Model Predictive Control Based on Parametric Disturbance Compensation

Xu, Lingliang; Chen, Guiming; Li, Guangshuai; Li, Qiaoyang

doi:10.1155/2020/9543928

Cited by 5 publications

(5 citation statements)

References 27 publications

(25 reference statements)

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“…Applying the Rolls–Helvitz criterion, we substitute

z = \frac{λ + 1}{λ - 1}

to obtain the characteristic equation of the λ domain [25]:

β_{02} T_{s} λ^{2} + 2 (β_{01} - β_{02} T_{s}) λ + (4 + β_{02} T_{s} - β_{01}) = 0

…”

Section: Mfspc_eso Methods Of Design In Speed Loopmentioning

confidence: 99%

Cascaded speed and current model of PMSM with ultra‐local model‐free predictive control

Chen

2021

IET Electric Power Appl

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During the operation of a permanent magnet synchronous motor (PMSM), the control system is impacted by changes in the internal parameters and by external disturbances, resulting in reduced accuracy. The robustness and anti-interference ability of a PMSM can be improved using a speed loop-current loop integrated predictive control method. First, a PMSM mathematical model is constructed considering parameter changes and external disturbances. Second, using only the input and output of the speed loop and the current loop, without involving any motor parameters, an ultra-local model-free predictive control model of the speed loop and the current loop is built; this model has a lower sensitivity to parameter changes. Finally, to improve the tracking accuracy for speed and current, an extended state observer (ESO) and a disturbance observation compensator (DOC) are established to observe the total disturbance of system in real time and perform feedforward compensation. The experimental results show that the control model has strong robustness and anti-interference ability, high stability, high speed, and capable current-tracking performance.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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“…Applying the Rolls–Helvitz criterion, we substitute

z = \frac{λ + 1}{λ - 1}

to obtain the characteristic equation of the λ domain [25]:

β_{02} T_{s} λ^{2} + 2 (β_{01} - β_{02} T_{s}) λ + (4 + β_{02} T_{s} - β_{01}) = 0

…”

Section: Mfspc_eso Methods Of Design In Speed Loopmentioning

confidence: 99%

Cascaded speed and current model of PMSM with ultra‐local model‐free predictive control

Chen

2021

IET Electric Power Appl

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“…The Model predictive control (MPC) is the most popular and has been extensively implemented in BLDC motors due to its simple control architecture and good improved output [197]. It is a feedback control technique that employs a system to predict future outputs of a motor drive in an EV in order to manage complex dynamics, handle multiple input and multiple output systems, and deal with constraints in a systematic manner [198].…”

Section: )Torque Ripples Mitigation Based On Model Predictive Control...mentioning

confidence: 99%

Critical Review on Torque Ripple Sources and Mitigation Control Strategies of BLDC Motors in Electric Vehicle Applications

Prabhu,

Thirumalaivasan,

Ashok

2023

IEEE Access

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In recent years, electric vehicles (EVs) have attracted a lot of attention and are rapidly growing trends today due to their ability to compete with and overtake fossil fuel-powered vehicles among all electrified transportation tools. The primary focus of EV evolution is on improving driving range and optimizing energy consumption, both of which are essential demands achieved through improved and developing motor performance. The brushless direct current (BLDC) motor is one of the most superior choices for dynamic applications in EVs, providing the highest power density and capability of any motor. BLDCs are gaining popularity in EVs not only for their high performance in speed and position controls but also for meeting the requirements of smooth torque maintenance with torque ripple mitigation. This paper provides an overview of various advanced control strategies, such as field-oriented control (FOC), direct torque control (DTC), intelligent control (IC), controlling input voltage (CIV), current shaping techniques (CST), model predictive control (MPC), and sliding mode control (SMC). These strategies are used to regulate the torque ripples produced in BLDC motors, aiming to achieve energy-efficient EV performance for various applications. Additionally, it describes a cloud-based control system coupled with electric motor drives to address challenges in predicting current vehicle conditions for improved power distribution between the motor and battery systems. Furthermore, recent concerns and difficulties in advancing torque ripple mitigation control strategies are highlighted, with comparisons and discussions for future EV research. Finally, an evaluative study on BLDC motor drive in EVs with different control strategies reveals its significance and potential outcomes. INDEX TERMS Brushless Direct Current (BLDC) motor, Cloud based torque control, Control structural architecture, Electric Vehicles (EVs), Various control strategies for torque ripple mitigation.

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“…According to the Routh-Hurwitz stability criterion (Xu et al, 2020), the discrete system is stable when the following conditions are satisfied:…”

Section: Stability Analysismentioning

confidence: 99%

Extended state observer-based predictive control for soft open point

Wang

Lu²,

Zhou³

et al. 2023

Front. Energy Res.

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This paper proposes an extended state observer-based ultra-local model-free three-vector predictive control method for Soft Open Point (SOP). First, the Ultra-Local Model-Free Predictive Control (ULMFPC) method is proposed to improve the robustness of the system, which only uses the input and output of the outer-loop, and any other parameters are not involved. Second, considering parameter perturbations and external disturbances in the SOP system, an expansion state observer (ESO) is established to observe the SOP system’s total perturbations and the perturbations are compensated in real-time to improve the system. Third, to solve the problem of significant current harmonics in traditional model predictive control (MPC), a three-vector MPC method (TV-MPC) is adopted to reduce the total harmonic distortion rate (THD) of the current. Finally, it is verified by simulation that the proposed method can effectively reduce the current harmonics of the SOP system, rate value setting time, and improve the dynamic performance effectively. When perturbations occur in the system, the proposed method can improve the anti-interference and robustness of the system.

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Model Predictive Control Based on Parametric Disturbance Compensation

Cited by 5 publications

References 27 publications

Cascaded speed and current model of PMSM with ultra‐local model‐free predictive control

Cascaded speed and current model of PMSM with ultra‐local model‐free predictive control

Critical Review on Torque Ripple Sources and Mitigation Control Strategies of BLDC Motors in Electric Vehicle Applications

Extended state observer-based predictive control for soft open point

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