Han Ho Choi, Member, IEEE, Nga Thi-Tuy Vu, and Jin-Woo Jung IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 59, NO. 8, AUGUST 2012,pp /9/241 Student: 高永發

Outline  ABSTRACT  MODEL DESCRIPTION  FUZZY SPEED REGULATOR DESIGN  SPEED REGULATOR PERFORMANCE DESIGN  LOAD TORQUE OBSERVER DESIGN  DESIGN ALGORITHM  SIMULATION AND EXPERIMENT  CONCLUSION 2016/9/242

ABSTRACT In this paper, a fuzzy speed regulator, as well as a fuzzy load torque observer, for a permanent magnet synchronous motor (PMSM) is designed based on the Takagi–Sugeno fuzzy approach. In terms of linear matrix inequalities (LMIs), sufficient conditions for the existence of the regulator and the observer are derived. LMI parameterizations of the gain matrices are given. LMI conditions for the existence of the fuzzy speed regulator and the fuzzy load torque observer guaranteeing various performance criteria are also derived. The proposed load-torque-observerbased fuzzy speed regulator system is implemented by using a TMS320F28335 floating-point digital signal processor, and simulation and experimental results are given to verify that the proposed method can be successfully used to control a PMSM under model parameter and load torque variations. 2016/9/243

MODEL DESCRIPTION A surface-mounted PMSM can be represented by the following nonlinear equation: where T L represents the load torque, ω is the electrical-rotor angular speed, i qs is the q-axis current, V qs is the q-axis voltage,ids is the d-axis current, V ds is the d- axis voltage, and k i > 0,i = 1,..., 6, are the parameter values depending on the stator resistance, the stator inductance, the rotor inertia, the viscous friction coefficient, and the magnetic flux. 2016/9/244

The ith rule of the T–S fuzzy model is of the following form: where F i (i = 1,..., r) values denote the fuzzy sets, r is the number of fuzzy rule, (I qi, I di ) is the ith operating point, and i s = [i qs, i ds ]T. Each fuzzy set F i is characterized by a membership function m i (i s ) and the ith operating point (i qs, i ds ) =(I qi, I di ). 2016/9/245

By using a standard fuzzy inference method, the following global nonlinear model can be obtained: 2016/9/246

FUZZY SPEED REGULATOR DESIGN Fuzzy model can be transformed into: 2016/9/247

Let the local speed regulator be given by the following linear controller: where x = [˜ω,˜i qs, i ds ]T, u qdf = [u qf, u df ]T, and K i ∈ R 2×3 are gain matrices. 2016/9/248

The final fuzzy speed regulator inferred as the weighted average of the each local controller is given by 2016/9/249

The closed-loop control system is given by 2016/9/2410

SPEED REGULATOR PERFORMANCE DESIGN  α-Stability  Quadratic Performance  Generalized H 2 Performance  Generalized H ∞ Performance 2016/9/2411

LOAD TORQUE OBSERVER PERFORMANCE DESIGN  α-Stability  Quadratic Performance  Generalized H 2 Performance  Generalized H ∞ Performance 2016/9/2412

DESIGN ALGORITHM 2016/9/2413

SIMULATION AND EXPERIMENT 2016/9/2414

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CONCLUSION Based on the T–S fuzzy approach, a load-torque-observerbased control design method has been proposed for a PMSM under model parameter and load torque variations. LMI existence conditions guaranteeing α- stability, quadratic performance,and H 2 /H ∞ performance bound have been derived for designing the fuzzy speed regulator and the load torque observer. The proposed fuzzy control system has been implemented by using a TMS320F28335 floating-point DSP. Finally,some simulation and experimental results have been given to show the effectiveness of the proposed design method. 2016/9/2418