Robotics and Autonomous Systems
An introduction to model-based systems
AI Communications - Model-Based Systems
A causal analysis method for concurrent hybrid automata
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Coordinating agile systems through the model-based execution of temporal plans
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Set-theoretic estimation of hybrid system configurations
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Adaptive sensor fault detection and identification using particle filter algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Robust, optimal predictive control of jump Markov linear systems using particles
HSCC'07 Proceedings of the 10th international conference on Hybrid systems: computation and control
Agent-based coding GA and application to combat modeling and simulation
ISICA'07 Proceedings of the 2nd international conference on Advances in computation and intelligence
Automated plan assessment in cognitive manufacturing
Advanced Engineering Informatics
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on model-based diagnostics
A comprehensive diagnosis methodology for complex hybrid systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on model-based diagnostics
Bridging control and artificial intelligence theories for diagnosis: A survey
Engineering Applications of Artificial Intelligence
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Modern automated systems evolve both continuously and discretely, and hence require estimation techniques that go well beyond the capability of a typical Kalman filter. Multiple model (MM) estimation schemes track these system evolutions by applying a bank of filters, one for each discrete system mode. Modern systems, however, are often composed of many interconnected components that exhibit rich behaviors, due to complex, system-wide interactions. Modeling these systems leads to complex stochastic hybrid models that capture the large number of operational and failure modes. This large number of modes makes a typical MM estimation approach infeasible for online estimation. This paper analyzes the shortcomings of MM estimation, and then introduces an alternative hybrid estimation scheme that can efficiently estimate complex systems with large number of modes. It utilizes search techniques from the toolkit of model-based reasoning in order to focus the estimation on the set of most likely modes, without missing symptoms that might be hidden amongst the system noise. In addition, we present a novel approach to hybrid estimation in the presence of unknown behavioral modes. This leads to an overall hybrid estimation scheme for complex systems that robustly copes with unforeseen situations in a degraded, but fail-safe manner.