Variable Admittance Control for Robotic Contact Force Tracking in Dynamic Environment Based on Reinforcement Learning

Zhou, Yufei; Li, Tianyu; Cui, Jingkai; Li, Yanhui; Zhu, Ming

doi:10.1109/rcar54675.2022.9872292

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…In order to validate the effectiveness of the proposed strategy, we conducted comparative tests on a two-degrees-of-freedom robotic arm model in two aspects (robustness test and force tracking test) against four control algorithms: traditional impedance control (CIC), adaptive hybrid impedance control (AIC) [20], adaptive variable impedance control (VIC) [22], and the fractional-order PID adaptive impedance (FOAIC) control proposed in this paper. The common parameters of these four algorithms remained consistent across various simulation environments.…”

Section: Algorithm Simulationmentioning

confidence: 99%

“…However, the optimization algorithm was performed offline and did not yield a satisfactory adaptation to the environment. Zhou Y et al [22] proposed a variable impedance control algorithm that achieved superior force tracking control when compared to traditional impedance control. However, the end effector exhibited significant overshoot when in contact with the environment, potentially leading to equipment damage.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Impedance Control for Force Tracking in Manipulators Based on Fractional-Order PID

Gu,

Huang

2023

Applied Sciences

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Force tracking control in robot arms has been widely used in many industrial applications, particularly in tasks involving end effectors and environmental contact, such as grinding, polishing, and other similar operations. However, these environments are not always precisely known. In order to address the force tracking control problem in unknown environments, this paper proposes a fractional-order PID adaptive impedance control strategy based on traditional impedance control. The unknown environmental information is estimated online using the adaptive impedance control algorithm, and the estimated parameters are used to generate reference trajectories to reduce force tracking errors. Fractional-order PID control is then introduced into the system to improve the control performance of the system model, and the theoretical proof of strategy stability is conducted. Finally, a comparison of four strategies was conducted through simulations: traditional impedance control, adaptive hybrid impedance control, adaptive variable impedance control, and the fractional-order PID impedance control proposed in this paper. The simulation results demonstrate that the strategy proposed in this paper exhibits robustness, virtually eliminates overshoot, and enhances response speed. In contrast, both adaptive hybrid impedance control and adaptive variable impedance control exhibit approximately 30% to 45% overshoot during interactions with the environment. Furthermore, in terms of force tracking error, the proposed strategy in this paper outperforms the above two strategies by approximately 29% to 60%, achieving excellent force tracking control performance.

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Section: Algorithm Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%