2013
DOI: 10.3934/naco.2013.3.109
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Abstract: An iterative algorithm, which is called the integrated optimal control and parameter estimation algorithm, is developed for solving a discrete time nonlinear stochastic control problem. It is based on the integration of the principle of model-reality differences and Kalman filtering theory, where the dynamic integrated system optimization and parameter estimation algorithm are used interactively. In this approach, the weighted least-square output residual is included in the cost function by appropriately monit… Show more

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Cited by 7 publications
(5 citation statements)
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“…Notice that the separation principle [1][2][3][4] is applied to solving Problem (M), where the optimal feedback control law and the optimal state estimate are designed separately as discussed in [16][17][18]. Further from this, the accuracy of the optimal state estimate is increased by smoothing the state estimate in the fixed interval [2,4].…”
Section: Problem Descriptionmentioning
confidence: 99%
See 1 more Smart Citation
“…Notice that the separation principle [1][2][3][4] is applied to solving Problem (M), where the optimal feedback control law and the optimal state estimate are designed separately as discussed in [16][17][18]. Further from this, the accuracy of the optimal state estimate is increased by smoothing the state estimate in the fixed interval [2,4].…”
Section: Problem Descriptionmentioning
confidence: 99%
“…In fact, the exact solution of stochastic optimal control problems is impossible to be obtained, especially for the problems involving nonlinear system dynamics. To obtain an optimal solution of the discrete-time nonlinear stochastic optimal control problem, the integrated optimal control and parameter estimation (IOCPE) algorithm has been proposed to solve this kind of the problem iteratively [16][17][18]. In this algorithm, the linear quadratic Gaussian (LQG) model is applied to a model-based optimal control problem, where the state estimation procedure is done using the Kalman filtering theory.…”
Section: Introductionmentioning
confidence: 99%
“…In addition, the optimal output solution obtained from the IOCPE algorithm has been improved by using the weighted output residual [16], which is introduced into the model cost function, and the output matching scheme [17], where the adjusted parameter is introduced into the model output. Moreover, the application of the approaches on the least-square and the Gauss-Newton with the principle of model-reality differences, which omits from using the adjusted parameters, enhance the practical usage of the IOCPE algorithm for delivering the optimal solution of the original optimal control problem [18] [19].…”
Section: Introductionmentioning
confidence: 99%
“…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 [1] [10] [13]. Besides, for solving the discrete time nonlinear stochastic optimal control problem, the Kalman filtering theory is associated with the principle of model-reality differences in order to do state estimation and system optimization [2] [3] [4] [6].…”
Section: Introductionmentioning
confidence: 99%