2023
DOI: 10.1109/access.2023.3280979
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A Novel Iterative Second-Order Neural-Network Learning Control Approach for Robotic Manipulators

Abstract: Iterative Learning Control (ILC) is known as a high-accuracy control strategy for repetitive control missions of mechatronic systems. However, applying such learning controllers for robotic manipulators to result in excellent control performances is now a challenge due to unstable behaviors coming from nonlinearities, uncertainties and disturbances in the system dynamics. To tackle this challenge, in this paper, we present a novel proportional-derivative iterative second-order neural-network learning control (… Show more

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Cited by 3 publications
(1 citation statement)
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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%
“…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%