Title :
Adaptive control of first-order nonlinear systems with reduced knowledge of the plant parameters
Author :
Brogliato, Bernard ; Lozano, Rogelio
Author_Institution :
URA CNRS 228, Lab. d´´Automatique de Grenoble, France
fDate :
8/1/1994 12:00:00 AM
Abstract :
This paper presents an adaptive control strategy for a class of first-order nonlinear systems of the form x˙=θ1* Tf(x)+θ2*Tg(x), where g(x) is a smooth function, whereas f(x) satisfies sectoricity conditions. θ 1* and θ2* are constant parameter vectors. It is assumed that the system remains controllable for all values of x, but the sign of θ2*Tg(x)(x) is unknown. The proposed adaptive scheme extends ideas previously presented the authors (1992) where the term premultiplying the input was supposed to be constant. The standard least-squares estimates of θ2* are modified using a hysteresis type switching algorithm that enables us to conclude on existence, uniqueness, boundedness and convergence of the solutions of the adaptive closed-loop system
Keywords :
adaptive control; closed loop systems; least squares approximations; nonlinear systems; parameter estimation; adaptive control; closed loop system; constant parameter vectors; first order nonlinear systems; hysteresis type switching algorithm; least squares estimates; parameter estimation; sectoricity conditions; smooth function; Adaptive control; Adaptive systems; Artificial intelligence; Control systems; Differential equations; Hysteresis; Nonlinear control systems; Nonlinear systems; Parameter estimation;
Journal_Title :
Automatic Control, IEEE Transactions on