Self-Monitoring and Control for Embedded Systems using Hybrid Constraint Automata

摘要

Many of today's mechatronic systems such as automobiles, automated factories or chemical plants are a complex mixture of hardware components and embed- ded control software, showing both continuous (vehicle dynamics, robot motion) and discrete (software) behavior. The problems of estimating the internal discrete/continuous state and automatically devising control actions as intelligent reaction are at the heart of self-monitoring and self-control capabilities for such systems. In this paper, we address these problems with a new integrated approach, which combines concepts, techniques and formalisms from AI (constraint opti- mization, hidden markov model reasoning), fault diagnosis in hybrid systems (sto- chastic abstraction of continuous behavior), and hybrid systems verification (hy- brid automata, reachability analysis). Preliminary experiments with an industrial filling station scenario show promising results, but also indicate current limita- tions.