Positivity-Preserving H∞ Model Reduction for Discrete-Time Positive Systems via a Successive Convex Optimization Algorithm
Source abstract
Abstract
This paper considers the positivity-preserving model reduction for discrete-time positive systems. Given a stable high-order positive system, we aim to find a reduced-order model such that the approximation error is minimized within a prescribed H∞ performance and positivity is preserved. Regarding the bounded real lemma, the sufficient and necessary condition for the existence of a reduced-order model is established in terms of bilinear matrix inequality and convex semi-definite constraint, which ensures that the reduced-order system is positive and the resulted error system is stable and has an H∞ performance level. Based on the inner-approximation strategy, we approximate the bilinear constraints with convex ones, under which an iterative procedure is provided to calculate the desired reduced-order model. Finally, an example is provided to demonstrate the effectiveness and potential benefits of the presented results.
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Research context
OpenAlex topics: Model Reduction and Neural Networks · Probabilistic and Robust Engineering Design · Control Systems and Identification
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Shunde Innovation School, University of Science and Technology Beijing, Foshan 528399, China
Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China
Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, Beijing 100083, China
Funding
National Natural Science Foundation of China · Postdoctoral Research Foundation of Shunde Innovation School, University of Science and Technology Beijing
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