STOCHASTIC CONTROL FOR SYSTEMS WITH NON-GAUSSIAN NOISE
Abstract
In this proposal some stochastic control problems and related topics are described for stochastic equations that are driven by some non-Gaussian noise processes. These noise models include Rosenblatt processes. These noise models have a long range dependence property that has been empirically determined in a wide variety of physical phenomena. Since these processes are not Markov optimal controls cannot be determined by solving partial differential equations such as Hamilton-Jacobi-Bellman equations. Furthermore stochastic maximum principles with forward-backward stochastic differential equations are not available for these problems. The proposers plan to obtain explicit optimal stochastic controls as they have done for an ergodic linear-quadratic control problem for a scalar system. It is also planned to study the problems of parameter estimation and adaptive control for these stochastic systems with non-Gaussian, long range dependent noise.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Aug 12, 2021
- Source ID
- FA95502010336
Entities
People
- Tyrone Edward Duncan
Organizations
- Air Force Office of Scientific Research
- United States Air Force
- University of Kansas