Title :
Robust nonlinear model predictive control via approximate value function
Author :
Yang, Yu ; Lee, Jong Min
Author_Institution :
Dept. of Chem. & Mater. Eng., Univ. of Alberta, Edmonton, AB, Canada
Abstract :
In order to improve the performance of nonlinear model predictive control (NMPC) in the presence of disturbances or model uncertainties, an approximate dynamic programming (ADP) control scheme is proposed. Namely, the Bellman´s optimality principle is employed to determine the input based on the approximate value function constructed from the historical operation data. In addition, the support vector data description is also applied in the state space to determine if the ADP control is suitable for the current state. The proposed control strategy is illustrated on a CSTR example to show its effectiveness.
Keywords :
approximation theory; dynamic programming; nonlinear control systems; predictive control; robust control; support vector machines; uncertain systems; Bellman´s optimality principle; CSTR; approximate dynamic programming control scheme; approximate value function; model uncertainty; robust nonlinear model predictive control; state space; support vector data description; Function approximation; Least squares approximation; Optimization; Stability analysis; Trajectory; Uncertainty; approximate dynamic programming; model predictive control; robust control Lyapunov function; support vector data description;
Conference_Titel :
Control, Automation and Systems (ICCAS), 2011 11th International Conference on
Conference_Location :
Gyeonggi-do
Print_ISBN :
978-1-4577-0835-0
