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As most of the safety critical industrial systems remain sensitive to functional degradation and operate under closed loop, it becomes imperative to take into account the state of health within the control design process. To that end, an effective assessment and extension of the Remaining Useful Life (RUL) of complex systems is a standing challenge that seeks novel solutions at the cross-over of Prognostics and Health Management (PHM) domain as well as automatic control. This paper considers a dynamical system subjected to functional degradation presents a novel control design strategy. Wherein the assessment of state of health of the system is taken into account leading to effective prediction of the RUL as well as its extension. To that end, the degradation model is considered unknown but input-dependent. The control design is formulated as an optimization problem wherein a suitable comprise is reached between the performance and desired RUL of the system. The main contribution of the paper remains in proposal of set-point modulation based approach wherein the control input at a given present time stage is modulated in such way that futuristic health of the system over a long time horizon is extended whilst assuring acceptable performance. The effectiveness of the proposed strategy is assessed in simulation using a numerical example as well as liquid propellant rocket engine case.

In this paper, a robust leader-following consensus protocol for multi-agent systems (MASs) subject to faults in the information exchange and disturbance is presented. The problem under consideration is to guarantee the convergence of the agent trajectories to a leader agent when all the agent followers are under faults in the information exchange as smooth time-varying delays and disturbances. The main contribution in this paper is the design of a robust leader-following control through the Lyapunov approach and an optimal $\mathscr {H}_{\infty }$ criterion such that all agents follow a virtual leader agent despite faults in the information exchange and disturbances. Linear matrix inequalities (LMIs)-based conditions are obtained whose solution allows computing the robust controller gain. In order to show the effectiveness of the proposed approach, numerical examples are carried out comparing a state-of-the-art approach and the proposed strategy in a fleet of unmanned aerial vehicles (UAVs) subject to wind turbulence which are shown to achieve the formation control.

En este artículo se presenta el desarrollo de un método de diagnóstico de fallas para sistemas no lineales representado por el enfoque de múltiples modelos. La principal contribución consiste en el diseño de un esquema de diagnóstico de fallas (FDD) a través de un filtro adaptativo desacoplado de fallas. El comportamiento del sistema está dado mediante múltiples modelos lineales alrededor de diferentes puntos de operación. Los residuos se establecen de tal manera que permiten estimar funciones de activación robustas a fallas. El filtro adaptivo diseñado permite la detección, localización y estimación de fallas múltiples o simultáneas. Las condiciones de estabilidad del filtro son estudiadas a través de LMI (Linear Matrix Inequality). El desempeño del esquema propuesto se valida en un sistema de tres tanques.

En este artículo se desarrolla un método para la representación de sistemas no lineales, así como un esquema de diagnóstico de fallas; en donde ambos utilizan un enfoque multi-modelos. El comportamiento dinámico del sistema se describe mediante el uso de diferentes puntos de operación por cada modelo lineal invariante en el tiempo. Por cada modelo un filtro de Kalman desacoplado de fallas es utilizado. Esto permite la generación de un banco de residuos con la finalidad de estimar funciones de ponderación robusta a fallas. Estas últimas se enlazan directamente al esquema de detección, localización y estimación de fallascon objeto de mejorar su eficacia. Finalmente, se realiza un ejemplo de simulación a fin de probar el desempeño del método desarrollado.