Feedback-Based Iterative Learning Design and Synthesis With Output Constraints for Robotic Manipulators

Sebastian, Gijo; Tan, Ying; Oetomo, Denny; Mareels, Iven

doi:10.1109/lcsys.2018.2842186

Cited by 21 publications

(7 citation statements)

References 23 publications

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“…This work extends our previous work [26] to address time-varying output constraints. A soft constraint of ILC has been added to ensure that the control input is uniformly bounded over the iteration-domain.…”

Section: Motivation and Control Objectivesupporting

confidence: 61%

“…In our previous work [26], the time-invariant output constraints were handled by using a full state-feedback designed using barrier-function based CEF. It has been shown that the output constraints can be satisfied with the bounded energy of feed-forward control input, leading to the possibility of having unbounded input signals.…”

Section: Motivation and Control Objectivementioning

confidence: 99%

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Input and Output Constraints in Iterative Learning Control Design for Robotic Manipulators

et al. 2018

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Motivated by the safety requirement of rehabilitation robotic systems for after stroke patients, this paper handles position or output constraints in robotic manipulators when the patients repeat the same task with the robot. In order to handle output constraints, if all state information is available, a state feedback controller can ensure that the output constraints are satisfied while iterative learning control (ILC) is used to learn the desired control input through iterations. By incorporating the feedback control using barrier Lyapunov function with feed-forward control (ILC) carefully, the convergence of the tracking error, the boundedness of the internal state, the boundedness of input signals can be guaranteed along with the satisfaction of the output constraints over iterations. The effectiveness of the proposed controller is demonstrated using simulations from the model of EMU, a rehabilitation robotic system.

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Section: Motivation and Control Objectivesupporting

confidence: 61%

Section: Motivation and Control Objectivementioning

confidence: 99%

Input and Output Constraints in Iterative Learning Control Design for Robotic Manipulators

et al. 2018

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“…According to (42), the signs of z i and ̇zi are opposite, and the states z i will converge to the origin ultimately. 3) When s = 0, the other situation is 𝜑 < 0 and φ > 0, which is not obtained because of…”

Section: Design Without Actuator Faults and Input Saturationmentioning

confidence: 99%

“…2) The existing methods based on the traditional BLF cannot be implemented whenever the limited state violates the prescribed constraints initially [41][42][43]. In this paper, the restriction is removed by introducing a novel sliding mode surface.…”

Section: Introductionmentioning

confidence: 99%

“…Even if some unknown actuator breaks down partially, some pre‐defined control performance is still satisfied. Due to coupled situations, special emphases are made to the computational complexity. 2)The existing methods based on the traditional BLF cannot be implemented whenever the limited state violates the prescribed constraints initially [41–43]. In this paper, the restriction is removed by introducing a novel sliding mode surface.…”

Section: Introductionmentioning

confidence: 99%

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Tracking control of a robotic system with deferred constraints and actuator faults

Zhang

Kong

Zhang

et al. 2021

IET Control Theory & Appl

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This paper proposes an adaptive sliding mode tracking control for a robotic manipulator to guarantee a deferred performance under input non-linearities and external perturbations. Control inputs of the robotic manipulator are entrapped into saturation and failures simultaneously. A common method to deal with output constraints is based on the barrier Lyapunov function, which requires the system states to be within the prescribed bounds initially; an error-shifting transformation is utilized and barrier functions are integrated into sliding model control to remove the unreasonable requirement and to realize a finite time convergence result collectively. Furthermore, the actuator faults are accommodated without any prior knowledge. Simulation examples are provided to verify the effectiveness of the proposed scheme. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Regulation of Space Manipulators with Free-Swinging Joint Failure Based on Iterative Learning Control

Jia

Chen

et al. 2021

Intelligent Robotics and Applications

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Feedback-Based Iterative Learning Design and Synthesis With Output Constraints for Robotic Manipulators

Cited by 21 publications

References 23 publications

Input and Output Constraints in Iterative Learning Control Design for Robotic Manipulators

Input and Output Constraints in Iterative Learning Control Design for Robotic Manipulators

Tracking control of a robotic system with deferred constraints and actuator faults

Regulation of Space Manipulators with Free-Swinging Joint Failure Based on Iterative Learning Control

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