1. Author's Information
    Kok Lay Teo
    Department of Mathematics and Statistics, Curtin University of Technology, Perth, Australia.

    Wah June Leong
    Department of Mathematics, Universiti Putra Malaysia, Serdang, Malaysia.

    Sy Yi Sim
    Department of Electrical Engineering Technology, Universiti Tun Hussein Onn Malaysia, Pagoh, Malaysia.

    Sie Long Kek
    Department of Mathematics and Statistics,Universiti Tun Hussein Onn Malaysia, Pagoh, Malaysia.

  2. Abstract
    In this paper, a computational approach is proposed for solving the discrete-time nonlinear optimal control problem, which is disturbed by a sequence of random noises. Because of the exact solution of such optimal control problem is impossible to be obtained, estimating the state dynamics is currently required. Here, it is assumed that the output can be measured from the real plant process. In our approach, the state mean propagation is applied in order to construct a linear model-based optimalcontrol problem, where the model output is measureable. On this basis, an output error, which takes into account the differences between the real output and the model output, is defined. Then, this output error is minimized by applying the stochastic approximation approach. During the computation procedure, the stochastic gradient is established, so as the optimal solution of the model used can be updated iteratively. Once the convergence is achieved, the iterative solution approximates to the true optimal solution of the original optimal control problem, in spite of model-reality differences. For illustration, an example on a continuous stirred-tank reactor problem is studied, and the result obtained shows the applicability of the approach proposed. Hence, the efficiency of the approach proposed is highly recommended.
    Nonlinear Optimal Control, Output Error, Model-Reality Differences, Iterative Solution, Stochastic Approximation

    ADLID: 218-v1
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  1. Keywords
    Nonlinear Optimal Control Output Error Model-Reality Differences Iterative Solution Stochastic Approximation
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