Title :
Aiming control: design of residence probability controllers
Author :
Kim, S. ; Meerkov, S.M. ; Runolfsson, T.
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Michigan Univ., Ann Arbor, MI, USA
Abstract :
Methods for the design of residence probability (RP) controllers for linear stochastic systems are developed. Comparisons with H 2- and H∞-optimal designs are given. In particular, it is shown that in the so-called strong RP-controllability case, every RP-optimal controller is H2 - and H∞-optimal; the converse is not true. In the weak RP-controllability case, the H∞ -optimal controllers result in RP arbitrarily close to 0. The design of gains for RP-controllers is not more complex than that in the linear quadratic Gaussian (LQG) method: it only involves solving a Riccati equation. However, unlike the LQG, RP-controllers require the selection of the initial set D0 as well; this is accomplished by solving a Lyapunov equation
Keywords :
Lyapunov methods; control system synthesis; controllability; optimal control; probability; stochastic systems; Lyapunov equation; Riccati equation; controllability; design; linear stochastic systems; optimal control; residence probability controllers; Closed loop systems; Control systems; Controllability; Design methodology; Differential equations; Feedback loop; Force control; Force feedback; Indium tin oxide; Stochastic systems;
Conference_Titel :
Decision and Control, 1990., Proceedings of the 29th IEEE Conference on
Conference_Location :
Honolulu, HI
DOI :
10.1109/CDC.1990.204003
