Iterative Learning Control for Beam Loading Cancellation in Electron Linear Accelerator

Shahriari, Zahra; Dumont, Guy A.; Fong, K.

doi:10.1016/j.ifacol.2021.12.010

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…An ILC algorithm was introduced by Arimoto [30], and it was used for controlling robots doing repetitive movements. ILC has been applied in many control applications such as high-speed trains [31]- [33], hydraulic cushion [34], walking piezo actuators (WPA) [35], fault estimation (FE) [36], twin-roll strip casting [37], crane system [38], electron linear accelerator [39], tank gun control system [40], monocrystalline batch process [41], nano-positioning stage [42], fractional-order multi-agent systems (FOMASs) [43], robotic manipulator [44], [45], [46], robotic path learning [47], magnetically levitated (maglev) planar motor [48], model uncertainties [49], autonomous farming vehicle [50], unmanned vehicle [51], additive manufacturing system [52], and marine hydrokinetic energy system [53]. A general ILC system has an architecture as shown in Fig.…”

Section: B Ilc 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: B Ilc 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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“…This alone, however, could not compensate the effect of the long-term fluctuations in the system, such as variations of a klystron gain and amount of the beam loading. At other facilities around the world, the interactive learning control (ILC) algorithm has been proved to be very effective against the iterative errors [4][5][6][7][8][9][10][11]. Therefore, we secondary developed the ILC scheme of the adaptive beam-loading compensation for the J-PARC LINAC [12].…”

Section: Jinst 17 T11002mentioning

confidence: 99%

Development of the novel and superior adaptive beam-loading compensation system calculated in the frequency domain for the J-PARC Linac

et al. 2022

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The Japan Proton Accelerator Research Complex (J-PARC) is one of the highest intensity proton accelerator facilities in the world. Its accelerator consists of a 400-MeV linac (LINAC), a 3-GeV rapid-cycling synchrotron (RCS), and a 30-GeV main ring (MR) and its beam is provided for wide-ranging applications. The peak current of the beam at LINAC had been increased from 30 mA to the design value of 50 mA in 2018. Therefore, the performance of the beam-loading compensation implementing in the low-level radio frequency (LLRF) system has been more important to satisfy the stability of the accelerating gradient. Although the iterative learning control (ILC) scheme with adaptive ability was demonstrated, the practical use of ILC was not convenient for large accelerators such as the J-PARC LINAC with forty-nine RF stations. Therefore, we developed at novel and superior adaptive beam-loading compensation system that was calculated in the frequency domain. Compared to the scheme of the previous ILC, the newly developed system is useful because it does not require parameter adjustment using the beam. In the experimental results utilizing this system, the optimum tables of FF_BEAM were able to be obtained as a smaller number of iterations. The peak-to-peak stability of amplitude was achieved to ± 0.23% and the peak-to-peak stability of phase was improved to ± 0.036°. In addition, errors such as white noise did not accumulate for the table of FF_BEAM and this system solved the problem of the divergence when the number of iterations was increased. This implementation of the adaptive beam-loading compensation system calculated in the frequency domain will potentially contribute to the improvement of the J-PARC LINAC LLRF, and this system may lead to a stable user operation. This system is so simple in principle and effective that it could be applied to other linear accelerators with the pulsed beam.

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Iterative Learning Control for Beam Loading Cancellation in Electron Linear Accelerator

Cited by 2 publications

References 4 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

Development of the novel and superior adaptive beam-loading compensation system calculated in the frequency domain for the J-PARC Linac

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