Abstract
Output measurement for nonlinear optimal control problems is an interesting issue. Because the structure of the real plant is complex, the output channel could give a significant response corresponding to the real plant. In this paper, a least squares scheme, which is based on the Gauss-Newton algorithm, is proposed. The aim is to approximate the output that is measured from the real plant. In doing so, an appropriate output measurement from the model used is suggested. During the computation procedure, the control trajectory is updated iteratively by using the Gauss-Newton recursion scheme. Consequently, the output residual between the original output and the suggested output is minimized. Here, the linear model-based optimal control model is considered, so as the optimal control law is constructed. By feed backing the updated control trajectory into the dynamic system, the iterative solution of the model used could approximate to the correct optimal solution of the original optimal control problem, in spite of model-reality differences. For illustration, current converted and isothermal reaction rector problems are studied and the results are demonstrated. In conclusion, the efficiency of the approach proposed is highly presented.
Highlights
By feed backing the updated control trajectory into the dynamic system, the iterative solution of the model used could approximate to the correct optimal solution of the original optimal control problem, in spite of model-reality differences
An efficient computational method, which is based on linear quadratic regulator (LQR) optimal control model, is proposed to solve the nonlinear stochastic optimal control problems in discrete time [17] [18] [19] [20]
By constructing an efficient matching scheme, it is possible to obtain the optimal solution of the original optimal control problem, in spite of model-reality differences
Summary
An efficient computational method, which is based on LQR optimal control model, is proposed to solve the nonlinear stochastic optimal control problems in discrete time [17] [18] [19] [20] This approach is known as the integrated optimal control and parameter estimation (IOCPE) algorithm. The IOCPE is developed, based on the principle of model-reality differences, for solving the discrete time deterministic and stochastic nonlinear optimal control problems. In both of these iterative algorithms, the adjusted parameters are introduced in the model-based optimal control problem.
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