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

Wang, Yicheng; Fang, Shuhua; Hu, Jianxiong; Huang, Demin

doi:10.1109/tec.2023.3235927

Cited by 8 publications

(10 citation statements)

References 27 publications

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“…Operational schematic diagrams are presented in Figures 2 and 3. (a) The ADRC involves designing nonlinear control laws and optimizing parameters, resulting in a more complex controller design [8][9][10][11]. Although the parameter tuning process of LADRC is relatively straightforward, its linear structure results in poor control performance under nonlinear conditions [12].…”

Section: Design Of the Lifting Wing Angle Of Attack Conversion Devicementioning

confidence: 99%

“…Gheisarnejad et al [7] introduced a controller leveraging deep deterministic policy gradient (DDPG) technology, which minimizes observer estimation errors and enhances the dynamic characteristics of the controller. Regarding the optimization of the ADRC controller, Wang et al [8] proposed a novel Deep Reinforcement Learning (DRL)-based ADRC to enhance the performance of Permanent Magnet Synchronous Motors (PMSMs). Yang et al [9] introduced an enhanced velocity compensator into a second-order Linear Auto-Disturbance Rejection Controller (LADRC) deviation coupling control structure, effectively enhancing system accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Synchronous Control of High-Speed Train Lift Wing Angle of Attack Drive System Based on Chaotic Particle Swarm Optimization and Linear Auto-Disturbance Resistant Controller

Cheng,

Liu,

Wang

2024

Electronics

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In this study, a control scheme is proposed based on Chaotic Particle Swarm Optimization (CPSO) to enhance the Linear Auto-Disturbance Rejection Controller (LADRC). The focus is on addressing the challenge of high-precision variations in angle-of-attack through dual-motor cooperative control within the lifting wing of a high-speed train. The scheme initiates with the design of a dual-loop structure for LADRC, integrating position and current control. The position loop is further refined. Subsequently, the CPSO algorithm is employed to optimize the parameters of the LADRC controller. Ultimately, the loop is closed by feeding back the position error in the cross-coupled structure to the current loop, thereby achieving high-precision control. The performance of the proposed structure is validated through both Matlab/Simulink simulations and an experimental platform. The experimental results demonstrate that CPSO-LADRC, in comparison to traditional LADRC and Proportion-Integration-Differentiation (PID) control, exhibits an increase in the maximum response time by 3.76 s and 3.3 s, respectively, a reduction in overshoot by 1.12% and 0.8%, as well as a decrease in the maximum synchronization error by 0.45 cm and 1 cm, respectively. These findings validate the effectiveness of the proposed synchronous loop controller method in simplifying computational complexity, enhancing system responsiveness, robustness, and synchronization performance. Additionally, our approach facilitates precise angle-of-attack transformation for the lifting wings of high-speed trains effectively.

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Section: Design Of the Lifting Wing Angle Of Attack Conversion Devicementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Synchronous Control of High-Speed Train Lift Wing Angle of Attack Drive System Based on Chaotic Particle Swarm Optimization and Linear Auto-Disturbance Resistant Controller

Cheng,

Liu,

Wang

2024

Electronics

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“…To efficiently control such complex PMSM systems, control methods such as modified PI control [10][11][12][13][14], sliding mode control (SMC) [15][16][17][18], model predictive control (MPC) [19][20][21], and deep learning (DL)-based control [22][23][24] have been proposed. In [11][12][13][14], modified PI control-based methods were implemented.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Recently, research on applying deep learning to PMSMs [22][23][24] has been conducted. To improve the performance of PMSMs in the electric aircraft field, a novel active disturbance rejection control based on deep reinforcement learning [22] was proposed. It trains a neural network using the twin-delayed deep deterministic (TDDD) policy gradient algorithm and performs optimization of the DRL model.…”

Section: Introductionmentioning

confidence: 99%

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Linear Matrix Inequality-Based Robust Model Predictive Speed Control for a Permanent Magnetic Synchronous Motor with a Disturbance Observer

Kim,

Kim

2024

Energies

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In this paper, a linear matrix inequality (LMI)-based robust model predictive speed control (RMPSC) with a disturbance observer (DOB) is proposed to guarantee stability and control performance against the parameter uncertainty and disturbance of a permanent magnetic synchronous motor (PMSM). All external torques applied to the PMSM are defined as disturbance, estimated by the DOB, and used to construct the RMPSC method. The proposed DOB and RMPSC are determined by a multiple LMI-based optimization approach. Furthermore, parameter uncertainty within a certain range, due to manufacturing errors or aging deterioration, is considered and a systematic tuning method is proposed to obtain the optimal gains. Finally, an offline optimization method is developed that ensures a low computational load to enable real-time processing. Simulation results demonstrate the effectiveness and validity of the proposed control method.

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Deep Reinforcement Learning Based Control of a Grid Connected Inverter With LCL-Filter for Renewable Solar Applications

Rajamallaiah,

Karri,

Alghaythi

et al. 2024

IEEE Access

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This research paper presents a novel approach to current control in Grid-Connected Inverters (GCI) using Deep Reinforcement Learning (DRL) based Twin Delayed Deep Deterministic Policy Gradient (TD3) method. The study focuses on addressing the limitations of traditional control techniques and state of the art techniques, particularly Proportional-Integral (PI) control and Model Predictive Control (MPC), by leveraging the adaptive and autonomous learning capabilities of DRL. The proposed novel modified TD3-based DRL method learns an optimal control policy directly from raw data, enabling the controller to adapt and improve its performance in real-time. The research includes a comprehensive analysis of the implementation and validation of the modified TD3-based DRL control in a grid-connected three phase three level Neutral Point Clamped (NPC) inverter system with Inductor-Capacitor-Inductor (LCL) filter. Realtime validation experiments are conducted to evaluate the control performance, power transfer capability in grid compliance. Furthermore, a detailed comparison is presented with experimentation, highlighting the advantages of the TD3-based DRL control over PI and MPC control techniques. Robustness checking is performed under various operating conditions, including parameter variations and dynamic conditions in the grid. The results analysis demonstrates that the TD3-based DRL control outperforms traditional PI control techniques in terms of static, dynamic response, and robustness. Additionally, The DRL based grid connected inverter current control method is validated in Renewable Energy Source (RES) solar PV grid integration application.

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

Cited by 8 publications

References 27 publications

Synchronous Control of High-Speed Train Lift Wing Angle of Attack Drive System Based on Chaotic Particle Swarm Optimization and Linear Auto-Disturbance Resistant Controller

Synchronous Control of High-Speed Train Lift Wing Angle of Attack Drive System Based on Chaotic Particle Swarm Optimization and Linear Auto-Disturbance Resistant Controller

Linear Matrix Inequality-Based Robust Model Predictive Speed Control for a Permanent Magnetic Synchronous Motor with a Disturbance Observer

Deep Reinforcement Learning Based Control of a Grid Connected Inverter With LCL-Filter for Renewable Solar Applications

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