Practical Control Algorithms for Nonlinear Stochastic Systems and Investigations of Nonlinear Filters

摘要

This report summarizes research in problems of stabilization and control of nonlinear stochastic systems observed by noisy measurement data, and the difficulties encountered in processing this noise-contaminated measurement data to obtain accurate estimates of the states of the system. Such problems are inherent in many Air Force systems applications. If it is possible to estimate the state of the system accurately, then well-known classical deterministic control techniques may often be used to give adequate system performance. This approach will greatly reduce the complexity of the control algorithms over that required by the optimal policy. On the other hand the use of nonlinear filtering techniques in place of the simpler linearized filters can greatly increase the accuracy of the state estimates and thereby improve system performance and alleviate divergence problems.