This paper deals with risk-sensitive optimal control problems for hidden Markov models. Using information states, the authors investigated the structure of the optimal controller and showed that an optimal risk-sensitive controller and its corresponding information state converge to the known solution for the risk-neutral solution as the risk factor goes to \(0\).