Control of Cross-Directional Systems with Approximate Symmetries
Idris Kempf, Paul Goulart, Stephen Duncan

TL;DR
This paper develops a semidefinite programming approach to approximate structural symmetries in cross-directional systems, improving stability and robustness of controllers in large-scale, high-speed applications despite inexact symmetries.
Contribution
It introduces a novel method for approximating system symmetries using semidefinite programming, enhancing control stability and performance in practical scenarios.
Findings
Proposed approximations can ensure system stability where Frobenius norm minimization fails.
Semidefinite programming-based approximations outperform traditional methods in stability.
Numerical examples demonstrate improved control robustness in synchrotron systems.
Abstract
Structural symmetries of linear dynamical systems can be exploited for decoupling the dynamics and reducing the computational complexity of the controller implementation. However, in practical applications, inexact structural symmetries undermine the ability to decouple the system, resulting in the loss of any potential complexity reduction. To address this, we propose substituting an approximation with exact structural symmetries for the original system model, thereby introducing an approximation error. We focus on internal model controllers for cross-directional systems encountered in large-scale and high-speed control problems of synchrotrons or the process industry and characterise the stability, performance, and robustness properties of the resulting closed loop. While existing approaches replace the original system model with one that minimises the Frobenius norm of the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Control Systems Optimization · Numerical methods for differential equations · Model Reduction and Neural Networks
MethodsFocus
