A combined stochastic and greedy hybrid estimation capability for concurrent hybrid models with autonomous mode transitions

Blackmore, Lars; Funiak, Stanislav; Williams, Brian

doi:10.1016/j.robot.2007.07.003

Cited by 7 publications

(15 citation statements)

References 45 publications

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“…Blackmore et al . [ 35 ] pointed out that their estimation accuracy depends on whether or not k is large enough for real belief state distribution. In other words, when the distribution over belief state is relatively flat, k best methods maybe lead to losing the solution.…”

Section: Resultsmentioning

confidence: 99%

State Tracking and Fault Diagnosis for Dynamic Systems Using Labeled Uncertainty Graph

Zhou

Feng

Zhao

et al. 2015

Sensors

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Cyber-physical systems such as autonomous spacecraft, power plants and automotive systems become more vulnerable to unanticipated failures as their complexity increases. Accurate tracking of system dynamics and fault diagnosis are essential. This paper presents an efficient state estimation method for dynamic systems modeled as concurrent probabilistic automata. First, the Labeled Uncertainty Graph (LUG) method in the planning domain is introduced to describe the state tracking and fault diagnosis processes. Because the system model is probabilistic, the Monte Carlo technique is employed to sample the probability distribution of belief states. In addition, to address the sample impoverishment problem, an innovative look-ahead technique is proposed to recursively generate most likely belief states without exhaustively checking all possible successor modes. The overall algorithms incorporate two major steps: a roll-forward process that estimates system state and identifies faults, and a roll-backward process that analyzes possible system trajectories once the faults have been detected. We demonstrate the effectiveness of this approach by applying it to a real world domain: the power supply control unit of a spacecraft.

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Section: Resultsmentioning

confidence: 99%

State Tracking and Fault Diagnosis for Dynamic Systems Using Labeled Uncertainty Graph

Zhou

Feng

Zhao

et al. 2015

Sensors

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“…This is because many discrete modes need to be tracked to capture the real system trajectory for focused HME, while PF methods can perform well for a relatively small number of particles. Blackmore et al 30 gave a detailed analysis of these two methods and proposed a mixed method, which combined focused HME with RBPFs to track hybrid systems. The improved method captures greedy and stochastic results at the same time and is much more robust than focused HME and RBPFs individually, despite a small performance penalty.…”

Section: Discussionmentioning

confidence: 99%

“…These algorithms vary in how they choose the subset of hybrid states to expand (pruning out hybrid states with low likelihood) and how they merge continuous state estimates (collapsing). 30 In this work, we discuss the focused HME in more detail, and compare it against the other approaches. In order to address the problem of exponential growth discussed in Section 2.2, this method employs a beam search technique that has been widely applied to discrete systems.…”

Section: Focused Hybrid Estimationmentioning

confidence: 99%

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Comparison of state estimation techniques for nonlinear hybrid systems

et al. 2016

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Modern real-world engineering systems exhibit complex hybrid behaviors, which are composed of continuous nonlinear plant dynamics interspersed with discrete mode switching. State estimation of hybrid systems for accurate and timely online monitoring and diagnosis applications is a difficult task. A number of different methods have been proposed, and we develop a conceptual framework to perform a comparative study of four different hybrid state estimation algorithms in this paper: (1) the switched extended Kalman filter; (2) focused hybrid estimation; (3) multiple-modal particle filtering; and (4) the one-step look-ahead particle filtering. The conceptual comparison is followed by an empirical evaluation, where we study the effectiveness of these algorithms in tracking the behaviors of a Reverse Osmosis Subsystem of an Advanced Water Recovery System that was developed at the NASA Johnson Space Center. We discuss our results, which show the strengths and weaknesses of each of these algorithms, and propose topics for further research into this important problem.

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“…These techniques include Multiple Model (MM) methods such as the Generalized Pseudo-Bayesian Algorithm (GPB) (Ackerson & Fu, 1970), the detection-estimation method (Tugnait, 1982), the residual correlation Kalman filter bank (Hanlon & Maybeck, 2000), the Interacting Multiple Model (IMM) algorithm (Blom & Bar-Shalom, 1988), and adaptive MM methods by Li et al (1996Li et al ( , 1999Li et al ( , 2000. More recently, techniques such as the Hybrid Mode Estimator (HME) (Hofbaur & Williams, 2002, 2004, the Hybrid Diagnostic Engine (HyDE) (Narasimhan & Brownston, 2007), and combined stochastic and greedy estimation (Blackmore, Funiak, & Williams, 2008) have also been developed.…”

Section: Introductionmentioning

confidence: 99%

Best-First Enumeration Based on Bounding Conflicts, and its Application to Large-scale Hybrid Estimation

Timmons

Williams

2020

jair

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There is an increasing desire for autonomous systems to have high levels of robustness and safety, attained through continuously planning and self-repairing online. Underlying this is the need to accurately estimate the system state and diagnose subtle failures. Estimation methods based on hybrid discrete and continuous state models have emerged as a method of precisely computing these estimates. However, existing methods have difficulty scaling to systems with more than a handful of components. Discrete, consistency based state estimation capabilities can scale to this level by combining best-first enumeration and conflict-directed search. While best-first methods have been developed for hybrid estimation, conflict-directed methods have thus far been elusive as conflicts learn inconsistencies from constraint violation, but probabilistic hybrid estimation is relatively unconstrained. In this paper we present an approach to hybrid estimation that unifies best-first enumeration and conflict-directed search through the concept of "bounding" conflicts, an extension of conflicts that represent tighter bounds on the cost of regions of the search space. This paper presents a general best-first enumeration algorithm based on bounding conflicts (A*BC) and a hybrid estimation method using this enumeration algorithm. Experiments show that an A*BC powered state estimator produces estimates up to an order of magnitude faster than the current state of the art, particularly on large systems.

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A combined stochastic and greedy hybrid estimation capability for concurrent hybrid models with autonomous mode transitions

Cited by 7 publications

References 45 publications

State Tracking and Fault Diagnosis for Dynamic Systems Using Labeled Uncertainty Graph

State Tracking and Fault Diagnosis for Dynamic Systems Using Labeled Uncertainty Graph

Comparison of state estimation techniques for nonlinear hybrid systems

Best-First Enumeration Based on Bounding Conflicts, and its Application to Large-scale Hybrid Estimation

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