RBF Neural Network Control for Linear Motor-Direct Drive Actuator Based on an Extended State Observer

Liu, Zhi; Chen, Tefang

doi:10.1155/2016/8390529

Cited by 6 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When the observation error is large, the observer gain also increases to achieve fast estimation. For the flexible and intelligent adjustment of parameters, neural networks and TESO are combined in [28] to handle the problem that appropriate ESO parameters are difficult to obtain. On the other hand, model-assisted ESOs are designed in [29] based on the hydraulic system model to improve system state observation accuracy.…”

Section: Introductionmentioning

confidence: 99%

Finite-Time Backstepping Control for Electro-Hydraulic Servo System via Extended State Observer with Perturbation Estimation Performance Improvement

Meng

Yan

et al. 2022

Machines

View full text Add to dashboard Cite

The extended state observer (ESO) has been widely used in the state and perturbation estimation of the electro-hydraulic servo system. It was found that there was a controlled quantity in the transfer function between the perturbation estimation value and the disturbance. This indicates that the traditional linear ESO’s estimation of the disturbance is affected by the change in the control input. To solve this problem, a new structure ESO for a hydraulic system (LHYESO) was designed by introducing the hydraulic system’s load pressure and system model. The corresponding frequency domain analysis results show that it eliminates the control input in the transfer function and reduces the dependence of the high-frequency domain range of the perturbation estimation on the significant observer gain. To improve the estimation speed, a finite-time convergent ESO for hydraulic systems (FTHYESO) was proposed based on the structure of LHYESO, and it was proved that the observation error converged to a sufficiently small value during a finite time. Moreover, a finite-time backstepping controller has been designed by using the Lyapunov method to guarantee the rapidity and precise response of the hydraulic servo system. Finally, the experiment results show the effectiveness of the proposed method.

show abstract

Section: Introductionmentioning

confidence: 99%

Finite-Time Backstepping Control for Electro-Hydraulic Servo System via Extended State Observer with Perturbation Estimation Performance Improvement

Meng

Yan

et al. 2022

Machines

View full text Add to dashboard Cite

show abstract

“…Zhou et al constructed a generalized ESO to estimate the lumped disturbance of the directcurrent motor servo system in real-time fashion [30]. Liu and Chen utilized ESO based on switched gain to estimate the influence of the hydraulic power and load disturbances [31]. Li et al proposed a fixed-time ESO to estimate the external disturbances and parameter uncertainties of the quad rotor unmanned aerial vehicle [32].…”

Section: Introductionmentioning

confidence: 99%

Integral Nonsingular Terminal Sliding Mode Control of Hydraulic Servo Actuator Based on Extended State Observer

Wan

2021

Shock and Vibration

View full text Add to dashboard Cite

Hydraulic servo actuator always suffers from various disturbance and uncertainties, which makes it difficult to design a higher performance controller. In this paper, an integral nonsingular terminal sliding mode controller based on extended state observer (ESO-INTSM) is proposed to improve the robust performance of hydraulic servo actuator. The ESO is designed to estimate not only the parametric uncertainties but also the model disturbance. Based on the observed states of ESO, the proposed controllers could enable hydraulic servo actuator to track the desired motion trajectories. The stability of the synthesized controller is proved via Lyapunov analysis, which is very important for high-accuracy tracking control of hydraulic servo actuator. Simulation and experimental results demonstrate that the proposed control strategy can effectively attenuate the adverse influence caused by the uncertainties and apparently improve the tracking accuracy.

show abstract

“…Among various NNs, the radial basis function NN (RBFNN) has been extensively applied to uncertain and complicated industrial control systems in recent years [38]- [41] due to the inherent characteristics of simple network structure, good approximation, fast learning speed, and strong generalization [42], [43]. In [40], a nonlinear current decoupling control scheme based on the RBFNN inverse system is presented.…”

Section: Introductionmentioning

confidence: 99%

“…In this method, the RBFNN is used to estimate the speed in real time. In [43], in order to reduce the effect of the hydraulic power and load fluctuation on the linear motordirect drive actuator system, the RBFNN is utilized to optimize controller parameters to achieve the novel adaptive control. However, for the SCS of PMSM, the studies in which the RBFNN is utilized to optimize control performance, and in which the complicated load fluctuation and uncertain J and B are considered simultaneously are rarely found.…”

Section: Introductionmentioning

confidence: 99%

Speed Regulation Based on Adaptive Control and RBFNN for PMSM Considering Parametric Uncertainty and Load Fluctuation

et al. 2020

View full text Add to dashboard Cite

A novel speed control scheme combining adaptive speed controller and radial basis function neural network (ASC-RBFNN) is proposed for the speed regulation of permanent magnet synchronous motor (PMSM) in this paper. On one hand, in order to reduce the effect of parametric uncertainty and complicated load fluctuation on the speed control performance, an ASC is proposed. Meanwhile, the speed control system (SCS) of PMSM with the proposed ASC is asymptotically stable even though the parametric uncertainty and complicated load fluctuation exist. On the other hand, with consideration of the uncertainty of complicated load, PMSM parameters, and ASC parameters, the RBFNN is used to optimize all ASC parameters for optimal speed control performance. Finally, the performance is verified on an experiment platform. The results indicate that the SCS with the ASC-RBFNN speed control scheme is with good system stability, fast speed response, and strong anti-fluctuation performance in whole-speed range.

show abstract

RBF Neural Network Control for Linear Motor-Direct Drive Actuator Based on an Extended State Observer

Cited by 6 publications

References 16 publications

Finite-Time Backstepping Control for Electro-Hydraulic Servo System via Extended State Observer with Perturbation Estimation Performance Improvement

Finite-Time Backstepping Control for Electro-Hydraulic Servo System via Extended State Observer with Perturbation Estimation Performance Improvement

Integral Nonsingular Terminal Sliding Mode Control of Hydraulic Servo Actuator Based on Extended State Observer

Speed Regulation Based on Adaptive Control and RBFNN for PMSM Considering Parametric Uncertainty and Load Fluctuation

Contact Info

Product

Resources

About