Parameter uncertainty modeling for multiobjective robust control design. Application to a temperature control system in a proton exchange membrane fuel cell

Autor: U. Veyna, X. Blasco, J.M. Herrero, A. Pajares
Rok vydání: 2023
Předmět:
Zdroj: Engineering Applications of Artificial Intelligence. 119:105758
ISSN: 0952-1976
DOI: 10.1016/j.engappai.2022.105758
Popis: [EN] Advanced control systems are tuned using dynamic models and optimization techniques. This approach frequently involves satisfying multiple conflicting objectives. Tuning robust controllers requires considering a framework that represents the system uncertainties, and its definition is not a trivial task. When dealing with a nonlinear model with many parameters, a high-quality representation requires a massive sampling of variations. In many cases, this represents an inaccessible computational cost for the optimization process. This work presents a new methodology for parameter uncertainty modeling that is oriented to tuning robust controllers based on multiobjective optimization techniques. The uncertainty modeling formulated represents a feasible computational cost and leads to robust solutions without attributing excessive conservatism. The novelty of this process consists in using the multiobjective space to define a set of scenarios with highly representative properties of the global uncertainty framework that formulate the control problem under a predefined minimization strategy. To demonstrate the effectiveness of this methodology, we present a temperature control design in a micro-CHP system under worst-case minimization. Based on the results, particular interest is given to verifying the appropriate formulation of the uncertainty modeling, which represents a 92.8% reduction of the computational cost involved in solving the robust optimization problem under a global uncertainty framework.
This work was supported in part by grant PID2021-124908NB-I00 founded by MCIN/AEI/10.13039/501100011033 and by "ERDF A way of making Europe"; by grant SP20200109 (PAID-10-20) funded by Universitat Politecnica de Valencia; and by grant PRE2019-087579 funded by MCIN/AEI/10.13039/501100011033 and by "ESF Investing in your future"; and by the Generalitat Valenciana regional government through project CIAICO/2021/064. Funding for open access charge: CRUE-Universitat Politecnica de Valencia.
Databáze: OpenAIRE
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