Integrated optimal control and parameter estimation algorithms for discrete-time nonlinear stochastic dynamical systems
This thesis describes the development of an efficient algorithm for solving nonlinear stochastic optimal control problems in discrete-time based on the principle of model-reality differences. The main idea is the integration of optimal control and parameter estimation. In this work, a simplified...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | English |
| Published: |
2011
|
| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/3019/1/24p%20KEK%20SIE%20LONG.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | This thesis describes the development of an efficient algorithm for solving
nonlinear stochastic optimal control problems in discrete-time based on the principle
of model-reality differences. The main idea is the integration of optimal control and
parameter estimation. In this work, a simplified model-based 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 model-based optimal control problem is solved. The algorithm
developed in this thesis contains three sub-algorithms. In the first sub-algorithm, 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 sub-algorithm. In addition, an
improvement is made in the third sub-algorithm 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 sub-algorithms 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 discrete-time nonlinear
optimal control problems, both for deterministic and stochastic cases, based on the
proposed modified linear optimal control theory. |
|---|