Dual Adaptive Dynamic Control of Mobile Robots Using Neural Networks

This paper proposes two novel dual adaptive neural control schemes for the dynamic control of nonholonomic mobile robots. The two schemes are developed in discrete time, and the robot's nonlinear dynamic functions are assumed to be unknown. Gaussian radial basis function and sigmoidal multilaye...

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Bibliographic Details
Published in:IEEE transactions on cybernetics 2009-02, Vol.39 (1), p.129-141
Main Authors: Bugeja, M.K., Fabri, S.G., Camilleri, L.
Format: Article
Language:English
Subjects:
Adaptive control
Adaptive control systems
Algorithms
Analysis of Variance
Artificial Intelligence
Biomechanical Phenomena
Computer Simulation
Dual adaptive control
Dynamical systems
Function approximation
Mobile robots
Monte Carlo Method
Monte Carlo methods
Multi-layer neural network
Multilayer perceptrons
Neural networks
Neural Networks (Computer)
Nonlinear Dynamics
Normal Distribution
Parameter estimation
Programmable control
Robot control
Robotics - methods
Robots
Statistics, Nonparametric
stochastic control
Stochastic Processes
Studies
Uncertainty
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Summary:This paper proposes two novel dual adaptive neural control schemes for the dynamic control of nonholonomic mobile robots. The two schemes are developed in discrete time, and the robot's nonlinear dynamic functions are assumed to be unknown. Gaussian radial basis function and sigmoidal multilayer perceptron neural networks are used for function approximation. In each scheme, the unknown network parameters are estimated stochastically in real time, and no preliminary offline neural network training is used. In contrast to other adaptive techniques hitherto proposed in the literature on mobile robots, the dual control laws presented in this paper do not rely on the heuristic certainty equivalence property but account for the uncertainty in the estimates. This results in a major improvement in tracking performance, despite the plant uncertainty and unmodeled dynamics. Monte Carlo simulation and statistical hypothesis testing are used to illustrate the effectiveness of the two proposed stochastic controllers as applied to the trajectory-tracking problem of a differentially driven wheeled mobile robot.
ISSN:1083-4419
2168-2267
1941-0492
2168-2275
DOI:10.1109/TSMCB.2008.2002851