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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

100%

Ferguene F., Toumi R.

International Journal of Applied Mathematics and Computer Science

2009

tom 19

nr 1

113-126

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.

Finite horizon nonlinear predictive control by the Taylor approximation application to robot tracking trajectory

81%

Hedjar R., Toumi R., Boucher P., Dumur D.

International Journal of Applied Mathematics and Computer Science

2005

tom 15

nr 4

527-540

In industrial control systems, practical interest is driven by the fact that today's processes need to be operated under tighter performance specifications. Often these demands can only be met when process nonlinearities are explicitly considered in the controller. Nonlinear predictive control, the extension of well-established linear predictive control to nonlinear systems, appears to be a well-suited approach for this kind of problems. In this paper, an optimal nonlinear predictive control structure, which provides asymptotic tracking of smooth reference trajectories, is presented. The controller is based on a finite-horizon continuous time minimization of nonlinear predicted tracking errors. A key feature of the control law is that its implementation does not need to perform on-line optimization, and asymptotic tracking of smooth reference signal is guaranteed. An integral action is used to increase the robustness of the closed-loop system with respect to uncertainties and parameters variations. The proposed control scheme is first applied to planning motions problem of a mobile robot and, afterwards, to the trajectory tracking problem of a rigid link manipulator. Simulation results are performed to validate the tracking performance of the proposed controller.

Ograniczanie wyników

2 International Journal of Applied Mathematics and Computer Science

2 Toumi R.

1 Boucher P.

1 Dumur D.

1 Ferguene F.

1 Hedjar R.

1 2009

1 2005

Wyniki wyszukiwania

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

Finite horizon nonlinear predictive control by the Taylor approximation application to robot tracking trajectory