Trajectory planning for systems with a multiplicative stochastic uncertainty

A trajectory planning problem for linear stochastic differential equations is considered in this paper. The aim is to control the system such that the expected value of the output interpolates given points at given times while the variance and an integral cost of the control effort are minimized. The solution is obtained by using the dynamic programming technique, which allows for several possible generalizations. The results of this paper can be used for control of systems with a multiplicative stochastic disturbance on the state vector and systems with a stochastic growth rate. This is frequently the case in investment problems, biomathematics and control theory.