Active Disturbance Rejection Control Based on Deep Reinforcement Learning of PMSM for More Electric Aircraft

Wang, Yicheng; Fang, Shuhua; Hu, Jianxiong

doi:10.1109/tpel.2022.3206089

Cited by 50 publications

(18 citation statements)

References 33 publications

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“…The control system's effectiveness is greatly impacted by the settings selected. Numerous academics have conducted thorough investigations on parameter tuning, and artificial intelligence algorithms offer a promising approach to address parameter optimization challenges in intricate nonlinear systems [26][27][28][29]. Ref.…”

Section: Introductionmentioning

confidence: 99%

Research on the Control System of Bearingless Induction Motor Based on Improved Active Disturbance Rejection Control

Wang,

Yang,

Sun

et al. 2024

PIER C

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To enhance the reaction speed, suspension performance, and anti-interference ability of a Bearingless Induction Motor (BIM) operation control system, an improved Active Disturbance Rejection Control (ADRC) technique is proposed. Firstly, the ADRC in the suspension system and the ADRC in the torque system are designed, respectively, using the BIM's mathematical model as the basis. Furthermore, the error integral signal is incorporated into the nonlinear state error feedback control law of the standard ADRC controller. Subsequently, a novel optimal control function is formulated using the fitting method, which is based on the original fal function. This approach effectively mitigates the impact of output signal fluctuations at the inflection point of the fal function. Simultaneously, the RBF neural network technique is employed to autonomously adjust the control parameters of the extended state observer, therefore enhancing the system's observation capability. Ultimately, the classic ADRC control strategy and the IADRC strategy are compared through simulation and experimentation. Simulations and experimental findings demonstrate that the suggested control method enhances the BIM control system's response time and resilience to external disturbance. Additionally, it enhances the levitation performance of the BIM system.

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Section: Introductionmentioning

confidence: 99%

Research on the Control System of Bearingless Induction Motor Based on Improved Active Disturbance Rejection Control

Wang,

Yang,

Sun

et al. 2024

PIER C

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“…In [6], receding-horizon optimization-based gain tuning of nonlinear DOB was proposed to balance *This work was supported by a Korea Institute for Advancement of Technology (KIAT) grant funded by the Korea Government (MOTIE) (P0020535, The Competency Development Program for Industry Specialist) 1 Kyunghwan Choi and Hyochan Lee are with the School of Mechanical Engineering, Gwangju Institute of Science and Technology, Gwangju 61005, Republic of Korea khchoi@gist.ac.kr; hyochanlee@gm.gist.ac.kr 2 Wooyong Kim is with the Department of Biomedical & Robotics Engineering, Incheon National University, Incheon 22012, Republic of Korea wooyongkim@inu.ac.kr disturbance estimation accuracy and noise suppression. DRL was utilized to optimize the gains of a nonlinear DOB and an active disturbance rejection controller in [7] and [8], respectively, to improve the disturbance rejection performance. A novel paradigm was presented in [9], where a DOB was designed by recurrent neural networks (RNNs) and DRL was utilized to optimize the RNNs for a target environment.…”

Section: Introductionmentioning

confidence: 99%

Using Deep Reinforcement Learning for Dynamic Gain Adjustment of a Disturbance Observer

Choi,

Lee,

Kim

2024

Preprint

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“…As the requirements for system stability, safety and anti-interference performance increase, the traditional PID control no longer meets these needs. Many scholars have done a lot of work on the improvement and optimization of PID based on traditional PID controllers, and have obtained some new control strategies with stronger anti-interference ability and better robustness [3][4]. At the same time, the emergence of modern control theory provides new ideas for improving the performance of the control system.…”

Section: Introductionmentioning

confidence: 99%

Sliding-mode Active Disturbance Rejection Permanent Magnet Synchronous Motor Control System

Chen

2024

JCEIM

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Aiming at the permanent magnet synchronous motor with external perturbations and internal uncertainties such as external load, rotational inertia and armature resistance changes, a servo control system based on sliding mode self-immunity controller is designed. By constructing a mathematical model of the servo system of the permanent magnet synchronous motor, considering the internal uncertainty caused by the change of the system parameters and the external random disturbance as the "total disturbance", and designing the transition process and the linear expansion state observer to observe and compensate for it, the system response can track the input signal quickly and without overshooting, and the sliding-mode state feedback makes the closed-loop servo system achieve fast and stable control, and the sliding-mode Active Disturbance Rejection control controller can be used to control the system. The sliding mode state feedback enables the closed-loop servo system to realize fast and stable control, and its consistent stability is proved by the Lyapunov method. Simulations show that this sliding- mode Active Disturbance Rejection control control strategy improves the system response speed and load carrying capability compared with the traditional sliding-mode control.

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Active Disturbance Rejection Control Based on Deep Reinforcement Learning of PMSM for More Electric Aircraft

Cited by 50 publications

References 33 publications

Research on the Control System of Bearingless Induction Motor Based on Improved Active Disturbance Rejection Control

Research on the Control System of Bearingless Induction Motor Based on Improved Active Disturbance Rejection Control

Using Deep Reinforcement Learning for Dynamic Gain Adjustment of a Disturbance Observer

Sliding-mode Active Disturbance Rejection Permanent Magnet Synchronous Motor Control System

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