Asymptotic tracking of uncertain systems with continuous control using adaptive bounding

Authors:
Vahram Stepanyan;Andrew Kurdila
Affiliations:
Mission Critical Technologies Inc., NASA Ames Research Center, Moffett Field, CA;Department of Mechanical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA
Venue:
IEEE Transactions on Neural Networks
Year:
2009

Citing 7
Cited 0

Adaptive control: stability, convergence, and robustness

Adaptive control: stability, convergence, and robustness
Universal approximation using radial-basis-function networks

Neural Computation
Feedback linearization using neural networks

Automatica (Journal of IFAC)
Neural Networks: A Comprehensive Foundation (3rd Edition)

Neural Networks: A Comprehensive Foundation (3rd Edition)
Design of Robust Adaptive Controllers for Nonlinear Systems with Dynamic Uncertainties

Automatica (Journal of IFAC)
Multilayer neural-net robot controller with guaranteed tracking performance

IEEE Transactions on Neural Networks
High-order neural network structures for identification of dynamical systems

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.01

Visualization

Abstract

This paper presents a robust adaptive control design method for a class of multiple-input-multiple-output uncertain nonlinear systems in the presence of parametric and nonparametric uncertainties and bounded disturbances. Using the approximation properties of the unknown continuous nonlinearities and the adaptive bounding technique, the developed controller achieves asymptotic convergence of the tracking error to zero, while ensuring boundedness of parameter estimation errors. The algorithm does not assume the knowledge of any bound on the unknown quantities in designing the controller. It is based on an integral technique involving the filtered tracking error and produces a continuous control. Theoretical developments are illustrated via simulation results.