Title :
Asymptotic Tracking of Uncertain Systems With Continuous Control Using Adaptive Bounding
Author :
Stepanyan, Vahram ; Kurdila, Andrew
Author_Institution :
NASA Ames Res. Center, Mission Critical Technol. Inc., Moffett Field, CA, USA
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.
Keywords :
MIMO systems; adaptive control; approximation theory; control nonlinearities; control system synthesis; convergence of numerical methods; filtering theory; neurocontrollers; nonlinear control systems; parameter estimation; robust control; tracking; uncertain systems; MIMO nonlinear uncertain system; adaptive bounding technique; asymptotic convergence; continuous nonlinearity; control signal; disturbance rejection; filtered tracking error; integral technique; multiple-input-multiple-output system; neural network approximation property; parameter estimation error; robust adaptive control design method; Asymptotic tracking; disturbance rejection; neural network approximation; nonlinear uncertain systems;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2009.2023214