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Mode set focused hybrid estimation

100%
Rienmüller T., Hofbaur M., Travé-Massuyès L., Bayoudh M.
International Journal of Applied Mathematics and Computer Science
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2013
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tom 23
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nr 1
131-144
EN
Estimating the state of a hybrid system means accounting for the mode of operation or failure and the current state of the continuously valued entities concurrently. Existing hybrid estimation schemes try to overcome the problem of an exponentially growing number of possible mode-sequence/continuous-state combinations by merging hypotheses and/or deducing likelihood measures to identify tractable sets of the most likely hypotheses. However, they still suffer from unnecessarily high computational costs as the number of possible modes increases. Hybrid diagnosis schemes, on the other hand, estimate the current mode of operation/failure only, thus leaving the continuous evolution of the system implicit. This paper proposes a novel scheme that uses a combination of both the approaches in order to define posterior transition probabilities between the specified modes of the hybrid system, hence focusing better on relevant hypotheses. In order to demonstrate the effectiveness of the proposed method, the algorithm is applied to a satellite attitude control system and compared with existing hybrid estimation/diagnosis schemes, such as the Interacting Multiple Model (IMM) algorithm, a purely parity based method (HyDiag), and an existing hybrid Mode Estimation (hME) algorithm.
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Detection and identification of loss of efficiency faults of flight actuators

100%
Ossmann D., Varga A.
International Journal of Applied Mathematics and Computer Science
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2015
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tom 25
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nr 1
53-63
EN
We propose linear parameter-varying (LPV) model-based approaches to the synthesis of robust fault detection and diagnosis (FDD) systems for loss of efficiency (LOE) faults of flight actuators. The proposed methods are applicable to several types of parametric (or multiplicative) LOE faults such as actuator disconnection, surface damage, actuator power loss or stall loads. For the detection of these parametric faults, advanced LPV-model detection techniques are proposed, which implicitly provide fault identification information. Fast detection of intermittent stall loads (seen as nuisances, rather than faults) is important in enhancing the performance of various fault detection schemes dealing with large input signals. For this case, a dedicated fast identification algorithm is devised. The developed FDD systems are tested on a nonlinear actuator model which is implemented in a full nonlinear aircraft simulation model. This enables the validation of the FDD system's detection and identification characteristics under realistic conditions.
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An LPV pole-placement approach to friction compensation as an FTC problem

88%
Patton R., Chen L., Klinkhieo S.
International Journal of Applied Mathematics and Computer Science
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2012
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tom 22
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nr 1
149-160
EN
The concept of combining robust fault estimation within a controller system to achieve active Fault Tolerant Control (FTC) has been the subject of considerable interest in the recent literature. The current study is motivated by the need to develop model-based FTC schemes for systems that have no unique equilibria and are therefore difficult to linearise. Linear Parameter Varying (LPV) strategies are well suited to model-based control and fault estimation for such systems. This contribution involves pole-placement within suitable LMI regions, guaranteeing both stability and performance of a multi-fault LPV estimator employed within an FTC structure. The proposed design strategy is illustrated using a nonlinear two-link manipulator system with friction forces acting simultaneously at each joint. The friction forces, regarded as a special case of actuator faults, are estimated and their effect is compensated within a polytope controller system, yielding a robust form of active FTC that is easy to apply to real robot systems.
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