Integrated real-time optimization and model predictive control under parametric uncertainties

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Date
2008-08-14T18:45:20Z
Authors
Adetola, Veronica A.
Keyword
Real-time optimization , Model predictive control , Adaptive control , Closed-loop identification
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.
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