AI & ML
Event-Based Control via Sparsity-Promoting Regularization: A Rollout Approach with Performance Guarantees
arXiv eess.SY0 views
arXiv:2509.24799v5 Announce Type: replace Abstract: This paper presents a controller design framework aiming to balance control performance and actuation rate. Control performance is evaluated by an infinite-horizon average cost, and the number of control actions is penalized via sparsity-promoting regularization. Since the formulated optimal control problem has a combinatorial nature, we employ a rollout algorithm to obtain a tractable suboptimal solution. In the proposed scheme, actuation timings are determined through a multistage minimization procedure based on a receding-horizon approach, and the corresponding control inputs are computed online. We establish theoretical performance guarantees with respect to periodic control and prove the stability of the closed-loop system. The effectiveness of the proposed method is demonstrated through a numerical example.
Source:https://arxiv.org/abs/2509.24799PreviousThe Phantom of Davis-Wielandt Shell: A Unified Perspective for Graphical Stability Analysis of MIMO LTI SystemsNextExplicit MPC for the constrained zonotope case with low-rank matrix updates
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