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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/19063
Title: Neural network based optimal position/force control for constrained robot manipulators
Authors: Sukavanam N.
Panwar V.
Published in: Proceedings of 2nd Indian International Conference on Artificial Intelligence, IICAI 2005
Abstract: In this paper 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 to 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 dynamic model uncertainties are compensated with a feedforward neural network. The FFNN requires no preliminary off-line training and is trained with on-line weight tuning algorithms that guarantee small errors and bounded control signals. The application of the derived control law is demonstrated through simulation with a two-arm robot manipulator to track a circular constrained surface while applying the desired force on the surface. Copyright © IICAI 2005.
Citation: Proceedings of 2nd Indian International Conference on Artificial Intelligence, IICAI 2005, (2005), 364- 383. Pune
URI: http://repository.iitr.ac.in/handle/123456789/19063
Issue Date: 2005
Keywords: Bounded controls
Constrained motion
Constrained robots
Control laws
Differential Riccati equation
Hamilton jacobi bellman
Hybrid position
Model uncertainties
Off-line training
Optimal feedback control law
Optimal position
Quadratic optimization
Robot manipulator
Sliding modes
State variables
State-space models
Tuning algorithm
Unconstrained motion
Artificial intelligence
Control theory
Dynamic models
Feedforward neural networks
Flexible manipulators
Industrial robots
Modular robots
Riccati equations
Robot applications
Uncertainty analysis
Optimization
ISBN: 0972741216; 9780972741217
Author Scopus IDs: 12804420600
17435058900
Author Affiliations: Sukavanam, N., Department of Mathematics, Indian Institute of Technology, Roorkee 247667, India
Panwar, V., Department of Mathematics, Indian Institute of Technology, Roorkee 247667, India
Corresponding Author: Sukavanam, N.; Department of Mathematics, Indian Institute of Technology, Roorkee 247667, India; email: nsukvfma@iitr.ernet.in
Appears in Collections:Conference Publications [MA]

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