Title :
A robust model predictive control algorithm for stable linear plants
Author :
Badgwell, Thomas A.
Author_Institution :
Dept. of Chem. Eng., Rice Univ., Houston, TX, USA
Abstract :
This paper presents a new robust model predictive control (MPG) algorithm for stable, linear plants that is a direct generalization of the nominally stabilizing regulator presented by Rawlings and Muske (1993). Model uncertainty is parameterized by a list of possible plants. Robust stability is achieved through the addition of constraints that prevent the sequence of optimal controller costs from increasing for the true plant. Asymptotic stability is demonstrated through a Lyapunov argument. Simulation experiments demonstrate the performance of the algorithm for a continuous stirred tank reactor (CSTR) process
Keywords :
Lyapunov methods; asymptotic stability; optimal control; predictive control; robust control; uncertain systems; CSTR; Lyapunov argument; MPG algorithm; asymptotic stability; continuous stirred tank reactor; model uncertainty; nominally stabilizing regulator; optimal controller cost sequence; robust model predictive control algorithm; robust stability; stable linear plants; Continuous-stirred tank reactor; Cost function; Optimal control; Prediction algorithms; Predictive control; Predictive models; Regulators; Robust control; Robust stability; Uncertainty;
Conference_Titel :
American Control Conference, 1997. Proceedings of the 1997
Conference_Location :
Albuquerque, NM
Print_ISBN :
0-7803-3832-4
DOI :
10.1109/ACC.1997.610857
