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Intelligent optimization in model-predictive control with risk-sensitive filtering
- Source :
- Journal of Intelligent & Fuzzy Systems. 40:7863-7873
- Publication Year :
- 2021
- Publisher :
- IOS Press, 2021.
-
Abstract
- The uncertainty issue in real-work optimization affects the level of optimization significantly. Because most future uncertainties cannot be foreseen in advance, the optimization must take the uncertainties as a risk in an intelligent way in the process of computation algorithm. Based on our risk-sensitive filtering algorithm, this study adopts a model-predictive control to construct a risk-averse, predictable model that can be used to regulate the level of a real-world system. Our model is intelligent in that the predictive model needs not to identify the system parameters in advance, and our algorithm will learn the parameters through data. When the real-world system is under the disturbance of unexpected events, our model can still maintain suitable performance. Our results show that the intelligent model designed in this study can learn the system parameters in a real-world system and minimize unexpected real-world disturbances. Through the learning process, our model is robust, and the optimal performance can still be retained even the system parameters deviate from expected, e.g., material shortage in a supply chain due to earthquake. When parameter error risks occur, the control rules can still drive the overall system with a minimal performance drop.
- Subjects :
- Statistics and Probability
021103 operations research
Computer science
business.industry
05 social sciences
0211 other engineering and technologies
General Engineering
02 engineering and technology
Risk sensitive
Machine learning
computer.software_genre
Model predictive control
Artificial Intelligence
0502 economics and business
Artificial intelligence
business
computer
050203 business & management
Subjects
Details
- ISSN :
- 18758967 and 10641246
- Volume :
- 40
- Database :
- OpenAIRE
- Journal :
- Journal of Intelligent & Fuzzy Systems
- Accession number :
- edsair.doi...........df284ae429af9aa1ed498d2dcaacf05b