Application of Repetitive Control in Electric Spring

Zhang, Xi; Zhang, Zheng

doi:10.1109/access.2020.3041648

Cited by 15 publications

(6 citation statements)

References 10 publications

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“…Another control method for perfect tracking of repetitive trajectory is called Repetitive Control (RC). Recently, RC has been used for leg exoskeleton control [56], 1-DOF Lagrangian system [57], industrial wide-format printing [58], contouring control of micro-stereolithography [59], magnetically suspended rotor system [60], [61], [62], control of linear actuator [63], functional electrical stimulation [64], [65], grid-connected inverters [66], [67], [68], [69], [70], nanometer-order contouring [71], dynamical galvanometerbased raster scanning [72], atomic force microscopy (AFM) scanner [73], mechanical ventilation [74], permanent magnet synchronous motor (PMSM) [75]- [77], servo motor [78], plug-in electric vehicle (PEV) charger [79], piezoelectric nano positioning stage [80], hydraulic press system [81], robotic manipulator [82], [83], and electric spring [84]. The RC is based on the idea of internal model principle by Francis and Wonham [85] stated that by incorporating model of reference/disturbance then perfect reference tracking or disturbance rejection can be achieved.…”

Section: Rc Designmentioning

confidence: 99%

A Performance Evaluation of Repetitive and Iterative Learning Algorithms for Periodic Tracking Control of Functional Electrical Stimulation System

Kurniawan,

Pratiwi,

Adinanta

et al. 2024

Journal of Robotics and Control

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Functional electrical stimulation (FES) is a medical device that delivers electrical pulses to the muscle, allowing patients with spinal cord injuries to perform activities such as walking, cycling, and grasping. It is critical for the FES to generate stimuli with the appropriate controller so that the desired movements can be precisely tracked. By considering the repetitive nature of the movements, the learning-based control algorithms are utilized for regulating the FES. Iterative learning control (ILC) and repetitive control (RC) are two learning algorithms that can be used to accomplish accurate repetitive motions. This study investigates a variety of ILC and RC designs with distinct learning functions; this constitutes our contribution to the field. The FES model of ankle angle, which is in a class of discrete-time linear systems is considered in this study. Two learning functions, i.e., proportional, and zero-phase learning functions, are simulated for the second-order FES model running at a sampling time of 0.1 s. The results indicate the superior performance of the ILC and RC in terms of convergence rate using the zero-phase learning function. ILC and RC with a zero-phase learning function can reach a zero root-mean-square error in two iterations if the model of the plant is correct. This is faster than proportional-based ILC and RC, which takes about 40 iterations. This indicates that the zero-phase learning function requires two iterations to ensure that the patient's ankle angle precisely tracks the intended trajectory. However, the tracking performance is degraded for both control methods, especially when the model is subject to uncertainties. This specific problem can lead to future research directions.

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Section: Rc Designmentioning

confidence: 99%

A Performance Evaluation of Repetitive and Iterative Learning Algorithms for Periodic Tracking Control of Functional Electrical Stimulation System

Kurniawan,

Pratiwi,

Adinanta

et al. 2024

Journal of Robotics and Control

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“…The compensator is used to stabilize the resulting RC closed-loop system, while the one-cycle delay, defined as an internal model, is needed to generate a repetitive control signal. Due to these fundamental features, the RC has been proven effective in suppressing such disturbances and is thus continuously developed, as reported in the literature [1][2][3][4][5][6] (and references therein).…”

Section: Introductionmentioning

confidence: 99%

Low-order sliding mode repetitive control for uncertain linear systems perturbed by band-limited periodic disturbances

Kurniawan¹,

Pratiwi²,

Harno

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part I:

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This article presents a systematic method for designing discrete-time low-order sliding mode repetitive controller for a class of uncertain linear systems. A linear system considered in this class is perturbed by band-limited periodic disturbances and parametric uncertainties. The digital controller design method we propose combines two control strategies, namely the repetitive control and the sliding mode control techniques. The proposed controller is required to be low order and is intended to reject the periodic disturbances with specific dominant frequencies and to establish robustness against plant parametric uncertainties. Moreover, the proposed method yields a repetitive controller with only a small number of delay terms and can perform fast transient responses. We show through mathematical analyses that the linear system applying the low-order sliding mode repetitive controller satisfies sliding mode stability and robustness criteria. In addition, we demonstrate via a numerical example that a servomotor controlled by the low-order sliding mode repetitive controller can track a frequency-shifted triangular signal with a short transient period and minor errors. Other examples are also presented and show that the low-order sliding mode repetitive controller exhibits better performance than other relevant control methods.

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“…Then to various intelligent control methods, the control of ES gradually develops towards strong robustness, nonlinearity and intelligence. ES belongs to variable structure nonlinear systems, linear controllers often can not play a good effect, such as the PI controller can not be realized on the AC reference signal of the non-differential following, and the traditional linear controller parameter debugging time-consuming and laborious, so more and more nonlinear control theories have been utilized in the electric power springs, such as the smooth-mode control [11], fuzzy control [12], repetitive control [13], adaptive control [14], and so on.…”

Section: Introductionmentioning

confidence: 99%

Research on Super-Twisting Sliding Mode Voltage Regulation Control of Electric Spring

Wang,

Zhang,

Hou

2024

IJMEE

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The traditional control of electric spring (ES) is too complex and lack of robustness, as well as the chattering phenomenon and control accuracy of traditional sliding mode control, a second-order terminal sliding mode control based on super twisting algorithm is proposed. In this paper, the phase plane method is used to establish the mathematical model of ES, solve the state space equation, and regard the AC power supply as a simplified error analysis. Then a second-order sliding mode controller is proposed. The controller parameters are solved to obtain the second-order sliding mode control rate, and the stability is verified. Finally, three groups of comparative simulation experiments were set up by Matlab/Simulink, and the effectiveness of the model was verified by considering the voltage mutation caused by the change of power supply and circuit parameters. Compared with the traditional control method, the proposed control strategy not only ensures the voltage stability, but also improves the control accuracy and the performance of suppressing sliding mode chattering, and has better dynamic response ability.

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Application of Repetitive Control in Electric Spring

Cited by 15 publications

References 10 publications

A Performance Evaluation of Repetitive and Iterative Learning Algorithms for Periodic Tracking Control of Functional Electrical Stimulation System

A Performance Evaluation of Repetitive and Iterative Learning Algorithms for Periodic Tracking Control of Functional Electrical Stimulation System

Low-order sliding mode repetitive control for uncertain linear systems perturbed by band-limited periodic disturbances

Research on Super-Twisting Sliding Mode Voltage Regulation Control of Electric Spring

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