New nonlinear residual feedback observer for fault diagnosis in nonlinear systems

et al. 2018

Information Sciences

This paper addresses the problem of reducing the required network load and computational power for the implementation of Set-Valued Observers (SVOs) in Networked Control System (NCS). Event-and self-triggered strategies for NCS, modeled as discrete-time Linear Parameter-Varying (LPV) systems, are studied by showing how the triggering condition can be selected. The methodology provided can be applied to determine when it is required to perform a full ("classical") computation of the SVOs, while providing low-complexity state overbounds for the remaining time, at the expenses of temporarily reducing the estimation accuracy. As part of the procedure, an algorithm is provided to compute a suitable centrally symmetric polytope that allows to find hyper-parallelepiped and ellipsoidal overbounds to the exact set-valued state estimates calculated by the SVOs. By construction, the proposed triggering techniques do not influence the convergence of the SVOs, as at some subsequent time instants, set-valued estimates are computed using the conventional SVOs. Results are provided for the triggering frequency of the self-triggered strategy and two interesting cases: distributed systems when the dynamics of all nodes are equal up to a reordering of the matrix; and when the probability distribution of the parameters influencing the dynamics is known. The performance of the proposed algorithm is demonstrated in simulation by using a time-sensitive example.

Section: Introductionmentioning

confidence: 99%

Self-Triggered and Event-Triggered Set-Valued Observers

et al. 2018

Information Sciences

“…Classical fault detection methods such as the ones proposed in [3], [5], [6], [7], [8], [9] and [10], rely on designing filters that generate residuals that should be large under faulty conditions. These strategies aim to derive bounds (or thresholds) on these residuals that can be used to decide whether a fault has occurred or not.…”

Section: Introductionmentioning

confidence: 99%

Fault detection for LPV systems using Set-Valued Observers: A coprime factorization approach

Systems & Control Letters

et al. 2017

This paper addresses the problem of fault detection for linear parameter-varying systems in the presence of measurement noise and exogenous disturbances using Set-Valued Observers (SVOs). The applicability of current methods is limited in the sense that, to increase accuracy, the detection requires a large number of past measurements and the boundedness of the set-valued estimates is only guaranteed for stable systems. In order to widen the class of systems to be modeled and also to reduce the associated computational cost, the aforementioned issues must be addressed. A solution involving left-coprime factorization and deadbeat observers is proposed that reduces the required number of past measurements without compromising accuracy and allowing the design of SVOs for fault detection of unstable systems by using the resulting coprime factorization stable subsystems. The algorithm is shown to produce bounded set-valued estimates and an example is provided. Performance is assessed through simulations, illustrating, in particular that small-magnitude faults (compared to exogenous disturbances) can be detected under mild assumptions.

“…Classical fault detection methods such as the ones proposed in [3], [5] and [6], rely on designing filters that generate residuals that should be large under faulty environments. Calculating thresholds for the residuals is typically cumbersome or poses stringent assumptions on the exogenous disturbances and measurement noise acting upon the system.…”

Section: Introductionmentioning

confidence: 99%

Self-Triggered Set-Valued Observers

2015 European Control Conference (ECC)

et al. 2015

Abstract-This paper addresses the problem of high computational requirements in the implementation of Set-Valued Observers (SVOs), which places stringent constraints in terms of their use in applications where low computational power is available or the plant is sensitive to delay. It is firstly shown how to determine an overbound for the set-valued estimates, which reduces the overhead by limiting the number of inequalities defining those set-valued state estimates. In the particular setting of distributed gossip problems, the proposed algorithm is shown to have constant complexity. This algorithm is of prime importance to reduce the computational load and enable the use of such estimates for real-time applications. Results are also provided regarding the frequency of the triggers in the worst-case scenario. The performance of the proposed method is evaluated through simulation.