arXiv:2401.17933
Moving horizon partition-based state estimation of large-scale systems -- Revised version
원문 초록
This report presents three Moving Horizon Estimation (MHE) methods for discrete-time partitioned linear systems, i.e. systems decomposed into coupled subsystems with non-overlapping states. The MHE approach is used due to its capability of exploiting physical constraints on states in the estimation process. In the proposed algorithms, each subsystem solves reduced-order MHE problems to estimate its own state and different estimators have different computational complexity, accuracy and transmission requirements among subsystems. In all cases, conditions for the convergence of the estimation error to zero are analyzed.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
From Models To Experiments: Shallow Recurrent Decoder Networks on the DYNASTY Experimental Facility
Recursive KalmanNet: Deep Learning-Augmented Kalman Filtering for State Estimation with Consistent Uncertainty Quantification
Benchmarking Spatiotemporal Reasoning in LLMs and Reasoning Models: Capabilities and Challenges
UMotion: Uncertainty-driven Human Motion Estimation from Inertial and Ultra-wideband Units
Contrastive Cross-Course Knowledge Tracing via Concept Graph Guided Knowledge Transfer
Secure Safety Filter Design for Sampled-data Nonlinear Systems under Sensor Spoofing Attacks
토론
이 논문과 연결된 포럼 글이 아직 없습니다.