A Variable Prediction Horizon Self-Tuning Method for Nonlinear Model Predictive Speed Control on PMSM Rotor Position System

Wei, Yao; Gao, Yang; Qi, Hanhong; Guo, Xiaoqiang; Li, Mengyuan; Zhang, Di

doi:10.1109/access.2021.3084321

Cited by 14 publications

(4 citation statements)

References 32 publications

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“…The cost function is described as equation ( 40 It is the best model-based method for predicting the future behavior of a system over a specific time horizon. According to the predefined cost function, the optimal switching state is directly selected and applied during the next sampling period, to maintain a constant non-commutated stator phase current during commutation and efficiently minimize commutation torque ripples [200,201]. For a BLDC motor, the Model predictive controller provides each of the three phase primary voltages required to exactly follow the reference path, in order to ensure the future output error is equal to zero by minimizing the cost function value.…”

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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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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“…Each iteration will check the stability criteria and find out the minimum horizon of stability. Reference [23] modified the prediction horizon range to a positive integral discrete time variable, and introduced a NMPC to achieve velocity control, incorporating a self-correction method for the prediction horizons. Studies in [24,25] proposed eventtriggered MPC, which adjusted the prediction horizon with Event triggering to reduce the complexity of optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Prediction Horizon-varying Model Predictive Control (MPC)for Autonomous Vehicle Control

Chen,

Lai,

et al. 2024

Preprint

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The prediction horizon is a key parameter in model predictive control (MPC), related to the effectiveness and stability of model predictive control. In vehicle control, the selection of prediction horizon is influenced by factors such as speed, path curvature, and target point density. To accommodate varying conditions such as road curvature and vehicle speed, we proposed a control strategy using the Proximal Policy Optimization (PPO) algorithm to adjust the prediction horizon, enabling MPC to achieve optimal performance, and called it PPO-MPC. In this paper, we constructed the vehicle dynamics model and designed a basic model prediction control. We have established a state space related to the path information and vehicle state, regarded the prediction horizon as actions, and designed a reward function to optimize the policy and value function. We conducted simulation verifications at various speeds and compared the MPC of the fixed Prediction Horizon. The simulation demonstrates that the PPO-MPC proposed in this article exhibits strong trajectory tracking capability.

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“…Ref. [28] adjusted the prediction horizon range to a positive integral discrete time variable and introduced an NMPC to achieve velocity control, incorporating a self-correction method for the prediction horizons. Studies in refs.…”

Section: Introductionmentioning

confidence: 99%

Prediction Horizon-Varying Model Predictive Control (MPC) for Autonomous Vehicle Control

Chen,

Lai,

et al. 2024

Electronics

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The prediction horizon is a key parameter in model predictive control (MPC), which is related to the effectiveness and stability of model predictive control. In vehicle control, the selection of a prediction horizon is influenced by factors such as speed, path curvature, and target point density. To accommodate varying conditions such as road curvature and vehicle speed, we proposed a control strategy using the proximal policy optimization (PPO) algorithm to adjust the prediction horizon, enabling MPC to achieve optimal performance, and called it PPO-MPC. We established a state space related to the path information and vehicle state, regarded the prediction horizon as actions, and designed a reward function to optimize the policy and value function. We conducted simulation verifications at various speeds and compared them with an MPC with fixed prediction horizons. The simulation demonstrates that the PPO-MPC proposed in this article exhibits strong adaptability and trajectory tracking capability.

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A Variable Prediction Horizon Self-Tuning Method for Nonlinear Model Predictive Speed Control on PMSM Rotor Position System

Cited by 14 publications

References 32 publications

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

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

Prediction Horizon-varying Model Predictive Control (MPC)for Autonomous Vehicle Control

Prediction Horizon-Varying Model Predictive Control (MPC) for Autonomous Vehicle Control

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