STOCHASTIC LINEAR-QUADRATIC OPTIMAL CONTROL WITH PARTIAL OBSERVATION

This paper studies a class of quadratic optimal control problems for partially observ-able linear dynamical systems. In contrast to the full information case, the control is required to be adapted to the filtration generated by the observation system, which in turn is influenced by the control. The variation method fails in this case due to the fact that the filtration is not fixed. To overcome the difficulty, we use the orthogonal decomposition of the state process to write the cost functional as the sum of two parts: one is a functional of the control and the filtering process and the other part is independent of the choice of the control. The first part possesses a mathematical structure similar to the full information problem. By completing the square, it is shown that the open-loop optimal control is given by a feedback representation via the filtering process. The optimal value is also obtained explicitly.