6533b854fe1ef96bd12af3ab

RESEARCH PRODUCT

Adaptive output feedback neural network control of uncertain non-affine systems with unknown control direction

Jafar ZareiHamid Reza KarimiMohammad Mahdi Arefi

subject

Lyapunov stabilityAdaptive controlObserver (quantum physics)Artificial neural networkComputer Networks and CommunicationsApplied MathematicsNeural network control; Observer-based control; Uncertain non-affine systems; Unknown gain direction; Control and Systems Engineering; Computer Networks and Communications; Applied Mathematics; Signal ProcessingUnknown gain directionControl engineeringNonlinear controlNonlinear systemNeural network controlExponential stabilityControl and Systems EngineeringControl theorySignal ProcessingObserver-based controlUncertain non-affine systemsMathematics

description

Abstract This paper deals with the problem of adaptive output feedback neural network controller design for a SISO non-affine nonlinear system. Since in practice all system states are not available in output measurement, an observer is designed to estimate these states. In comparison with the existing approaches, the current method does not require any information about the sign of control gain. In order to handle the unknown sign of the control direction, the Nussbaum-type function is utilized. In order to approximate the unknown nonlinear function, neural network is firstly exploited, and then to compensate the approximation error and external disturbance a robustifying term is employed. The proposed controller is designed based on strict-positive-real (SPR) Lyapunov stability theory to ensure the asymptotic stability of the closed-loop system. Finally, two simulation studies are presented to demonstrate the effectiveness of the developed scheme.

yearjournalcountryeditionlanguage
2014-08-01Journal of the Franklin Institute
https://doi.org/10.1016/j.jfranklin.2014.05.006