QSpace at Queen's University >
Graduate Theses, Dissertations and Projects >
Queen's Graduate Theses and Dissertations >
Please use this identifier to cite or link to this item:
|Title: ||Integrated real-time optimization and model predictive control under parametric uncertainties|
|Authors: ||Adetola, Veronica A.|
|Keywords: ||Real-time optimization|
Model predictive control
|Issue Date: ||2008|
|Series/Report no.: ||Canadian theses|
|Abstract: ||The actualization of real-time economically optimal process operation requires proper integration of real-time optimization (RTO) and dynamic control. This dissertation addresses the integration problem and provides a formal design technique that
properly integrates RTO and model predictive control (MPC) under
parametric uncertainties. The task is posed as an adaptive extremum-seeking control (ESC) problem in which the controller is
required to steer the system to an unknown setpoint that optimizes a user-specified objective function.
The integration task is first solved for linear uncertain systems. Then a method of determining appropriate excitation conditions for nonlinear systems with uncertain reference setpoint is provided.
Since the identification of the true cost surface is paramount to the success of the integration scheme, novel parameter estimation techniques with better convergence properties are developed. The
estimation routine allows exact reconstruction of the system's
unknown parameters in finite-time. The applicability of the identifier to improve upon the performance of existing adaptive
controllers is demonstrated.
Adaptive nonlinear model predictive controllers are developed for a class of constrained uncertain nonlinear systems. Rather than relying on the inherent robustness of nominal MPC, robustness
features are incorporated in the MPC framework to account for the
effect of the model uncertainty. The numerical complexity and/or the
conservatism of the resulting adaptive controller reduces as more information becomes available and a better uncertainty description is obtained.
Finally, the finite-time identification procedure and the adaptive MPC are combined to achieve the integration task. The proposed design solves the economic optimization and control problem at the
same frequency. This eliminates the ensuing interval of "no-feedback" that occurs between economic optimization interval,
thereby improving disturbance attenuation.|
|Description: ||Thesis (Ph.D, Chemical Engineering) -- Queen's University, 2008-08-08 12:30:47.969|
|Appears in Collections:||Department of Chemical Engineering Graduate Theses|
Queen's Graduate Theses and Dissertations
Items in QSpace are protected by copyright, with all rights reserved, unless otherwise indicated.