On the synthesis of a stable and causal compensator for discrete-time high-order repetitive control systems

Kurniawan, Edi; Adinanta, Hendra; Harno, Hendra G.; Prakosa, Jalu A.; Suryadi, Suryadi; Purwowibowo, Purwowibowo

doi:10.1007/s40435-020-00695-y

Cited by 9 publications

(6 citation statements)

References 20 publications

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“…RC system with control law (21) and plant model ( 2) is asymptotically stable, if the following conditions are fulfilled [86], [87]:…”

Section: Rc Designmentioning

confidence: 99%

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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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“…RC system with control law (21) and plant model ( 2) is asymptotically stable, if the following conditions are fulfilled [86], [87]:…”

Section: Rc Designmentioning

confidence: 99%

“…For stable minimum phase plant, the learning function is simply the inverse of 𝑃(𝑧) in (2), 𝐿(𝑧) = 1/𝑃(𝑧), as its inverse will have stable poles. Some other learning function designs can also be found in [86], [88]. Phase lead-based learning function has been proposed in [88].…”

Section: The Learning Function 𝐿(𝑧) Satisfiesmentioning

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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“…Quanser QUBE-Servo 2 [10] merupakan fasilitas pembelajaran motor DC yang dapat dimanfaatkan untuk meneliti pemodelan sistem kontrolnya. Bahkan, produk ini telah dimanfaatkan dalam penelitian sistem kontrol tingkat internasional [11,12,13]. Tidak hanya memiliki data spesifikasi motor DC untuk perumusan teori tetapi juga dapat terhubung dengan software Matlab dan Simulink sehingga berbagai teknik pemodelan dan validasi eksperimen dapat dilakukan.…”

Section: Pendahuluanunclassified

“…Perumusan model dari hitungan teori ternyata lebih jauh berbeda dari validasi eksperimen. Hal ini terjadi mungkin diakibatkannya ada suatu faktor yang berpengaruh namun belum dimasukkan terutama pada persamaan (11). Hasil model identifikasi pada fungsi transfer berpangkat 1 cenderung lebih mendekati model perumusan teori motor DC pada rumus (11) karena sama-sama merupakan persamaan linier berpangkat 1.…”

Section: Gambar 7 Hasil Perbandingan Model Identifikasi Pangkat 1 Dengan Eksperimenunclassified

Kajian Identifikasi Model Eksperimen pada Kontrol Kecepatan Motor DC

Prakosa

Suryadi

Kurniawan

et al. 2021

joki

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Motor arus searah atau Direct Current (DC) masih banyak digunakan dalam industri, pendidikan, penelitian bahkan dalam peralatan rumah tangga. Pengembangan teknik kontrol kecepatan motor DC dengan berbagai teori kontrol baik persektif adaptif maupun robust menantang untuk dilakukan. Model sistem kontrol kecepatan motor DC yang akurat perlu diteliti baik teori dan eksperimen. Fasilitas QUBE-Servo 2 dapat mengimplementasikan kajian pemodelan sistem kontrol tersebut terutama secara eksperimen. Data eksperimen tegangan listrik dan kecepatan sudut motor dapat dimanfaatkan untuk identifikasi model sistem kontrolnya yang dapat dibandingkan dengan teori motor DC dari data spesifikasi motor terkait. Tujuan penelitian ini adalah untuk mempelajari metode identifikasi model sistem kontrol kecepatan sudut pada motor DC guna mendapatkan model yang paling akurat. Hasil eksperimen teknik kalang tertutup Proportional-Integral-Derivative (PID) menunjukkan bahwa model perumusan teori motor DC berbeda terutama fenomena overshoot dan fluktuasi pada tahap responnya. Fungsi transfer dari hasil identifikasi model berpangkat 2 lebih mendekati hasil eksperimen yang cenderung tidak linear daripada pangkat yang lebih rendah. Pemanfaatkan metode sistem identifikasi dalam merancang model berbasis data eksperimen memiliki akurasi baik sehingga layak digunakan dalam pengembangan dan optimalisasi teknik kontrol kecepatan motor DC.

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“…Another approach is HORC method, which attempts to modify the structure of the internal model and thus gives the robustness against period variation. Some examples of this approach were found in Steinbuch et al (2007), Ramos et al (2013), Flores and Flores (2018), Jamil et al (2020) and Kurniawan et al (2021), where an internal model with multiple periodic generators was used. The HORC keeps the sampling time T s remain fixed, but the use of multiple periodic generators increases the RC gains at the frequencies around the harmonics.…”

Section: Introductionmentioning

confidence: 99%

Design and analysis of higher-order repetitive sliding mode controller for uncertain linear systems with time-varying periodic disturbances

Kurniawan

Wang

Septanto³

et al. 2023

Transactions of the Institute of Measurement and Control

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In this article, a discrete-time design of higher-order repetitive sliding mode controller (HO-RSMC) for uncertain linear systems with time-varying periodic disturbances is presented. In the design, a higher-order repetitive control (HORC) is employed to significantly suppress the periodic exogenous disturbance with period variation. The HORC-based control system offers a slow transient response due to the multiple control delays. Moreover, since the system stability is easily affected by model uncertainties, an output-feedback sliding mode controller (SMC) is added to overcome the shortcomings of HORC. Stability analysis of the resulting closed-loop system is rigorously provided. The simulation results and comparison studies confirm the effectiveness and good performance of the proposed design.

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On the synthesis of a stable and causal compensator for discrete-time high-order repetitive control systems

Cited by 9 publications

References 20 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

Kajian Identifikasi Model Eksperimen pada Kontrol Kecepatan Motor DC

Design and analysis of higher-order repetitive sliding mode controller for uncertain linear systems with time-varying periodic disturbances

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