Neural Network Learning Adaptive Robust Control of an Industrial Linear Motor-Driven Stage With Disturbance Rejection Ability

Wang, Ze; Hu, Chuxiong; Zhu, Yu; He, Shu; Yang, Kaiming; Zhang, Ming

doi:10.1109/tii.2017.2684820

Cited by 140 publications

(44 citation statements)

References 36 publications

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“…It can be known from the control law in Equation (29) and the tracking error dynamic in Equation (32) that the tracking performance of the adaptive robust controller depends on the design of the robust control term u r . Since the adaptive law design is synthesized by tracking error, the parameter projection method is used to modify the adaptive law [20]. Therefore, the adaptive law of adaptive robust control is expressed as:…”

Section: Electric Medium Passenger Bus Vector Control System Based Onmentioning

confidence: 99%

“…In the study of adaptive robust control, the authors of [19] used adaptive synthesis robust control strategies based on µ synthesis to resist the interference of high-frequency dynamic problems generated by the motor structure mode on linear motor control. The authors of [20] used neural networks to learn adaptive robust controllers to resist interference from unknown factors. The author of [21] used an adaptive robust controller based on extended disturbance observer to improve the control accuracy of linear motors.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Robust Control System for Axial Flux Permanent Magnet Synchronous Motor of Electric Medium Bus Based on Torque Optimal Distribution Method

et al. 2019

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In this paper, an adaptive robust drive control system for an axial flux permanent magnet synchronous motor of an electric medium-sized bus based on the optimal torque distribution method is studied. The drive control system is mainly divided into two parts. First, a torque distribution method is proposed. The optimal torque distribution method based on particle swarm optimization algorithm is used to increase the high efficiency interval of the system and apply it to the energy feedback braking. Secondly, in order to reduce the nonlinear disturbance of the system and improve the accuracy of the unified control, this paper models and studies the vector system based on adaptive robust control. Finally, the whole drive control system is modeled, simulated and experimented. The simulation and experimental results show that the torque distribution method proposed in this paper can effectively increase the high-efficiency running time of the electric medium bus, and improve the shortcomings of insufficient mileage of the electric medium-sized bus. The use of a current controller based on adaptive robust control improves the control accuracy of the drive system and can effectively suppress the disturbances generated by it.

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Section: Electric Medium Passenger Bus Vector Control System Based Onmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive Robust Control System for Axial Flux Permanent Magnet Synchronous Motor of Electric Medium Bus Based on Torque Optimal Distribution Method

et al. 2019

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“…In [27], a novel model reference adaptive controller based on the RBFNN to simulate the nonlinear characteristics of the system without knowledge of accurate motor models or parameters for the drive system of the fivephase interior permanent magnet motor. In [28], the RBFNN is applied to approximate and compensate the complicated disturbances to achieve good tracking performance and excellent disturbance rejection performance for an industrial linear motor stage. Motivated by these methods, we adopt the RBFNN to obtain good decoupling performance in this paper.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Decoupling Control Using Radial Basis Function Neural Network for Permanent Magnet Synchronous Motor Considering Uncertain and Time-Varying Parameters

et al. 2020

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In this paper, a novel control scheme with respect to the adaptive decoupling controller based on radial basis function neural network (ADEC-RBFNN) is developed. On one hand, in order to improve the system performance of the torque closed-loop control system (TCLCS) of the permanent magnet synchronous motor (PMSM) with the effects of the dynamic coupling and back electromotive force (EMF), we present a novel ADEC with which the TCLCS is asymptotically stable under Lyapunov stability theory. On the other hand, considering the uncertainty and time variant of both the PMSM and ADEC parameters, the RBFNN is utilized to optimize the ADEC parameters to achieve optimal system performance. Ultimately, experimental results demonstrate that the torque and current with the proposed control scheme have the good performance of small fluctuation and fast response in the whole ranges of the speed and torque, that is to say, the system with the proposed control scheme is with the good decoupling performance. INDEX TERMS Adaptive decoupling control, permanent magnet synchronous motor, radial basis function neural network, torque closed-loop control system.

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“…Over the past few years, there have been several kinds of research spending amounts of effort to deal with position tracking problems of linear motor systems in the presence of external disturbances. A neural network learning adaptive robust controller was designed by [12] to achieve both tracking performance and disturbance rejection. Besides, compensation approaches were proposed in researches [13][14][15], by which the frictional force and position-dependent disturbance are compensated to guarantee the stability of the overall system.…”

Section: Introductionmentioning

confidence: 99%

Observer-Based Tracking Control for Polysolenoid Linear Motor with Unknown Disturbance Load

Quang

2020

Actuators

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Linear motors have been playing a crucial role in mechanical motion systems due to its ability to provide a straight motion directly without mediate mechanical actuators. This paper investigates tracking control problems of Polysolenoid Linear Motor, which is a particular type of permanent magnet linear motor in a tubular structure. In order to deal with unmeasurable velocity, our method proposes a novel observer guaranteed asymptotic convergence of the observer errors. Then, based on observed velocity, our method proposes controllers for position-velocity and current tracking control concerning an unknown disturbance load problem by using Lyapunov direct method. The proposed controllers ensure that the position-velocity tracking error converges to arbitrarily small values by adjusting control parameters. Finally, the validity and effectiveness of our approach are shown in illustrative examples.

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Neural Network Learning Adaptive Robust Control of an Industrial Linear Motor-Driven Stage With Disturbance Rejection Ability

Cited by 140 publications

References 36 publications

Adaptive Robust Control System for Axial Flux Permanent Magnet Synchronous Motor of Electric Medium Bus Based on Torque Optimal Distribution Method

Adaptive Robust Control System for Axial Flux Permanent Magnet Synchronous Motor of Electric Medium Bus Based on Torque Optimal Distribution Method

Adaptive Decoupling Control Using Radial Basis Function Neural Network for Permanent Magnet Synchronous Motor Considering Uncertain and Time-Varying Parameters

Observer-Based Tracking Control for Polysolenoid Linear Motor with Unknown Disturbance Load

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