Monitoring and Diagnosis of Hybrid Systems Using Particle Filtering Methods
X. Koutsoukos, J. Kurien, and F. Zhao
Proceedings of the 15th International Symposium on Mathematical Theory of
Networks and Systems - MTNS 2002,
Notre Dame, IN, August 2002.
Abstract -- Embedded systems are composed of a large number of components
that interact with the physical world via a set of sensors
and actuators, have their own computational capabilities, and
communicate with each other via a wired or wireless network.
Diagnostic systems for such applications must address new challenges
caused by the distribution of resources, the networking environment,
and the tight coupling between the computational and the physical
worlds. Our approach is to move from centralized, discrete or continuous
techniques toward a distributed, hybrid diagnosis architecture.
Monitoring and diagnosis of any dynamical system depend crucially on
the ability to estimate the system state given the observations.
Estimation for hybrid systems is particularly challenging because it
requires keeping track of multiple models and the transitions between
them. This paper presents a particle filtering based estimation algorithm
that addresses the challenge of the interaction between continuous and
discrete dynamics in hybrid systems. The hybrid estimation methodology
has been demonstrated on a rocket propulsion system.