Impedance control and parameter optimization of surface polishing robot based on reinforcement learning

Ding, Yucheng; Zhao, Junchao; Min, Xinpu

doi:10.1177/09544054221100004

Cited by 23 publications

(6 citation statements)

References 14 publications

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“…Building upon the techniques employed to assess a patient's motor functional ability, the determination of actual assistive torque and the specific implementation method of real-time control have emerged as focal points of research in AAN control. These research areas encompass several approaches, such as direct adjustment of assistive force/torque through force control methods [8,9,12], adaptive adjustment of impedance/admittance coefficients to achieve force/position interaction performance [7,13], and intelligent learning algorithms [14,15]. Shawgi Younis et al [7] applied an adaptive inertia-related torque controller.…”

Section: Introductionmentioning

confidence: 99%

Patient’s Healthy-Limb Motion Characteristic-Based Assist-As-Needed Control Strategy for Upper-Limb Rehabilitation Robots

Guo,

Li,

Huang

et al. 2024

Sensors

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The implementation of a progressive rehabilitation training model to promote patients’ motivation efforts can greatly restore damaged central nervous system function in patients. Patients’ active engagement can be effectively stimulated by assist-as-needed (AAN) robot rehabilitation training. However, its application in robotic therapy has been hindered by a simple determination method of robot-assisted torque which focuses on the evaluation of only the affected limb’s movement ability. Moreover, the expected effect of assistance depends on the designer and deviates from the patient’s expectations, and its applicability to different patients is deficient. In this study, we propose a control method with personalized treatment features based on the idea of estimating and mapping the stiffness of the patient’s healthy limb. This control method comprises an interactive control module in the task-oriented space based on the quantitative evaluation of motion needs and an inner-loop position control module for the pneumatic swing cylinder in the joint space. An upper-limb endpoint stiffness estimation model was constructed, and a parameter identification algorithm was designed. The upper limb endpoint stiffness which characterizes the patient’s ability to complete training movements was obtained by collecting surface electromyographic (sEMG) signals and human–robot interaction forces during patient movement. Then, the motor needs of the affected limb when completing the same movement were quantified based on the performance of the healthy limb. A stiffness-mapping algorithm was designed to dynamically adjust the rehabilitation training trajectory and auxiliary force of the robot based on the actual movement ability of the affected limb, achieving AAN control. Experimental studies were conducted on a self-developed pneumatic upper limb rehabilitation robot, and the results showed that the proposed AAN control method could effectively estimate the patient’s movement needs and achieve progressive rehabilitation training. This rehabilitation training robot that simulates the movement characteristics of the patient’s healthy limb drives the affected limb, making the intensity of the rehabilitation training task more in line with the patient’s pre-morbid limb-use habits and also beneficial for the consistency of bilateral limb movements.

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Section: Introductionmentioning

confidence: 99%

Patient’s Healthy-Limb Motion Characteristic-Based Assist-As-Needed Control Strategy for Upper-Limb Rehabilitation Robots

Guo,

Li,

Huang

et al. 2024

Sensors

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“…Many scholars have used reinforcement learning to explore the optimal control strategy; for example, Luo et al ( 2021 ) proposed a method based on Q-learning to optimize online stiffness and damping parameters. Ding et al ( 2023 ) used reinforcement learning to analyze and optimize the impedance parameters. Bogdanovic et al ( 2020 ) used a deep deterministic policy gradient to learn the robot output impedance strategy and the required position in the joint space.…”

Section: Introductionmentioning

confidence: 99%

A study on robot force control based on the GMM/GMR algorithm fusing different compensation strategies

Xiao,

Zhang,

Zhang

et al. 2024

Front. Neurorobot.

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To address traditional impedance control methods' difficulty with obtaining stable forces during robot-skin contact, a force control based on the Gaussian mixture model/Gaussian mixture regression (GMM/GMR) algorithm fusing different compensation strategies is proposed. The contact relationship between a robot end effector and human skin is established through an impedance control model. To allow the robot to adapt to flexible skin environments, reinforcement learning algorithms and a strategy based on the skin mechanics model compensate for the impedance control strategy. Two different environment dynamics models for reinforcement learning that can be trained offline are proposed to quickly obtain reinforcement learning strategies. Three different compensation strategies are fused based on the GMM/GMR algorithm, exploiting the online calculation of physical models and offline strategies of reinforcement learning, which can improve the robustness and versatility of the algorithm when adapting to different skin environments. The experimental results show that the contact force obtained by the robot force control based on the GMM/GMR algorithm fusing different compensation strategies is relatively stable. It has better versatility than impedance control, and the force error is within ~±0.2 N.

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“…An extended state observer based on the robot dynamic model is developed to estimate the contact force in real time, and a new and efficient adaptive filter combining insights of the notch filter with the tracking differentiator is designed to relieve the strong vibration disturbance of torque signals from the eccentrically rotating polisher [12]. In order to solve the problem of how to maintain the stability of the actuator contact force in the robot automatic polishing system, Ding and Zhao [13] proposed a robot impedance control parameter learning algorithm based on reinforcement learning and obtained the optimal impedance parameters through numerical simulation methods. When the external environment changes, traditional impedance control has poor trajectory tracking capabilities and unstable control.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fuzzy Sliding Mode Control and Dynamic Modeling of Flap Wheel Polishing Force Control System

Hong,

Zhou,

2024

Applied Sciences

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Polishing force is one of the key process parameters in the polishing process of blisk blades, and its control accuracy will affect the surface quality and processing accuracy of the workpiece. The contact mechanism between the polishing surface and flap wheel was analyzed, and the calculation model of the polishing force and nonlinear dynamic model of the polishing force control system was established. Considering the influence of friction characteristics, parameter perturbation, and nonlinear dead zone on the control accuracy of the polishing force system, an adaptive fuzzy sliding mode controller (AFSMC) was designed. AFSMC uses a fuzzy system to adaptively approximate the nonlinear function terms in the sliding mode control law, adopts an exponential approach law in the switching control part of the sliding mode control (SMC), and designs the adaptive law for adjustable parameters in the fuzzy system based on the Lyapunov Theorem. Simulation and experimental results show that the designed AFSMC has a fast dynamic response, strong anti-interference ability, and high control accuracy, and it can reduce SMC high-frequency chatter. Polishing experiments show that compared with traditional PID, AFSMC can improve the form and position accuracy of the blade by 42% and reduce the surface roughness by 50%.

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Impedance control and parameter optimization of surface polishing robot based on reinforcement learning

Cited by 23 publications

References 14 publications

Patient’s Healthy-Limb Motion Characteristic-Based Assist-As-Needed Control Strategy for Upper-Limb Rehabilitation Robots

Patient’s Healthy-Limb Motion Characteristic-Based Assist-As-Needed Control Strategy for Upper-Limb Rehabilitation Robots

A study on robot force control based on the GMM/GMR algorithm fusing different compensation strategies

Adaptive Fuzzy Sliding Mode Control and Dynamic Modeling of Flap Wheel Polishing Force Control System

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