title: Nonlinear adaptive trajectory tracking using dynamic neural networks.
abstract: In this paper the adaptive nonlinear identification and trajectory tracking are discussed via dynamic neural networks. By means of a Lyapunov-like analysis we determine stability conditions for the identification error. Then we analyze the trajectory tracking error by a local optimal controller. An algebraic Riccati equation and a differential one are used for the identification and the tracking error analysis. As our main original contributions, we establish two theorems: the first one gives a bound for the identification error and the second one establishes a bound for the tracking error. We illustrate the effectiveness of these results by two examples: the second-order relay system with multiple isolated equilibrium points and the chaotic system given by Duffing equation.
authors.affiliation: Departamento de Control Automatico, CINVESTAV-IPN, Mexico D.F., 07360, Mexico.
authors.forename: A S
authors.initials: AS
authors.lastname: Poznyak
authors.forename: W
authors.initials: W
authors.lastname: Yu
authors.forename: E N
authors.initials: EN
authors.lastname: Sanchez
authors.forename: J P
authors.initials: JP
authors.lastname: Perez