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Data-Knowledge-Driven Multiobjective Integrated Optimal Control for Nonlinear Systems
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DOI：10.1109/TSMC.2024.3443996.png)
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Abstract
En 中文
Multiobjective optimal control (MOC) optimize multiple performance indices of nonlinear systems to obtain setpoints, and design the controller to track the setpoints. However, if the feasibility of the controller is not considered, untraceable setpoints may be obtained. Furthermore, the performance of data-driven MOC may be degraded due to insufficient data. To address this problem, a data-knowledge-driven multiobjective integrated optimal control (DK-MIOC) method is proposed in this article. First, an integrated optimal control (IOC) framework is designed that integrates a cost function for both system performance and tracking error. Then, the feasibility of the controller can be considered simultaneously while solving for the optimal setpoints. Second, a data-knowledge-driven model is incorporated into this framework to predict future dynamics. Then, the performance indices can be accurately predicted even with insufficient data. Third, a collaborative optimization algorithm is implemented to determine setpoints and control laws. Consequently, the operational control performance of the nonlinear system is enhanced. Furthermore, the stability of the DK-MIOC strategy is also analyzed. Finally, DK-MIOC is tested on a conventional nonlinear system and a wastewater treatment process (WWTP) to validate its effectiveness.
Keywords:
Optimization
Optimal control
Nonlinear systems
Cost function
Predictive models
Prediction algorithms
Data models
Collaborative optimization algorithm (COA)
data-knowledge-driven model (DK-model)
multiobjective optimal control (MOC)
Journal
IF:
10.5
Papers:
1.1W
Citations:
5.0W