Real-time Fault Estimation for a Class of Discrete-Time Linear Parameter-Varying Systems
Chris van der Ploeg (TNO, Eindhoven University of Technology)
Emilia Silvas (TNO, Eindhoven University of Technology)
Nathan Wouw (University of Minnesota, Eindhoven University of Technology)
Peyman Esfahani (TU Delft - Team Peyman Mohajerin Esfahani, TU Delft - Team Bart De Schutter)
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Abstract
Estimating and detecting faults is crucial in ensuring safe and efficient automated systems. In the presence of disturbances, noise, or varying system dynamics, such estimation is even more challenging. To address this challenge, this letter proposes a novel filter to estimate multiple fault signals for a class of discrete-time linear parameter-varying (LPV) systems. The design of such a filter is formulated as an optimization problem and is solved recursively, while the system dynamics may vary over time. Conditions for the existence and detectability of the fault are introduced and the problem is formulated and solved using the quadratic programming framework. We further propose an approximate scheme that can be arbitrarily precise while it enjoys an analytical solution, which supports real-time implementation. The method is illustrated and validated on an automated vehicle's lateral dynamics, which is a practically relevant example for LPV systems. The results show that the estimation filter can decouple unknown disturbances and known or measurable parameter variations in the dynamics while estimating the unknown fault.