Single‐loop model prediction control of PMSM with moment of inertia identification

Liu, Fengyang; Erliang, Kang; Cui, Naizheng

doi:10.1002/tee.23091

Cited by 6 publications

(5 citation statements)

References 24 publications

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“…[15] presented a moment of inertia (MOI) identification method based on the improved model-reference adaptive system that uses a dynamic gain and a curvature model, which not only decreases the identification error caused by the load torque but also ensures fast convergence speed and high identification accuracy. Liu [16] compared the effects of various parameters on the running stability of the motor, and a least-squares identification method based on the forgetting factor is added to identify the parameter online to optimize the prediction model in real time. Kim [17] proposed a new method of servo system parameter identification, in which the MOI, Coulomb friction torque, and viscous coefficient of the servo device can be obtained from the half-cycle integration of the extremely low-frequency sinusoidal torque command.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Multi-Parameter Estimation of Inertial Stabilization Platform with Unknown Load

Zheng

Xie

et al. 2023

Actuators

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In order to improve the state monitoring and adaptive control capability of inertial stabilization platforms (ISPs) with unknown loads, it is necessary to estimate the dynamic parameters comprehensively online. However, most current online estimation methods regard the system as a linear dual-inertia model which neglects the backlash and nonlinear friction torque. It reduces the accuracy of the model and leads to incomplete and low accuracy of the estimated parameters. The purpose of this research is to achieve a comprehensive and accurate online estimation of multiple parameters of ISPs and lay a foundation for state monitoring and adaptive control of ISPs. First, a dual-inertia model containing backlash and nonlinear friction torque of the motor and load is established. Then, the auto-regressive moving average (ARMA) model of the motor and load is established by the forward Euler method, which clearly expresses the online identification formula of the parameters. On this basis, the adaptive identification method based on the recursive extended least squares (RELS) algorithm is used to realize the online estimation of multiple parameters. The simulation and experimental results show that the proposed adaptive multi-parameter estimation method can realize the simultaneous online identification of the moment of inertia of the load, the damping coefficient of motor and load, the transmission stiffness, the Coulomb friction torque of motor and load, and the backlash, and the steady-state error is less than 10%. Compared with the traditional linear dual-inertia model, the similarity between the model based on the proposed adaptive parameter estimation algorithm and the actual system is increased by 65.3%.

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Section: Introductionmentioning

confidence: 99%

Adaptive Multi-Parameter Estimation of Inertial Stabilization Platform with Unknown Load

Zheng

Xie

et al. 2023

Actuators

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“…e performance objective function is used to determine the optimal control vector by minimizing the error between the actual response vector and the expected vector [4][5][6][7][8][9]. A single-loop MPC and moment of inertia recognition based on the forgetting factor method, which has better dynamic performance than traditional MPC, is proposed in literature [10]. Given that the current prediction method has better steady-state performance while maintaining good dynamic characteristics, a current prediction method which operates by observing the back electromotive force is proposed in the literature, which is estimated by the historical stator voltage [11].…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control of Permanent Magnet Synchronous Motor Based on State Transition Constraint Method

Gao

Zhang

Fan

et al. 2021

Mathematical Problems in Engineering

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Permanent magnet synchronous motors are widely used and have sufficient development prospects in the drive systems of electric vehicles. Traditional model predictive control (MPC) methods are shown to achieve good control performance by tracking the d- and q-axis current as well as limiting the current amplitude. However, the dynamic response performance and current harmonics during the switching process are not considered in the traditional MPC. Therefore, this paper proposes an MPC that can effectively improve control performance, where the switch transfer sequence in the switch constraint module is considered in the improved model. The state transition error is obtained from the switch constraint module according to the current switch state and the transition probability, after which, the integration into the cost function in which the driving error, tracking error, and constraint error are considered. A reinforcement learning (RL) algorithm is used to obtain the weight coefficient of the transition error term in the constraint module for automatically determining the best switch state for the next control period using the cost function. Simulation tests show that the total harmonic distortion of the phase current based on the improved MPC is 978.4%, less than 2843.0% of the traditional MPC method under 20 Nm at 1000 rpm. The torque response time of the motor is reduced by 0.026 s, whereas the simulation results indicate that the 100 km acceleration performance of an electric vehicle is improved by 9.9%.

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“…Among the uncertainties of the system, the moment of inertia and viscous friction coefficient are the key factors to determine the control performance of the system. Among the traditional parameter identification methods, the common ones are sine signal integration method [19,20], steady-state direct method [21,22], least square method with forgetting factor [23]. There is a significant problem in these methods, that is, they need to have specific conditions for speed and load torque.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Backstepping Nonsingular Terminal Sliding Mode Control of Servo System Based on New Sliding Mode and Reaching Law

Zhu

Wang

Zhu

2021

Preprint

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Aiming at the problem that the control accuracy of permanent magnet synchronous motor (PMSM) servo system is easily affected by parameter uncertainty, an adaptive backstepping nonlinear nonsingular terminal sliding mode control (ABNNTSMC) method is proposed in this paper. Based on the existing fast nonsingular terminal sliding mode, a new piecewise nonlinear nonsingular terminal sliding mode is proposed to improve the convergence speed and ensure the steady-state accuracy. At the same time, a new reaching law with attenuation term is proposed to reduce the vibration gradually. Finally, the adaptive law is used to estimate the moment of inertia and viscous friction coefficient of the system to improve the robustness of the system. Simulation results show that ABNNTSMC has higher steady-state accuracy and smaller vibration compared with several existing results.

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Single‐loop model prediction control of PMSM with moment of inertia identification

Cited by 6 publications

References 24 publications

Adaptive Multi-Parameter Estimation of Inertial Stabilization Platform with Unknown Load

Adaptive Multi-Parameter Estimation of Inertial Stabilization Platform with Unknown Load

Model Predictive Control of Permanent Magnet Synchronous Motor Based on State Transition Constraint Method

Adaptive Backstepping Nonsingular Terminal Sliding Mode Control of Servo System Based on New Sliding Mode and Reaching Law

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