A fuzzy-based impedance control for force tracking in unknown environment

Shen, Yichao; Lu, Yan; Zhuang, Chungang

doi:10.1007/s12206-022-0936-6

Cited by 10 publications

(6 citation statements)

References 30 publications

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“…On the other hand, variable stiffness methods [20] deal with an online adaption law of the stiffness term, as a function of the force error. In [21], it is instead determined by a fuzzy-logic law; additionally, [22] proposes a fractional-order control law, instead of the classical second-order impedance controller.…”

Section: B Related Work and Motivationmentioning

confidence: 99%

“…The most challenging aspect of interaction control lies in selecting an appropriate model for the environment, whose inevitable inaccuracies constitute the aspect the aforementioned works seek to compensate: in the literature, the most common choice is adopting a linear spring model [8], [12], [17], [18], [20], [21], [22], [23], [24], [26], [27], [28] but, as specified in [8], [29], [30], it constitutes, in general, an approximation. To cope with this inherent difficulty, recent research trends lean towards leveraging AI-based and data-driven methods to devise control laws from data, rather than relying on modelbased strategies.…”

Section: B Related Work and Motivationmentioning

confidence: 99%

See 1 more Smart Citation

Optimized Residual Action for Interaction Control with Learned Environments

Petrone,

Puricelli,

Ferrentino

et al. 2024

Preprint

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In industrial settings, robotic tasks often require interaction with various objects, necessitating compliant manipulation to prevent damage while accurately tracking reference forces. To this aim, interaction controllers are typically employed, but they need either human tinkering for parameter tuning or precise environmental modeling. Both these aspects can be problematic, as the former is a time-consuming procedure, and the latter is unavoidably affected by approximations, hence being prone to failure during the actual application. Addressing these challenges, current research focuses on devising high-performance force controllers. Along this line, this work introduces ORACLE (Optimized Residual Action for Interaction Control with Learned Environments), a novel force control approach. Utilizing neural networks, ORACLE predicts robot-environment interaction forces, which are then used in an optimal residual action controller to locally correct actions from a base force controller, minimizing the expected force-tracking error. Tested on a real Franka Emika Panda robot, ORACLE demonstrates superior force tracking performance compared to state-of-the-art controllers, with a short setup time.

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Section: B Related Work and Motivationmentioning

confidence: 99%

Section: B Related Work and Motivationmentioning

confidence: 99%

Optimized Residual Action for Interaction Control with Learned Environments

Petrone,

Puricelli,

Ferrentino

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Hongyao Zhang et al developed a hybrid force/position anti-disturbance control strategy based on fuzzy PID control to improve the grinding quality of aerospace blades [9]. Yichao Shen et al proposed a fuzzy-based adaptive impedance control that can grind or polish workpieces of different materials with constant contact force [10]. The neural network algorithm is robust, fault-tolerant, and can adequately approximate any complex nonlinear relationship.…”

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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“…Liu et al (2022) used a fuzzy variable impedance control strategy to optimize impedance parameters in different motion states. Shen et al (2022) adjust the impedance parameters, such as damping and stiffness, by fuzzy controller. Zhu (2016) suggested adjusting impedance parameters by fuzzy controller in the field of hexapod robot.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Zhu

et al. 2023

Journal of Field Robotics

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As a key technology of robot grinding, force control has great influence on grinding effects. Based on the traditional impedance control, a position‐based force tracking adaptive impedance control strategy is proposed to improve the grinding quality of aeroengine complex curved parts, which considers the stiffness damping environmental interaction model, modifies the reference trajectory by a Lyapunov‐based approach to realize the adaptive grinding process. In addition, forgotten Kalman filter based on six‐dimensional force sensor is used to denoise the force information and a three‐step gravity compensation process including static base value calculation, dynamic zero update and contact force real‐time calculation is proposed to obtain the accurate contact force between tool and workpiece in this method. Then, to verify the effectiveness of the proposed method, a simulation experiment which including five different working conditions is conducted in MATLAB, and the experiment studying the deviation between the reference trajectory and the actual position is carried out on the robot grinding system. The results indicate that the position‐based force tracking adaptive impedance control strategy can quickly respond to the changes of environmental position, reduce the fluctuation range of contact force in time by modifying the reference trajectory, compensate for the defect of the steady‐state error of the traditional impedance control strategy and improve the surface consistency of machined parts.

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A fuzzy-based impedance control for force tracking in unknown environment

Cited by 10 publications

References 30 publications

Optimized Residual Action for Interaction Control with Learned Environments

Optimized Residual Action for Interaction Control with Learned Environments

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

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

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