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Optimal Sensor and Actuator Selection Using Balanced Model Reduction
- Source :
- IEEE Transactions on Automatic Control. 67:2108-2115
- Publication Year :
- 2022
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2022.
-
Abstract
- Optimal sensor and actuator selection is a central challenge in high-dimensional estimation and control. Nearly all subsequent control decisions are affected by these sensor/actuator locations, and optimal placement amounts to an intractable brute-force search among the combinatorial possibilities. In this work, we exploit balanced model reduction and greedy optimization to efficiently determine sensor and actuator selections that optimize observability and controllability. In particular, we determine locations that optimize scalar measures of observability and controllability via greedy matrix QR pivoting on the dominant modes of the direct and adjoint balancing transformations. Pivoting runtime scales linearly with the state dimension, making this method tractable for high-dimensional systems. The results are demonstrated on the linearized Ginzburg-Landau system, for which our algorithm approximates known optimal placements computed using costly gradient descent methods.<br />Comment: 8 pages, 6 figures
- Subjects :
- 0209 industrial biotechnology
Computer science
Systems and Control (eess.SY)
Dynamical Systems (math.DS)
02 engineering and technology
Electrical Engineering and Systems Science - Systems and Control
Matrix (mathematics)
020901 industrial engineering & automation
Dimension (vector space)
Control theory
FOS: Mathematics
FOS: Electrical engineering, electronic engineering, information engineering
Mathematics - Numerical Analysis
Observability
Mathematics - Dynamical Systems
Electrical and Electronic Engineering
Mathematics - Optimization and Control
Selection (genetic algorithm)
Scalar (physics)
Numerical Analysis (math.NA)
Computer Science Applications
Controllability
Optimization and Control (math.OC)
Control and Systems Engineering
Actuator
Gradient descent
Subjects
Details
- ISSN :
- 23343303 and 00189286
- Volume :
- 67
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Automatic Control
- Accession number :
- edsair.doi.dedup.....da06854bfdb84c70384fcdf0ec7ee4a4