Angle Error-Tracking Iterative Learning Control for Pneumatic Artificial Muscle System

Yang, Qing; Wang, Wei; Zhang, Yuntao; Yan, Qingdong; Cai, Jianping

doi:10.1109/access.2021.3133864

Cited by 4 publications

(1 citation statement)

References 51 publications

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“…Tracking control problems have been found in many biomedical applications such as wearable lower-limb system [1], [2], magnetoactive soft continuum robots (MSCRs) [3], pneumatic artificial muscles [4], [5], [6], [7], mimic the intact human knee profile [8], simple magnetically actuated pivot walker [9]. Similarly, tracking control problems also appeared on robotic arm [10], multi-input multi-output (MIMO) drug infusion [11], grasping ability hands robot [12], leg hydraulic drive system (LHDS) [13], pneumatic soft robotic actuator [14], and functional electrical stimulation (FES) [15], [16].…”

Section: Introductionmentioning

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: Introductionmentioning

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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Initial-Rectification Barrier Iterative Learning Control for Pneumatic Artificial Muscle Systems With Nonzero Initial Errors and Iteration-Varying Reference Trajectories

Yu¹,

Lai²

2022

IEEE Access

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Pneumatic artificial muscle actuators possess great potential in compliant rehabilitation devices since they are flexible and lightweight. The inherent high nonlinearities, uncertainties, hysteresis and time-varying characteristics in pneumatic artificial muscle systems brings much challenge for accurate system modeling and controller design. The angle tracking problem based on iterative learning control technology is considered in this work. This research proposes a new initial-rectification adaptive iterative learning control scheme for a pneumatic artificial muscle-actuated device with nonzero initial errors and iteration-varying reference trajectories. A barrier Lyapunov function is used to deal with the constraint requirement. A new initial rectification construction method is given to solve the nonzero initial error problem. Nonparametric uncertainties in the system are approximated by using a neural network, whose optimal weight is estimated by using difference learning method. As the iteration number increases, the system states of angle and angular velocity can accurately track the reference trajectories over the whole interval, respectively. In the end, the simulation results show excellent trajectory tracking performance of the iterative learning controller even if the reference trajectories are non-repetitive over the iteration domain.

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Designing a Neural Network-Based Controller: A Practical Educational Tool for Data-Driven Control Courses

Romero,

Gaytán,

Chong-Quero

et al. 2023

2023 World Engineering Education Forum - Global Engineering Deans Council (WEEF-GEDC)

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Angle Error-Tracking Iterative Learning Control for Pneumatic Artificial Muscle System

Cited by 4 publications

References 51 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

Initial-Rectification Barrier Iterative Learning Control for Pneumatic Artificial Muscle Systems With Nonzero Initial Errors and Iteration-Varying Reference Trajectories

Designing a Neural Network-Based Controller: A Practical Educational Tool for Data-Driven Control Courses

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