Data-driven adaptive optimal control for stochastic systems with unmeasurable state

This paper proposes a computational data-driven adaptive optimal control strategy for a class of linear stochastic systems with unmeasurable state. First, a data-driven optimal observer is designed to obtain the optimal state estimation policy. On this basis, an off-policy data-driven ADP algorithm is further proposed, yielding the stochastic optimal control in the absence of system model. An application example of the learning mechanism of central nervous system in arm movement control is given to illustrate the effectiveness and practicality of the strategy.