Coordinated Decentralized Protocols for Failure Diagnosisof Discrete Event Systems
Discrete Event Dynamic Systems
Bayesian Fault Detection and Diagnosis in Dynamic Systems
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Factored particles for scalable monitoring
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Tractable inference for complex stochastic processes
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Diagnosis of continuous valued systems in transient operating regions
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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The objective of this paper is to build a full-order Luenberger observer of an omnidirectional mobile robot (Robotino) modelled by bond graph. This work is done in the framework of the control and supervision of a mobile robot group formation. In the first step, the bond graph model of the robot is proposed and validated. In the second step, a full-order Luenberger observer design method for linear time invariant systems modeled by bond graph is presented, applied to a mobile robot, and extended to a group of robots. The bond graph approach is used from modeling up to building observers. This method requires structural analysis (Observability, redundancy, bond graph rank) and formal calculus (gain matrix calculus).