Discrete State Estimators for a Class of Hybrid Systems on a Lattice

Author(s):  
Domitilla Del Vecchio ◽  
Richard M. Murray
Automatica ◽  
2006 ◽  
Vol 42 (2) ◽  
pp. 271-285 ◽  
Author(s):  
Domitilla DelVecchio ◽  
Richard M. Murray ◽  
Eric Klavins

2017 ◽  
Vol 148 ◽  
pp. 123-160 ◽  
Author(s):  
Ernst Althaus ◽  
Björn Beber ◽  
Werner Damm ◽  
Stefan Disch ◽  
Willem Hagemann ◽  
...  

2021 ◽  
Vol 20 (4) ◽  
pp. 1-37
Author(s):  
Guillaume Dupont ◽  
Yamine Ait-Ameur ◽  
Neeraj Kumar Singh ◽  
Marc Pantel

Hybrid systems are complex systems where a software controller interacts with a physical environment, usually named a plant, through sensors and actuators. The specification and design of such systems usually rely on the description of both continuous and discrete behaviours. From complex embedded systems to autonomous vehicles, these systems became quite common, including in safety critical domains. However, their formal verification and validation as a whole is still a challenge. To address this challenge, this article contributes to the definition of a reusable and tool supported formal framework handling the design and verification of hybrid system models that integrate both discrete (the controller part) and continuous (the plant part) behaviours. This framework includes the development of a process for defining a class of basic theories and developing domain theories and then the use of these theories to develop a generic model and system-specific models. To realise this framework, we present a formal proof tool chain, based on the Event-B correct-by-construction method and its integrated development environment Rodin, to develop a set of theories, a generic model, proof processes, and the required properties for designing hybrid systems in Event-B. Our approach relies on hybrid automata as basic models for such systems. Discrete and continuous variables model system states and behaviours are given using discrete state changes and continuous evolution following a differential equation. The proposed approach is based on refinement and proof using the Event-B method and the Rodin toolset. Two case studies borrowed from the literature are used to illustrate our approach. An assessment of the proposed approach is provided for evaluating its extensibility, effectiveness, scalability, and usability.


2017 ◽  
Vol 2017 ◽  
pp. 1-18 ◽  
Author(s):  
Hamzah Abdel-Aziz ◽  
Xenofon Koutsoukos

Dynamical models are essential for model-based control methodologies which allow smart buildings to operate autonomously in an energy and cost efficient manner. However, buildings have complex thermal dynamics which are affected externally by the environment and internally by thermal loads such as equipment and occupancy. Moreover, the physical parameters of buildings may change over time as the buildings age or due to changes in the buildings’ configuration or structure. In this paper, we introduce an online model learning methodology to identify a nonparametric dynamical model for buildings when the thermal load is latent (i.e., the thermal load cannot be measured). The proposed model is based on stochastic hybrid systems, where the discrete state describes the level of the thermal load and the continuous dynamics represented by Gaussian processes describe the thermal dynamics of the air temperature. We demonstrate the evaluation of the proposed model using two-zone and five-zone buildings. The data for both experiments are generated using the EnergyPlus software. Experimental results show that the proposed model estimates the thermal load level correctly and predicts the thermal behavior with good performance.


2009 ◽  
Vol 19 (14) ◽  
pp. 1564-1580 ◽  
Author(s):  
Maria D. Di Benedetto ◽  
Stefano Di Gennaro ◽  
Alessandro D'Innocenzo

1996 ◽  
Vol 41 (3) ◽  
pp. 414-419 ◽  
Author(s):  
M. Dogruel ◽  
L. Ozguner ◽  
S. Drakunov

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