Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Learning and Inference in Parametric Switching Linear Dynamical Systems
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Optimal control of discrete hybrid stochastic automata
HSCC'05 Proceedings of the 8th international conference on Hybrid Systems: computation and control
Hybrid estimation of complex systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Brief A probabilistically constrained model predictive controller
Automatica (Journal of IFAC)
A probabilistic particle-control approximation of chance-constrained stochastic predictive control
IEEE Transactions on Robotics
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Hybrid discrete-continuous models, such as Jump Markov Linear Systems, are convenient tools for representing many real-world systems; in the case of fault detection, discrete jumps in the continuous dynamics are used to model system failures. Stochastic uncertainty in hybrid systems arises in both the continuous dynamics, in the form of uncertain state estimation, disturbances or uncertain modeling, and in the discrete dynamics, which are themselves stochastic. In this paper we present a novel method for optimal predictive control of Jump Markov Linear Systems that is robust to both continuous and discrete uncertainty. The approach extends our previous 'particle control' approach, which approximates the predicted distribution of the system state using a finite number of particles. Here, we present a weighted particle control approach, which uses importance weighting to ensure that low probability events such as failures are considered. We demonstrate the method with a car braking scenario.