A model-predictive satisficing approach to a nonlinear tracking problem

Author

Curtis, J. Willard ; Beard, Randal W.

Author_Institution

Dept. of Electr. & Comput. Eng., Brigham Young Univ., Provo, UT, USA

Volume

1

fYear

2001

fDate

2001

Firstpage

491

Abstract

In this paper we use the recently introduced concept of satisficing decision theory in conjunction with a receding horizon optimization technique to achieve suitable tracking for a nonholonomic robot system. The satisficing approach creates a family of "universal formulas" parameterized by two functions. A model predictive scheme is employed to generate these two functions in a way that minimizes the quadratic cost at the next time step. By always choosing an element of the satisficing set, global stability is guaranteed

Keywords

decision theory; minimisation; model reference adaptive control systems; nonlinear control systems; optimal control; predictive control; quadratic programming; robots; tracking; global stability; model predictive scheme; model-predictive control; model-predictive satisficing approach; nonholonomic robot; nonholonomic robot system; nonlinear tracking problem; quadratic cost minimization; receding horizon optimization technique; satisficing decision theory; tracking control; universal formulas; Control systems; Cost benefit analysis; Cost function; Decision theory; Lyapunov method; Nonlinear control systems; Nonlinear systems; Predictive models; Robots; Stability;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 2001. Proceedings of the 40th IEEE Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-7061-9

Type

conf

DOI

10.1109/.2001.980148

Filename

980148