Field Weakening Operation Control Strategies of PMSM Based on Feedback Linearization

Zhou, Kai; Ai, Min; Sun, Dongyang; Jin, Ningzhi; Wu, Xiaogang

doi:10.3390/en12234526

Cited by 32 publications

(22 citation statements)

References 36 publications

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“…wherein, ω r is the wind turbine angular speed. For a surface mounted PMSG, the expressions of stator winding voltage and flux linkage in d-q synchronous rotation coordinate are as follows [59]:…”

Section: Integer-order Nonlinear Mathematical Modelmentioning

confidence: 99%

Fuzzy Chaos Control of Fractional Order D-PMSG for Wind Turbine with Uncertain Parameters by State Feedback Design

Yang

Sheng

et al. 2021

Energies

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To research the chaotic motion problem of the direct-drive permanent magnet synchronous generator (D-PMSG) for a wind turbine with uncertain parameters and fractional order characteristics, a control strategy established upon fuzzy state feedback is proposed. Firstly, according to the working mechanism of D-PMSG, the Lorenz nonlinear mathematical model is established by affine transformation and time transformation. Secondly, fractional order nonlinear systems (FONSs) are transformed into linear sub-model by Takagi–Sugeno (T-S) fuzzy model. Then, the fuzzy state feedback controller is designed through Parallel Distributed Compensation (PDC) control principle to suppress the chaotic motion. By applying the fractional Lyapunov stability theory (FLST), the sufficient conditions for Mittag–Leffler stability are formulated in the format of linear matrix inequalities (LMIs). Finally, the control performance and effectiveness of the proposed controller are demonstrated through numerical simulations, and the chaotic motions in D-PMSG can be eliminated quickly.

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Section: Integer-order Nonlinear Mathematical Modelmentioning

confidence: 99%

Fuzzy Chaos Control of Fractional Order D-PMSG for Wind Turbine with Uncertain Parameters by State Feedback Design

Yang

Sheng

et al. 2021

Energies

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“…The analysis of two-region control leads to different control structures, but they are always vector methods in the (d, q) frame. A field-weakening operation based on feedback linearization is presented in [9]. A variable q-axis voltage control and voltage angle control (VAC) method is shown in [10].…”

Section: Introductionmentioning

confidence: 99%

“…In conclusion, we believe that the consideration of vehicle dynamics in the dynamic analysis of a closed-loop PMSM control system is not seen frequently in the literature [4][5][6][7][8][9][10][11][12][13][14][15][16] and would be valuable. In the quoted articles, as described above, the focus is on other problems of the propulsion system, e.g., there are static characteristics of currents, voltages, and torque or there are different motor control structures.…”

Section: Introductionmentioning

confidence: 99%

Design and Modelling of Energy Conversion with the Two-Region Torque Control of a PMSM in an EV Powertrain

Sieklucki

Kara

2022

Energies

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This paper investigates the properties and design of energy conversion in an electric vehicle (EV) powertrain. Here, we combined the dynamics of vehicle motion with controlled electric propulsion, which is an EV powertrain. The control of two types of permanent magnet synchronous motors (PMSMs) was considered. An algorithm was developed for the determination of the static characteristics of two-region motor torque control. A constant torque and a constant power region were used in the powertrain of the EV. The design of the control system for the PMSM was considered in the d,q reference frame. A precise mechanical model of the EV and the determination of road loads is shown. The main results of this study were the selection of the PI controller parameters (in analytical form), which was carried out for the simplified motor model and then extended for the d,q model, and energy consumption during the WLTP standard driving cycle. The presented simulation results of the proposed control system with synchronous motors in the EV (Fisker Karma as an example) confirmed the approach taken for the selection of the controller. The presentation of the EV’s acceleration for an optimized powertrain, and hence its performance, is a novelty not found in other articles.

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“…This structure leads to torque control of the synchronous motors. Some control methods such as model predictive control (MPC) [4], terminal sliding mode control [5,6], application of support vector machine in internal model control [7], adaptive control [8], input-output feedback linearization technique [9], and neural network control approach [10,11] are employed to control the speed and position of PMSMs.…”

Section: Introductionmentioning

confidence: 99%

PSO based Takagi-Sugeno fuzzy PID controller design for speed control of permanent magnet synchronous motor

Ghadiri

Khodadadi

Eijei³

et al. 2021

FACTA U EE

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A permanent magnet synchronous motor (PMSM) is one kind of popular motor. They are utilized in industrial applications because their abilities included operation at a constant speed, no need for an excitation current, no rotor losses, and small size. In the following paper, a fuzzy evolutionary algorithm is combined with a proportional-integral-derivative (PID) controller to control the speed of a PMSM. In this structure, to overcome the PMSM challenges, including nonlinear nature, cross-coupling, air gap flux, and cogging torque in operation, a Takagi-Sugeno fuzzy logic-PID (TSFL-PID) controller is designed. Additionally, the particle swarm optimization (PSO) algorithm is developed to optimize the membership functions' parameters and rule bases of the fuzzy logic PID controller. For evaluating the proposed controller's performance, the genetic algorithm (GA), as another evolutionary algorithm, is incorporated into the fuzzy PID controller. The results of the speed control of PMSM are compared. The obtained results demonstrate that although both controllers have excellent performance; however, the PSO based TSFL-PID controller indicates more superiority.

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Field Weakening Operation Control Strategies of PMSM Based on Feedback Linearization

Cited by 32 publications

References 36 publications

Fuzzy Chaos Control of Fractional Order D-PMSG for Wind Turbine with Uncertain Parameters by State Feedback Design

Fuzzy Chaos Control of Fractional Order D-PMSG for Wind Turbine with Uncertain Parameters by State Feedback Design

Design and Modelling of Energy Conversion with the Two-Region Torque Control of a PMSM in an EV Powertrain

PSO based Takagi-Sugeno fuzzy PID controller design for speed control of permanent magnet synchronous motor

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