**6. Concluding remarks**

A fixed‐interval smoothing scheme was modified in this chapter for solving the discrete‐time nonlinear stochastic optimal control problem. The state estimation procedure, which is using the Kalman filtering theory and is followed by the fixed‐interval smoothing, is applied to estimate the system dynamics. Then, the smoothed state estimate is used in designing the feedback optimal control law. By employing this smoothed state estimate, system optimization and parameter estimation are integrated. During the computation procedure, the differences between the real plant and the model used are calculated iteratively. On the other hand, the output measured from the real plant is fed back into the model used, in turn, updates the iterative solution. Once the convergence is achieved, the iterative solution approaches to the correct optimal solution of the original optimal control problem, in spite of model‐reality differences. The illustrative example on the optimal control of the continuous stirred‐tank reactor problem was studied. The results obtained demonstrated the applicable of the ap‐ proach proposed, and the efficiency of the approach proposed is highly presented.
