Title :
Markov Chains, Entropy, and Fundamental Limitations in Nonlinear Stabilization
Author :
Mehta, Prashant G. ; Vaidya, Umesh ; Banaszuk, Andrzej
Author_Institution :
Univ. of Illinois, Urbana
fDate :
4/1/2008 12:00:00 AM
Abstract :
In this paper, we propose a novel methodology for establishing fundamental limitations in nonlinear stabilization. To aid the analysis, we express the stabilization problem as control of Markov chains. Using Markov chains, we derive the limitations as certain maximum probability bounds or as positive conditional entropy of the certain signals in the feedback loop. The former is related to the infeasibility of the asymptotic stabilization in the presence of quantization and the latter to the Bode integral formula. In either cases, it is shown that uncertainty - associated here with the unstable eigenvalues of the linearization - leads to fundamental limitations.
Keywords :
Markov processes; asymptotic stability; control system analysis; feedback; nonlinear control systems; Markov chains; asymptotic stabilization; entropy; maximum probability bounds; nonlinear stabilization; Eigenvalues and eigenfunctions; Entropy; Feedback loop; Information theory; Nonlinear dynamical systems; Nonlinear systems; Quantization; Random processes; Stochastic systems; Transfer functions; Ergodic theory; Markov chains; fundamental limitations; nonlinear systems; stabilization;
Journal_Title :
Automatic Control, IEEE Transactions on
DOI :
10.1109/TAC.2008.917640