Position‐based force tracking adaptive impedance control strategy for robot grinding complex surfaces system

Li, Lun; Wang, Zhengjia; Zhu, Guang; Zhao, Jibin

doi:10.1002/rob.22169

Cited by 8 publications

(1 citation statement)

References 34 publications

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“…Therefore Guilian Wang et al proposed an adaptive variable impedance control algorithm with pre-PD adjustment to discover the soft grinding and polishing of complex surfaces and the stability and convergence of the force control algorithm were proved by comparative experiments [5]. Lun Li, based on traditional impedance control, proposed a position-based force-tracking adaptive impedance control strategy to improve the complex surface grinding quality of aero engine parts grinding quality of aero-engine [6].…”

Section: Introductionmentioning

confidence: 99%

Constant Force Grinding Controller for Robots Based on Soft-Actor-Critic Optimal Parameter Finding Algorithm

Rei,

Wang,

Chen

et al. 2024

Preprint

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Since conventional PID (Proportional-Integral-Derivative) controllers hardly control the robot to stabilize for constant force grinding under changing environmental conditions, it is necessary to add a compensation term to conventional PID controllers. An optimal parameter finding algorithm based on SAC(Soft-Actor-Critic) is proposed to solve the problem that the compensation term parameters are difficult to obtain, including training state action and normalization preprocessing, reward function design, and targeted deep neural network design. The algorithm is used to find the optimal controller compensation term parameters and applied to the PID controller to complete the compensation through the inverse kinematics of the robot to achieve constant force grinding control. To verify the algorithm's feasibility, a simulation model of a grinding robot with sensible force information is established, and the simulation results show that the controller trained with the algorithm can achieve constant force grinding of the robot. Finally, the robot constant force grinding experimental system platform is built for testing, which verifies the control effect of the optimal parameter finding algorithm on the robot constant force grinding and has specific environmental adaptability.

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Section: Introductionmentioning

confidence: 99%

Constant Force Grinding Controller for Robots Based on Soft-Actor-Critic Optimal Parameter Finding Algorithm

Rei,

Wang,

Chen

et al. 2024

Preprint

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Key technology of temporary support robot for rapid excavation of coal mine roadway

Zhang,

Ma,

Mao

et al. 2024

Journal of Field Robotics

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The coal industry has long been troubled by the imbalance between mining and tunneling, and between excavating and support. The main cause of this problem is the inability to perform excavating and permanent support operations in parallel. Additionally, the limited space near the tunneling face hampers the efficiency of permanent support. Temporary support is an effective method to ensure the stability of the surrounding rock and expand the space for parallel operations between excavating and permanent support activities. This work provides a brief overview of the current research status on temporary support, emphasizing that the key to achieving safe, efficient, and rapid excavation lies in the development of temporary support robots. To meet the development needs of temporary support robots, three key technologies are proposed: the construction of a coupling model between the robot and the surrounding rock, spatial layout optimization of the rapid advancement system, and adaptive control of the robot. This work details the methods and approaches for constructing the coupling model, the elements of system spatial layout optimization, and the methods and strategies for the robot's adaptive control. Our team successfully tested the shield robot system at Xiaobaodang Mining Company, verifying the feasibility of these key technologies.

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Adaptive Position/Force Controller Design Using Fuzzy Neural Network and Stiffness Estimation for Robot Manipulator

Tseng,

Jiang,

Lee

2024

Int. J. Fuzzy Syst.

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This paper proposes an adaptive hybrid position/force control approach using fuzzy neural networks (FNNs) for a robot manipulator with joint friction compensation. The dynamics model and system uncertainties are estimated by FNNs. For force tracking control, an adaptive impedance controller is employed with an online stiffness estimator, wherein the stiffness of the contacted environment is estimated using a gradient descent algorithm. The adaptive update laws of the FNNs and the stability of the controller are obtained using the Lyapunov stability theorem. Finally, the proposed adaptive hybrid controller is implemented on the AR605, a 6-axis articulated robot manipulator manufactured by the Industrial Technology Research Institute (ITRI). The effectiveness and robustness of the proposed control strategies are verified by the simulation and experimental results.

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Position‐based force tracking adaptive impedance control strategy for robot grinding complex surfaces system

Cited by 8 publications

References 34 publications

Constant Force Grinding Controller for Robots Based on Soft-Actor-Critic Optimal Parameter Finding Algorithm

Constant Force Grinding Controller for Robots Based on Soft-Actor-Critic Optimal Parameter Finding Algorithm

Key technology of temporary support robot for rapid excavation of coal mine roadway

Adaptive Position/Force Controller Design Using Fuzzy Neural Network and Stiffness Estimation for Robot Manipulator

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