Neural network implementation of a nonlinear receding-horizon controller

Author

Cavagnari, L. ; Magni, L. ; Scattolini, R.

Author_Institution

Dipt. di Inf. e Sistemistica, Pavia Univ., Italy

Volume

1

fYear

1998

fDate

1-4 Sep 1998

Firstpage

158

Abstract

This paper presents the application of an output feedback nonlinear receding horizon control algorithm to a laboratory seesaw equipment. This control law guarantees exponential stability of the equilibrium and allows one to consider the presence of control and state constraints. Since the specific control application requires a small sampling interval, the nonlinear control law is computed off-line for different values of the initial state. Then, an approximating function is derived with the aid of a neural net, which is subsequently implemented for online computations

Keywords

asymptotic stability; discrete time systems; feedback; function approximation; neurocontrollers; nonlinear control systems; nonlinear dynamical systems; discrete time systems; exponential stability; function approximation; laboratory seesaw equipment; mechanical systems; neural net; nonlinear dynamical systems; nonlinear receding-horizon controller; output feedback; state constraints; Control systems; Laboratories; Mechanical systems; Mechanical variables control; Neural networks; Nonlinear control systems; Sampling methods; Signal processing algorithms; Stability; Strain control;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Applications, 1998. Proceedings of the 1998 IEEE International Conference on

Conference_Location

Trieste

Print_ISBN

0-7803-4104-X

Type

conf

DOI

10.1109/CCA.1998.728316

Filename

728316