Design of Open-Closed-Loop Iterative Learning Control With Variable Stiffness for Multiple Flexible Manipulator Robot Systems

Dong, Jian; He, Bin; Ma, Ming; Zhang, Chenghong; Li, Gang

doi:10.1109/access.2019.2898266

Cited by 16 publications

(5 citation statements)

References 32 publications

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“…In some industries, such as logistics warehouses, steel ball factories, and automobile parts factories, robots transport jobs between machines for reducing costs and worker requirements and then greatly improve the throughput in factories. He et al 31 and Dong et al 32 studied on the dynamics of robot system and the accuracy of robot control system. In robot scheduling and path planning, real-world flowshop systems often involve uncertainty; however, various types of metaheuristics have been developed for solving realistic optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Solving robotic distributed flowshop problem using an improved iterated greedy algorithm

Gao

et al. 2019

International Journal of Advanced Robotic Systems

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In this study, we propose an improved iterated greedy algorithm for solving the distributed permutation flowshop problem, where there is a single robot in each factory and the makespan needs to be minimized. In the problem considered, the robot is used to transfer each job from the predecessor machine to the successor machine. A blocking constraint between machines is considered, thus jobs should remain on the completed machine while waiting for the robot. The loading and unloading times are considered and different for all of the jobs conducted by the robot, and the deteriorating time is also considered. In the proposed algorithm, first, four types of neighborhood structures are developed. Then, the simulated annealing algorithm is embedded in the proposed algorithm to enhance the exploration abilities. Furthermore, a problem-specific destruction and construction strategies are investigated. Finally, several realistic instances were generated to test the proposed algorithm, and its competitive performance was verified based on detailed experimental comparisons.

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Section: Introductionmentioning

confidence: 99%

Solving robotic distributed flowshop problem using an improved iterated greedy algorithm

Gao

et al. 2019

International Journal of Advanced Robotic Systems

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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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“…Lots of literatures improved the trajectory tracking accuracy and decreased vibration level of flexible manipulator by adjusting the joint driving torque (Alandoli et al, 2021; Zhao et al, 2021). The existing vibration control techniques based on the joint servo motor can be broadly classified into two categories, namely, feedback control and feed-forward control (Dong et al, 2019; Habibi et al, 2019). Input command shaping is a typical feed-forward control strategy in which the expected input is convolved with a series of impulse sequence, so as to avoid activating the vibration modes and eliminate the residual vibration of the flexible manipulator (Cole and Wongratanaphisan, 2013).…”

Section: Introductionmentioning

confidence: 99%

Hybrid control of piezoelectric flexible manipulator based on Volterra filtered-xLMS algorithm

Yang

Zhang

2021

Journal of Vibration and Control

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To suppress the nonlinear vibration of the flexible manipulator during motion, this article presents a hybrid control strategy based on a servo motor and a piezoelectric actuator. The dynamic model of the piezoelectric flexible manipulator is established first. To realize the trajectory tracking, a proportional derivative control method is used to schedule the control torque. Because the Volterra filter can approximate the nonlinear system model, a Volterra filtered-xLMS algorithm based on a second-order Volterra filter structure is proposed, by which the active nonlinear vibration control of flexible link is realized. Simulation results show that the proposed Volterra filtered-xLMS algorithm can not only make use of the advantages of the classical filtered-xLMS algorithm but also solve the problem of effective modeling of nonlinear secondary path. The proposed hybrid control strategy based on Volterra filtered-xLMS algorithm and proportional derivative control algorithm can improve the position accuracy of joint and effectively suppress the vibration response of the nonlinear flexible link. A piezoelectric flexible manipulator with PZT (lead zirconate titanate) sensor and actuator is designed to demonstrate the validity and efficiency of the proposed method by experiments. Experiment results demonstrate that the attenuation time of vibration response is reduced from 5 s to 1.5 s, the vibration response at the first-order frequency is reduced by 60%, and the proposed methodology has an important advantage in application of active vibration control of piezoelectric flexible manipulator.

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Design of Open-Closed-Loop Iterative Learning Control With Variable Stiffness for Multiple Flexible Manipulator Robot Systems

Cited by 16 publications

References 32 publications

Solving robotic distributed flowshop problem using an improved iterated greedy algorithm

Solving robotic distributed flowshop problem using an improved iterated greedy algorithm

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

Hybrid control of piezoelectric flexible manipulator based on Volterra filtered-xLMS algorithm

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