Title :
Near-optimum regulators for stochastic linear singularly perturbed systems
Author :
Khalil, H. ; Gajic, Z.
Author_Institution :
Michigan State University, East Lansing, Michigan
Abstract :
This paper presents a new approach to approximating linear-quadratic-Gaussian estimation and control problems for singularly perturbed systems. A near-optimal control law, in which separate slow-mode and fast-mode filters are used to estimate the slow and fast variables, is employed to approximate the optimal control law. The order of approximation of the optimal performance is 0(??N) where N is determined by appropriately choosing the coefficients of the near-optimal control law.
Keywords :
Control systems; Electric variables control; Feedback control; Kalman filters; Linear approximation; Optimal control; Regulators; Riccati equations; State estimation; Stochastic systems;
Conference_Titel :
Decision and Control, 1982 21st IEEE Conference on
Conference_Location :
Orlando, FL, USA
DOI :
10.1109/CDC.1982.268373