Kek, Sie Long (2011) Integrated optimal control and parameter estimation algorithms for discretetime nonlinear stochastic dynamical systems. Doctoral thesis, Universiti Teknologi Malaysia.

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Abstract
This thesis describes the development of an efficient algorithm for solving nonlinear stochastic optimal control problems in discretetime based on the principle of modelreality differences. The main idea is the integration of optimal control and parameter estimation. In this work, a simplified modelbased optimal control model with adjustable parameters is constructed. As such, the optimal state estimate is applied to design the optimal control law. The output is measured from the model and used to adapt the adjustable parameters. During the iterative procedure, the differences between the real plant and the model used are captured by the adjustable parameters. The values of these adjustable parameters are updated repeatedly. In this way, the optimal solution of the model will approach to the true optimum of the original optimal control problem. Instead of solving the original optimal control problem, the modelbased optimal control problem is solved. The algorithm developed in this thesis contains three subalgorithms. In the first subalgorithm, the state mean propagation removes the Gaussian white noise to obtain the expected solution. Furthermore, the accuracy of the state estimate with the smallest state error covariance is enhanced by using the Kalman filtering theory. This enhancement produces the filtering solution by using the second subalgorithm. In addition, an improvement is made in the third subalgorithm where the minimum output residual is combined with the cost function. In this way, the real solution is closely approximated. Through the practical examples, the applicability, efficiency and effectiveness of these integrated subalgorithms have been demonstrated through solving several practical real world examples. In conclusion, the principle of modelreality differences has been generalized to cover a range of discretetime nonlinear optimal control problems, both for deterministic and stochastic cases, based on the proposed modified linear optimal control theory.
Item Type:  Thesis (Doctoral) 

Subjects:  Q Science > QA Mathematics Q Science > QA Mathematics > QA299.6433 Analysis 
Depositing User:  Mrs. Sabarina Che Mat 
Date Deposited:  02 Nov 2021 01:39 
Last Modified:  02 Nov 2021 01:39 
URI:  http://eprints.uthm.edu.my/id/eprint/3019 
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