control system , 2008 .

[5]  H. Shayeghi,et al.  A robust mixed H2/H∞ based LFC of a deregulated power system including SMES , 2008 .

[6]  Sing Kiong Nguang,et al.  Fuzzy Control and Filter Design for Uncertain Fuzzy Systems (Lecture Notes in Control and Information Sciences) , 2006 .

[7]  Yuxin Su,et al.  Automatic disturbances rejection controller for precise motion control of permanent-magnet synchronous motors , 2005, IEEE Transactions on Industrial Electronics.

[8]  Chang-Ming Liaw,et al.  Development of Robust Current 2-DOF Controllers for a Permanent Magnet Synchronous Motor Drive With Reaction Wheel Load , 2009, IEEE Transactions on Power Electronics.

[9]  Bor-Sen Chen,et al.  Robust Optimal Reference-Tracking Design Method for Stochastic Synthetic Biology Systems: T–S Fuzzy Approach , 2010, IEEE Transactions on Fuzzy Systems.

[10]  Alan J. Laub,et al.  The LMI control toolbox , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

[11]  Vahid Azimi,et al.  Robust multi-objective H2/H∞ tracking control based on the Takagi–Sugeno fuzzy model for a class of nonlinear uncertain drive systems , 2012, J. Syst. Control. Eng..

[12]  F. B. Amara,et al.  Mixed sensitivity H∞ control of magnetic fluid deformable mirrors , 2009, 2009 International Symposium on Optomechatronic Technologies.

[13]  Rong-Jong Wai,et al.  Adaptive Fuzzy Neural Network Control Design via a T–S Fuzzy Model for a Robot Manipulator Including Actuator Dynamics , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[14]  Petko H. Petkov,et al.  Robust control design with MATLAB , 2005 .

[15]  Manuel G. Ortega,et al.  Improved design of the weighting matrices for the S/KS/T mixed sensitivity problem-application to a multivariable thermodynamic system , 2006, IEEE Transactions on Control Systems Technology.