Flux Immunity Robust Predictive Current Control With Incremental Model and Extended State Observer for PMSM Drive

Yang, Ming; Lang, Xiaoyu; Long, Jinlin; Xu, Dianguo

doi:10.1109/tpel.2017.2654540

Cited by 176 publications

(82 citation statements)

References 37 publications

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“…Therefore, the increment of d ( k ) can be expressed as Equation .

normalΔ boldd ((), k) = ([], \begin{array}{c} Ψ_{r} ((), k) - Ψ_{r} ((), k - 1) \\ ω_{r} ((), k) Ψ_{r} ((), k) - ω_{r} ((), k - 1) Ψ_{r} ((), k - 1) \end{array}) \approx ([], \begin{array}{c} 0 \\ 0 \end{array})

Therefore, rotor flux and back EMF in Equation can be eliminated, so that the effects of rotor flux and back EMF can be greatly reduced . Thus, the incremental model can be simplified as Equation .…”

Section: Incremental Model Of Immentioning

confidence: 99%

“…Therefore, rotor flux and back EMF in Equation (4) can be eliminated, so that the effects of rotor flux and back EMF can be greatly reduced. 13 Thus, the incremental model (5) can be simplified as Equation (8).…”

Section: Incremental Model Of Immentioning

confidence: 99%

“…Incremental model is another way to achieve offset‐free prediction, which can decrease the effects of the errors of parameters greatly. Then, the robustness of the model prediction can be improved . However, larger prediction noise will be produced by incremental model than real value model, which should be analyzed and improved.…”

Section: Introductionmentioning

confidence: 99%

“…Then, the robustness of the model prediction can be improved. 13 However, larger prediction noise will be produced by incremental model than real value model, which should be analyzed and improved.…”

mentioning

confidence: 99%

See 3 more Smart Citations

An offset‐free robust model predictive control with incremental model and improved current observer for induction motor

Wang

Zheng

Sun

2019

Int Trans Electr Energ Syst

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Summary Model predictive control (MPC) is becoming a popular topic in the control of AC motor and power electronic converters because of its high dynamic performance and multitarget optimization ability. However, steady‐state error still exists in conventional MPC method because of unavoidable errors of model parameters. In addition, time delay in digital control also affects the performance, and even stability of MPC. In this paper, an offset‐free robust model predictive control (OFR‐MPC) method is proposed for the current control of three‐phase induction motor (IM). An incremental model is adopted to improve the parameter robustness and to realize offset‐free prediction. An improved current observer is designed to realize time‐delay compensation in control variable optimization. In addition, the improved current observer is also acting as a filter to solve the problem of noise amplification effect of the incremental model. The proposed method can also be applied to other AC motors and power electronic converters. Simulations and experiments have been implemented to verify the performance of the proposed OFR‐MPC method.

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“…Therefore, the increment of d ( k ) can be expressed as Equation .

normalΔ boldd ((), k) = ([], \begin{array}{c} Ψ_{r} ((), k) - Ψ_{r} ((), k - 1) \\ ω_{r} ((), k) Ψ_{r} ((), k) - ω_{r} ((), k - 1) Ψ_{r} ((), k - 1) \end{array}) \approx ([], \begin{array}{c} 0 \\ 0 \end{array})

Section: Incremental Model Of Immentioning

confidence: 99%

Section: Incremental Model Of Immentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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An offset‐free robust model predictive control with incremental model and improved current observer for induction motor

Wang

Zheng

Sun

2019

Int Trans Electr Energ Syst

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“…Such parameter mismatches can bring different degrees of prediction error, which may further lead to wrong selection of optimal voltage vector and deteriorate system control performance. Many published papers have proposed methods to solve this problem, including designing model-free prediction algorithm [19,20], identifying parameters online [9,21,22], and constructing observers to estimate the influence of parameter deviations [23][24][25]. However, the model-free predictive control poses relatively high requirements for hardware implementation of the drive system, while the latter two categories are prone to making control algorithms complex, which may be not suitable for the real-time implementation and may even result in instability of the system.…”

Section: Introductionmentioning

confidence: 99%

Prediction Error Analysis of Finite-Control-Set Model Predictive Current Control for IPMSMs

Huang

Niu

et al. 2018

Energies

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Finite-control-set model predictive current control (FCS-MPCC) has been widely investigated in the field of motor control. When the discrete motor prediction model is not obtained accurately, prediction error often occurs, which can result in improper determinations of optimal voltage vectors and can further affect the control performance of motor systems. However, papers evaluating the motor control performance employing FCS-MPCC rarely consider prediction error and its utilization to weaken the influence of inaccurate prediction model. This paper investigates in depth the prediction error caused by three influencing factors from the perspective of model accuracy-discretization method, prediction stepsize, and parameter mismatch. Firstly, the evaluation index, prediction error, is defined and its formulas considering the above three factors are derived based on interior permanent magnet synchronous motor (IPMSM). Then, the theoretical analysis of prediction error is provided. Finally, experimental results of an IPMSM drive system are presented to verify and complement the theoretical analysis. Both the theoretical analysis and experimental results fully elaborate the prediction error, which can offer practical guidelines for the evaluation and improvement of motor control performance, especially for FCS-MPCC in IPMSM applications.

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Adaptive model predictive control with extended state observer for multi‐UAV formation flight

Zhang

Sun

Deng

2020

Adaptive Control & Signal

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This article studies the adaptive model predictive control with extended state observers (ESO) to deal with multiple unmanned aerial vehicles formation flight in presence of external disturbances and system uncertainties. Specifically, to deal with the mismatch of predictive model caused by external disturbances and system uncertainties, ESOs are introduced to estimate the lumped disturbances, where the ultimately bounded property of observer system can be guaranteed by using the Lyapunov stability theorem. With these observations, the distributed adaptive model predictive controller is designed to achieve trajectory tracking and disturbance rejection simultaneously for multiple unmanned aerial vehicles, as well as taking the state and input saturation into account. Moreover, the stability of proposed model predictive controller is analyzed. Finally, the simulation examples are provided to illustrate the validity of the proposed control scheme.

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Flux Immunity Robust Predictive Current Control With Incremental Model and Extended State Observer for PMSM Drive

Cited by 176 publications

References 37 publications

An offset‐free robust model predictive control with incremental model and improved current observer for induction motor

An offset‐free robust model predictive control with incremental model and improved current observer for induction motor

Prediction Error Analysis of Finite-Control-Set Model Predictive Current Control for IPMSMs

Adaptive model predictive control with extended state observer for multi‐UAV formation flight

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