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Force/position control with bounded actions on a dexterous robotic hand with two-degree-of-freedom fingers

Rojas-García Lina N., Chávez-Olivares César A., Bonilla-Gutiérrez Isela, Mendoza-Gutiérrez Marco O., Ramírez-Cardona Fernando

Biocybernetics and Biomedical Engineering

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2022

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Vol. 42, no. 1

233--246

EN

This paper addresses the problem of controlling a robotic hand in tasks that require precise regulation of the applied force, as well as efficient use of actuators, ensuring that these operate safely within their torque limits. The proposed control structure is composed of generalized saturation functions, which have been widely used for the control of robotic systems that move freely without interacting with the environment; however, in this work they are used to regulate the forces generated by the interaction of a robotic hand and the objects that it can manipulate. The force/position control scheme is also used to validate the design of a robotic hand, whose dynamic modeling was obtained with computer-aided design (SolidWorks) and numerical simulation (Simscape MultibodyTM) tools. The results obtained validate both the effectiveness of the proposed control scheme and the usefulness of the computational tools used to characterize the dynamics of the robotic fingers.

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Dynamic external force feedback loop control of a robot manipulator using a neural compensator - Application to the trajectory following in an unknown environment

Ferguene F., Toumi R.

International Journal of Applied Mathematics and Computer Science

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2009

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Vol. 19, no 1

113-126

EN

Force/position control strategies provide an effective framework to deal with tasks involving interaction with the environment. One of these strategies proposed in the literature is external force feedback loop control. It fully employs the available sensor measurements by operating the control action in a full dimensional space without using selection matrices. The performance of this control strategy is affected by uncertainties in both the robot dynamic model and environment stiffness. The purpose of this paper is to improve controller robustness by applying a neural network technique in order to compensate the effect of uncertainties in the robot model. We show that this control strategy is robust with respect to payload uncertainties, position and environment stiffness, and dry and viscous friction. Simulation results for a three degrees-of-freedom manipulator and various types of environments and trajectories show the effectiveness of the suggested approach compared with classical external force feedback loop structures.

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Neural network parallel force/position control of robot manipulators under environment and robot dynamics uncertainties

Ferguene F., Achour N., Toumi R.

Archives of Control Sciences

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2009

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Vol. 19, no. 1

93-121

EN

The performance of a parallel force/position controller for robot force tracking is affected by the uncertainties in both the robot dynamics and the environment stiffness. This paper aims to improve the controller's robustness by applying the neural network (NN) technique to compensate for the robot dynamics at the input trajectory level and adaptive feed-forward compensation to cope with variations in the contact environment. A NN control technique is applied to a conventional PID force/position parallel control scheme which is composed of a PD action on position loop and a proposed adaptive I (integral) action on the force loop, which allows a complete use of available sensor measurements by operating the control action in a full dimensional space without using selection matrices. Simulation results for a three degrees-of-freedom robot show that highly robust position/force tracking can be achieved in the presence of a full dynamic robot and large environment stiffness uncertainties.