An Effective Model-Free Predictive Current Control for Synchronous Reluctance Motor Drives

Carlet, Paolo Gherardo; Tinazzi, Fabio; Bolognani, Silverio; Zigliotto, M.

doi:10.1109/tia.2019.2910494

Cited by 119 publications

(60 citation statements)

References 26 publications

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“…To this end, numerous approaches have been presented recently, like model-free predictive controllers. In this regard, authors of [24] similar to [25,26] present a model-free PC where the motor current variations, due to applying each eight base inverter voltage vectors, are measured and dynamically stored in a look-up table. Based on these measurements, the current variations are predicted.…”

Section: Literature Reviewmentioning

confidence: 99%

Model-Free Neural Network-Based Predictive Control for Robust Operation of Power Converters

et al. 2021

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An accurate definition of a system model significantly affects the performance of model-based control strategies, for example, model predictive control (MPC). In this paper, a model-free predictive control strategy is presented to mitigate all ramifications of the model’s uncertainties and parameter mismatch between the plant and controller for the control of power electronic converters in applications such as microgrids. A specific recurrent neural network structure called state-space neural network (ssNN) is proposed as a model-free current predictive control for a three-phase power converter. In this approach, NN weights are updated through particle swarm optimization (PSO) for faster convergence. After the training process, the proposed ssNN-PSO combined with the predictive controller using a performance criterion overcomes parameter variations in the physical system. A comparison has been carried out between the conventional MPC and the proposed model-free predictive control in different scenarios. The simulation results of the proposed control scheme exhibit more robustness compared to the conventional finite-control-set MPC.

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Section: Literature Reviewmentioning

confidence: 99%

Model-Free Neural Network-Based Predictive Control for Robust Operation of Power Converters

et al. 2021

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“…This problem can be alleviated by adopting disturbance observers to estimate the machine parameters [29], [30]. Meanwhile, the model free predictive control has also been presented to enhance the parameter robustness in [31], [32], where the knowledge of machine parameter is not required to predict the future behavior of the machine. The control scheme of the proposed method is illustrated in Figure 9.…”

Section: Cost Function Designmentioning

confidence: 99%

An Indirect Reference Vector-Based Model Predictive Control for a Three-Phase PMSM Motor

Niu

Luo

et al. 2020

IEEE Access

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Almost all of the existing reference vector based finite control set-model predictive control (FCS-MPC) methods for electric machines adopt the deadbeat control principle to directly obtain the reference vector. This paper proposes a computationally efficient control approach for a three phase permanent magnet synchronous motor (PMSM) based on indirect reference vector without using the deadbeat control. Instead of calculating a virtual reference vector in the traditional manner using deadbeat control, the reference vector is innovatively determined through two two-level bang-bang comparators. To improve the performance of the machine, the sampling period is subdivided into two equal time intervals and total 20 synthesized voltage vectors are obtained. Nevertheless, there is no need to evaluate all the 20 vectors by excluding the inappropriate vectors in advance using the reference vector based method, thus reducing the computation time. Moreover, with this proposed method, the complicated calculation of reference vector is avoided. Simulation and experimental results are presented to prove the effectiveness of the proposed method.

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“…In the last years, model predictive control (MPC) is perceived as a favorable alternative to those both control methods. MPC technique has been applied to numerous types of motors, comprehensive induction machines [8], permanent magnet synchronous machines (PMSMs) [9], interior PMSMs [10], brush-less DC motors [11], and synchronous reluctance motor [12].…”

Section: Introductionmentioning

confidence: 99%

Simplified Sensorless Current Predictive Control of Synchronous Reluctance Motor Using Online Parameter Estimation

et al. 2020

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In this paper, a simplified efficient method for sensorless finite set current predictive control (FSCPC) for synchronous reluctance motor (SynRM) based on extended Kalman filter (EKF) is proposed. The proposed FSCPC is based on reducing the computation burden of the conventional FSCPC by using the commanded reference currents to directly calculate the reference voltage vector (RVV). Therefore, the cost function is calculated for only three times and the necessity to test all possible voltage vectors will be avoided. For sensorless control, EKF is composed to estimate the position and speed of the rotor. Whereas the performance of the proposed FSCPC essentially necessitates the full knowledge of SynRM parameters and provides an insufficient response under the parameter mismatch between the controller and the motor, online parameter estimation based on EKF is combined in the proposed control strategy to estimate all parameters of the machine. Furthermore, for simplicity, the parameters of PI speed controller and initial values of EKF covariance matrices are tuned offline using Particle Swarm Optimization (PSO). To demonstrate the feasibility of the proposed control, it is implemented in MATLAB/Simulink and tested under different operating conditions. Simulation results show high robustness and reliability of the proposed drive.

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An Effective Model-Free Predictive Current Control for Synchronous Reluctance Motor Drives

Cited by 119 publications

References 26 publications

Model-Free Neural Network-Based Predictive Control for Robust Operation of Power Converters

Model-Free Neural Network-Based Predictive Control for Robust Operation of Power Converters

An Indirect Reference Vector-Based Model Predictive Control for a Three-Phase PMSM Motor

Simplified Sensorless Current Predictive Control of Synchronous Reluctance Motor Using Online Parameter Estimation

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