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Separation Principle for Nonlinear Partially Observed Stochastic Systems

C.D. Charalambous, R.J. Elliott

This project is concerned with optimal control of nonlinear stochastic systems with noisy dynamics and observations. The pay-off function is of an exponential-of-integral form which leads to robust controllers with respect to unknown noise statistics. This type of pay-off function minimizes in addition to the average value of the integral sample cost, its standard deviation. A series of results are introduced concerning the explicit computation of optimal control laws, for systems with nonlinear dynamics and observations. The result extends the separation theorem of Linear-Gaussian Systems to nonlinear systems. Lie algebraic methods are also introduced to decide a priori whether there exists finite-dimensional optimal control laws. In addition, connections to deterministic disturbance attenuation problems are delineated.

Annual Report

Fri Nov 26 23:00:32 GMT 1999