In this paper, we propose the novel concept of "control storage function" and introduce upper and lower bounds to the best asymptotic average performance for nonlinear control systems based on suitable notions of dissipativity and controlled dissipativity. This allows to extend and unify the formulation and analysis of economic model predictive control for general optimal operation regimes, including, in particular, steady-state or periodic operation. A closed-loop economic performance and stability analysis are carried out within this generalized framework. As a special case, when the optimal operation is periodic, we present a new approach to formulate terminal cost functions. Finally, several examples and counterexamples are proposed and discussed to show the merits of the proposed approach.

Analysis of economic model predictive control with terminal penalty functions on generalized optimal regimes of operation / Dong, Zihang*; Angeli, David. - In: INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL. - ISSN 1049-8923. - STAMPA. - 28:(2018), pp. 4790-4815. [10.1002/rnc.4283]

Analysis of economic model predictive control with terminal penalty functions on generalized optimal regimes of operation

Angeli, David
2018

Abstract

In this paper, we propose the novel concept of "control storage function" and introduce upper and lower bounds to the best asymptotic average performance for nonlinear control systems based on suitable notions of dissipativity and controlled dissipativity. This allows to extend and unify the formulation and analysis of economic model predictive control for general optimal operation regimes, including, in particular, steady-state or periodic operation. A closed-loop economic performance and stability analysis are carried out within this generalized framework. As a special case, when the optimal operation is periodic, we present a new approach to formulate terminal cost functions. Finally, several examples and counterexamples are proposed and discussed to show the merits of the proposed approach.
2018
28
4790
4815
Dong, Zihang*; Angeli, David
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1138982
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