Many of today’s mechatronic systems – such as automobiles, automated factories or chemical plants – are a complex mixture of hardware components and embedded 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 for- malisms from AI (constraint optimization, hid- den markov model reasoning), fault diagnosis in hybrid systems (stochastic abstraction of continuous behavior), and hybrid systems verification (hybrid automata, reachability analysis). Prelimi- nary experiments with an industrial filling station scenario show promising results, but also indicate current limitations.
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diagnosis, fault-tolerant control, hybrid modeling
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