Flexible-Time Receding Horizon Iterative Learning Control With Application to Marine Hydrokinetic Energy Systems

Cobb, Mitchell; Reed, James; Wu, Maxwell; Mishra, Kirti D.; Barton, Kira; Vermillion, Chris

doi:10.1109/tcst.2022.3165734

Cited by 4 publications

(4 citation statements)

References 33 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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“…In regard to the algorithm's convergence properties, readers are referred to [29] where under relatively restrictive assumptions on the mathematical nature of the performance index and variability in the environment, it was proven that the estimated response surface would converge to the true response surface, and the basis parameters would converge to a set containing the optimal basis parameters. Additionally, in [20], it was demonstrated that even when the previously mentioned assumptions were loosened, convergence was still achieved.…”

Section: Higher-level Iterative Learning Controller (Ilc)mentioning

confidence: 99%

“…The ILC algorithm used in this work is responsible for optimizing the path geometry for each kite design to generate a flight efficiency map. This algorithm is described in [20], and a summary is included here for self-containment. The goal of this ILC algorithm is to maximize an economic objective, which for the kite system is the lap-averaged power production, by altering a set of parameters b that define the path's shape.…”

Section: Higher-level Iterative Learning Controller (Ilc)mentioning

confidence: 99%

“…To remedy the limitations of the nested sequential approach of [18], the present paper details a fully nested formulation that is nevertheless computationally efficient. To remedy the non-optimality of the CPF from [18], the present paper leverages an economic iterative learning control (ILC) strategy first presented in [20], which iteratively updates the control parameters that define the kite's flight path, in pursuit of maximizing the lap-averaged power. Furthermore, using the fully nested formulation, the present paper examines the Pareto front traced through the variation of the fixed power requirement in a single-objective mass minimization.…”

Section: Introductionmentioning

confidence: 99%

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Combined Plant and Controller Optimization of an Underwater Energy Harvesting Kite System

Perkins¹,

Beknalkar²,

Reed³

et al. 2022

Preprint

Self Cite

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This paper presents the formulation and results for a control-aware optimization of the combined geometric and structural design of an energy-harvesting underwater kite. Because kite-based energy-harvesting systems, both airborne and underwater, possess strong coupling between closed-loop flight control, geometric design, and structural design, consideration of all three facets of the design within a single co-design framework is highly desirable. However, while prior literature has addressed one or two attributes of the design at a time, the present work constitutes the first comprehensive effort aimed at addressing all three. In particular, focusing on the goals of power maximization and mass minimization, we present a co-design formulation that fuses a geometric optimization tool, structural optimization tool, and closed-loop flight efficiency map. The resulting integrated co-design tool is used to address two mathematical optimization formulations that exhibit subtle differences: a Pareto optimal formulation and a dual-objective formulation that focuses on a weighted power-to-mass ratio as the techno-economic metric of merit. Based on the * Address all correspondence for other issues to this author.resulting geometric and structural designs, using a mediumfidelity closed-loop simulation tool, the proposed formulation is shown to achieve more than three times the powerto-mass ratio of a previously published, un-optimized benchmark design.

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Economic Cyclic Learning for Longitudinal Control of an Airborne Wind Energy System

Reed,

Vermillion

2023

2023 IEEE Conference on Control Technology and Applications (CCTA)

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Flexible-Time Receding Horizon Iterative Learning Control With Application to Marine Hydrokinetic Energy Systems

Cited by 4 publications

References 33 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

Combined Plant and Controller Optimization of an Underwater Energy Harvesting Kite System

Economic Cyclic Learning for Longitudinal Control of an Airborne Wind Energy System

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