Design of Optimal Hybrid Position/Force Controller for  a Robot Manipulator Using Neural Networks

Panwar, Vikas; Sukavanam, N.

doi:https://doi.org/10.1155/2007/65028

Mathematical Problems in Engineering

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Research Article | Open Access

Volume 2007 | Article ID 065028 | https://doi.org/10.1155/2007/65028

Design of Optimal Hybrid Position/Force Controller for a Robot Manipulator Using Neural Networks

Vikas Panwar¹and N. Sukavanam²

Academic Editor: Semyon M. Meerkov

Received21 Nov 2004

Revised25 Apr 2006

Accepted21 Nov 2006

Published21 Feb 2007

Abstract

The application of quadratic optimization and sliding-mode approach is considered for hybrid position and force control of a robot manipulator. The dynamic model of the manipulator is transformed into a state-space model to contain two sets of state variables, where one describes the constrained motion and the other describes the unconstrained motion. The optimal feedback control law is derived solving matrix differential Riccati equation, which is obtained using Hamilton Jacobi Bellman optimization. The optimal feedback control law is shown to be globally exponentially stable using Lyapunov function approach. The dynamic model uncertainties are compensated with a feedforward neural network. The neural network requires no preliminary offline training and is trained with online weight tuning algorithms that guarantee small errors and bounded control signals. The application of the derived control law is demonstrated through simulation with a 4-DOF robot manipulator to track an elliptical planar constrained surface while applying the desired force on the surface.

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Copyright

Copyright © 2007 Vikas Panwar and N. Sukavanam. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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