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

Xu, Lingliang; Chen, Guiming; Li, Qiaoyang

doi:10.1049/elp2.12108

Cited by 5 publications

(2 citation statements)

References 24 publications

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“…Case 2: In order to verify the finiteness of the proposed controller, it is compared with other controllers to verify. The controller proposed above is compared with ESO control (Xu et al, 2021). The simulation results are shown in Figure 4.…”

Section: Simulationmentioning

confidence: 99%

Model-free robust adaptive control of overhead cranes with finite-time convergence based on time-delay control

Liu

Wei-min

2022

Transactions of the Institute of Measurement and Control

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In this paper, a model-free robust adaptive control scheme with finite-time convergence based on time-delay control is proposed for anti-sway and positioning control of two-dimensional underactuated overhead cranes. First, the whole overhead cranes system is simplified to an ultra-local model for time delay estimation (TDE). TDE brings a direct and effective model-free property but also an estimation error. Second, a sliding mode disturbance observer is designed to estimate and compensate for the TDE error. Third, sliding mode control (SMC) is used to enhance the robustness of the controller. An adaptive integral sliding surface is then designed to accelerate the sliding surface convergence rate and shorten the convergence time. To further optimize the selection of parameters, the parameter estimation is integrated to enhance the performance of model-free control. In the final analysis of the simulation, data yield that the introduction of parameter estimation increases the control performance by more than 20% on average, and the above facts verify the effectiveness of the scheme. Finally, the stability of the closed-loop control system is analyzed by using Lyapunov stability theory, and the effectiveness and robustness of the control scheme are verified through computer simulation results.

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Section: Simulationmentioning

confidence: 99%

Model-free robust adaptive control of overhead cranes with finite-time convergence based on time-delay control

Liu

Wei-min

2022

Transactions of the Institute of Measurement and Control

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“…13,14 Among them, the EMF method is simple and reliable and is widely used in high-and mediumspeed situations. The main EMF-based control methods are sliding mode control, 3,[15][16][17][18][19][20][21] state observer, 22,23 and model reference adaptive control, 24,25 model predictive control. [26][27][28] As sliding mode control does not require high system model accuracy, is insensitive to parameter changes and external disturbances, and is a special nonlinear control system with a variable structure, it can be used to estimate the EMF and extract the speed and position information in the EMF.…”

Section: Introductionmentioning

confidence: 99%

Adaptive quasi-proportional resonant control with parameter estimation for PMSM sensorless control

Chen

2023

Advances in Mechanical Engineering

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Conventional sliding mode control (SMO) utilizes a sign function and a low-pass filter (LPF) to achieve sensorless control of a permanent magnet synchronous motor (PMSM). However, the introduction of the sign function generates a large number of high harmonics, resulting in a significant system output chattering, whereas the addition of the LPF generates control delays and phase shifts. These degrade the PMSM speed and the position estimation accuracy and reduce the system control performance. To improve the performance of the PMSM, adaptive quasi-proportional resonant sensorless control with parameter estimation (AQPR_PE) is proposed. In this control model, an adaptive quasi-proportional resonance controller is used reduce the system output error and to weaken the chattering phenomenon. Meanwhile, the system PMSM parameters are estimated online and fed back to the control model to improve the system parameter robustness and the control accuracy. The Bode diagram, Popov theory, and root trajectory are used to analyze the stability of AQPR, parameter estimation, and AQPR_PE, respectively. Finally, the effectiveness of the proposed method is demonstrated by experimental validation.

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Parallel adaptive RBF neural network-based active disturbance rejection control for hybrid compensation of PMSM

Gao,

Su,

Pan

et al. 2024

RIA

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Purpose This study aims to promote the anti-disturbance and tracking accuracy performance of the servo systems, in which a modified active disturbance rejection control (MADRC) scheme is proposed. Design/methodology/approach An adaptive radial basis function (ARBF) neural network is utilized to estimate and compensate dominant friction torque disturbance, and a parallel high-gain extended state observer (PHESO) is employed to further compensate residual and other uncertain disturbances. This parallel compensation structure reduces the burden of single ESO and improves the response speed of permanent magnet synchronous motor (PMSM) to hybrid disturbances. Moreover, the sliding mode control (SMC) rate is introduced to design an adaptive update law of ARBF. Findings Simulation and experimental results show that as compared to conventional ADRC and SMC algorithms, the position tracking error is only 2.3% and the average estimation error of the total disturbances is only 1.4% in the proposed MADRC algorithm. Originality/value The disturbance parallel estimation structure proposed in MADRC algorithm is proved to significantly improve the performance of anti-disturbance and tracking accuracy.

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

Cited by 5 publications

References 24 publications

Model-free robust adaptive control of overhead cranes with finite-time convergence based on time-delay control

Model-free robust adaptive control of overhead cranes with finite-time convergence based on time-delay control

Adaptive quasi-proportional resonant control with parameter estimation for PMSM sensorless control

Parallel adaptive RBF neural network-based active disturbance rejection control for hybrid compensation of PMSM

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