Nonlinear adaptive trajectory tracking using dynamic neural networks.
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.
Poznyak
A S
AS
Departamento de Control Automatico, CINVESTAV-IPN, Mexico D.F., 07360, Mexico.
Yu
W
W
Sanchez
E N
EN
Perez
J P
JP