Computer-controlled systems: theory and design (2nd ed.)
Computer-controlled systems: theory and design (2nd ed.)
Dual pole-placement controller with direct adaptation
Automatica (Journal of IFAC)
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Functional Adaptive Control: An Intelligent Systems Approach
Functional Adaptive Control: An Intelligent Systems Approach
Robust Practical Point Stabilization of a Nonholonomic Mobile Robot Using Neural Networks
Journal of Intelligent and Robotic Systems
Rescue robotics for homeland security
Communications of the ACM - Homeland security
Discovering Statistics Using SPSS
Discovering Statistics Using SPSS
Adaptive Approximation Based Control: Unifying Neural, Fuzzy and Traditional Adaptive Approximation Approaches (Adaptive and Learning Systems for Signal Processing, Communications and Control Series)
Adaptive control for mobile robot using wavelet networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Brief paper: Dual adaptive control of nonlinear stochastic systems using neural networks
Automatica (Journal of IFAC)
Paper: Dual adaptive control of chip refiner motor load
Automatica (Journal of IFAC)
Stochastic iterative dynamic programming: a Monte Carlo approach to dual control
Automatica (Journal of IFAC)
Control of a nonholonomic mobile robot using neural networks
IEEE Transactions on Neural Networks
Motion control and trajectory tracking control for a mobile robot via disturbance observer
WSEAS TRANSACTIONS on SYSTEMS
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Information Sciences: an International Journal
Fuzzy Neural Network Control for Robot Manipulator Directly Driven by Switched Reluctance Motor
International Journal of Cognitive Informatics and Natural Intelligence
Global tracking control of a wheeled mobile robot using RBF neural networks
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
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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.