Smoothing Solution for Discrete-Time Nonlinear Stochastic Optimal Control Problem with Model-Reality Differences

Released on by Sie Long Kek
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  • Smoothing Solution for Discrete-Time Nonlinear Stochastic Optimal Control Problem with Model-Reality Differences Book Detail

  • Author : Sie Long Kek
  • Release Date : 2016
  • Publisher :
  • Genre : Mathematics
  • Pages :
  • ISBN 13 :
  • File Size : 67,67 MB
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Smoothing Solution for Discrete-Time Nonlinear Stochastic Optimal Control Problem with Model-Reality Differences by Sie Long Kek PDF Summary

Book Description: In this chapter, the performance of the integrated optimal control and parameter estimation (IOCPE) algorithm is improved using a modified fixed-interval smoothing scheme in order to solve the discrete-time nonlinear stochastic optimal control problem. In our approach, a linear model-based optimal control problem with adding the adjustable parameters into the model used is solved iteratively. The aim is to obtain the optimal solution of the original optimal control problem. In the presence of the random noise sequences in process plant and measurement channel, the state dynamics, which is estimated using Kalman filtering theory, is smoothed in a fixed interval. With such smoothed state estimate sequence that reduces the output residual, the feedback optimal control law is then designed. During the computation procedure, the optimal solution of the modified model-based optimal control problem can be updated at each iteration step. When convergence is achieved, the iterative solution approaches to the correct optimal solution of the original optimal control problem, in spite of model-reality differences. Moreover, the convergence of the resulting algorithm is also given. For illustration, optimal control of a continuous stirred-tank reactor problem is studied and the result obtained shows the efficiency of the approach proposed.

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Nonlinear Systems

Nonlinear Systems

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The book consists mainly of two parts: Chapter 1 - Chapter 7 and Chapter 8 - Chapter 14. Chapter 1 and Chapter 2 treat design techniques based on linearization