Smart Predict-Then-Control: Integrating identification and control via decision regret
This paper presents Smart Predict-Then-Control (SPC) framework for integrating system identification and control. This novel SPC framework addresses the limitations of traditional methods, the unaligned modeling error and control cost. It leverages decision regret to prioritize control-relevant dynamics, optimizing prediction errors based on their impact on control performance. Furthermore, the existence of guarantees on regret bounds are theoretically proved. The proposed SPC is validated on both linear and nonlinear systems.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Physics-informed data-driven control without persistence of excitation
We show that data that is not sufficiently informative to allow for system re-identification can still provide meaningful information when combined with external or physical knowledge of the system, such as bounded syste…
NUTRIVISION: A System for Automatic Diet Management in Smart Healthcare
Maintaining health and fitness through a balanced diet is essential for preventing non communicable diseases such as heart disease, diabetes, and cancer. NutriVision combines smart healthcare with computer vision and mac…
ManagementNutritionobject-detectionObject DetectionA Novel Data Segmentation Method for Data-driven Phase Identification
This paper presents a smart meter phase identification algorithm for two cases: meter-phase-label-known and meter-phase-label-unknown. To improve the identification accuracy, a data segmentation method is proposed to exc…
ClusteringFastAudio: A Learnable Audio Front-End for Spoof Speech Detection
Voice assistants, such as smart speakers, have exploded in popularity. It is currently estimated that the smart speaker adoption rate has exceeded 35% in the US adult population. Manufacturers have integrated speaker ide…
Speaker IdentificationSpeaker VerificationVoice Anti-spoofingActive User Authentication for Smartphones: A Challenge Data Set and Benchmark Results
In this paper, automated user verification techniques for smartphones are investigated. A unique non-commercial dataset, the University of Maryland Active Authentication Dataset 02 (UMDAA-02) for multi-modal user authent…
Face DetectionFace VerificationUser Identification