From the 2 of 41 linked papers with an AI index.
41 papers
Event-Triggered Discrete-Time Multivariable Extremum Seeking Systems
Victor Hugo Pereira Rodrigues, Tiago Roux Oliveira, Miroslav KrstiÄ +1
The paper proposes a discrete-time extremum seeking method that updates control inputs only when a state‑dependent event‑trigger condition is satisfied, reducing actuation and comm…
Control-Oriented System Identification: Classical, Learning, and Physics-Informed Approaches
S. Sivaranjani, Yuanyuan Shi, Nikolay Atanasov +6
The paper surveys classical, machine‑learning, and physics‑informed system identification methods that incorporate control‑relevant properties such as dissipativity and symmetry, d…
IMMPC: An Internal Model Based MPC for Rejecting Unknown Disturbances
Felix Brändle, Frank Allgöwer
Model predictive control (MPC) is a powerful control method that allows for the direct inclusion of state and input constraints into the controller design. However, errors in the m…
Data-Driven Robust MPC for Unknown Nonlinear Systems via Set-Membership Learning
Yuzhou Wei, Wenjie Liu, Yifan Xie +3
Data-driven model predictive control (MPC) has become an attractive approach for controlling unknown systems, especially when data are corrupted by noise. However, most existing da…
Verifiable computations for dynamic encrypted control
Sebastian Schlor, Frank Allgöwer
Encrypted control can preserve the privacy of data and parameters while the necessary computations can be outsourced to a cloud server. To ensure the integrity of the received valu…
Koopman meets input-output data: Data-driven output-feedback control of nonlinear systems with closed-loop guarantees
Robin Strässer, Julian Berberich, Manuel Schaller +2
Data-driven control of nonlinear systems from input-output measurements remains a fundamental challenge, as existing approaches with rigorous closed-loop guarantees predominantly r…