Multiscenarios Parameter Optimization Method for Active Disturbance Rejection Control of PMSM Based on Deep Reinforcement Learning

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

doi:10.1109/tie.2022.3225829

Cited by 19 publications

(9 citation statements)

References 28 publications

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“…Based on the provided equation, the design of a second-order linear active disturbance rejection controller for the position loop can be formulated. Considering the structure of Equation ( 7) and approximating the transfer function of the current closed-loop control system as 1, the equality i * q = i q can be derived, thus allowing for the transformation of Equation (20) as:…”

Section: Position Loop Controller Designmentioning

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: Position Loop Controller Designmentioning

confidence: 99%

“…They proved this approach results in a more robust ADRC controller than the conventional ADRC. Wang et al [20] introduced artificial intelligence algorithms into the parameter optimization process of the ADRC. They constructed a DRL parameter optimization model.…”

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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“…Ensuring the accuracy of motor parameters is an indispensable prerequisite for achieving high performance control of PM-SWG. However, in actual working conditions, the electrical parameters [5][6][7][8] of PMSWG are easily affected by magnetic saturation, external temperature, and other factors. In order to realize efficient motor performance, it is increasingly important that motor parameters are accurately recognized.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Learning Co-evolutionary Variational Particle Swarm Optimization Algorithm for Parameter Identification of PMSWG

Zhang,

Zhou,

Luo

et al. 2024

PIER C

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Targeting the problems of traditional particle swarm algorithm easily falling into local optimum and low recognition accuracy, an adaptive learning co-evolutionary variational particle swarm optimization algorithm (ALCEVPSO) is proposed in this paper to identify the parameters of permanent magnet synchronous wind generator (PMSWG). At first, an adaptive learning strategy is adopted for the inertia weights of the PSO, and the global optimization seeking ability of the PSO is improved. After that, multiple swarm co-evolution strategies are introduced to share the best positions within sub-populations, and by this method, the algorithm's falling into local optimality is avoided. Finally, Cauchy Gaussian mixed variants are introduced, and the population diversity is enriched. The proposed method has the advantages of strong optimization ability and high search accuracy compared with the traditional particle swarm algorithm, which is shown by simulated and experimental results. By this method, the motor parameters of the permanent magnet synchronous motor can be accurately identified.

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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%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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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Multiscenarios Parameter Optimization Method for Active Disturbance Rejection Control of PMSM Based on Deep Reinforcement Learning

Cited by 19 publications

References 28 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

An Adaptive Learning Co-evolutionary Variational Particle Swarm Optimization Algorithm for Parameter Identification of PMSWG

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

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