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This paper addresses the co-design of event-triggered dynamic output-feedback (DOF) control for discrete-time linear parameter-varying (LPV) systems. Two independent event-triggering schemes are introduced to determine when to transmit data from the sensor to the controller and from the controller to the actuator channels. Enhanced conditions employing linear matrix inequalities (LMIs) are derived to establish constructive criteria for simultaneously designing the gain-scheduled DOF controller and the triggering mechanisms, ensuring the asymptotic stability of the closed-loop LPV system's origin. Additionally, a convex optimisation problem is proposed to enlarge the inter-event sampling on the independent channels of sensor-to-controller and controller-to-actuator. Numerical examples are provided to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This article proposes a novel approach for designing a mode‐dependent H∞$\mathcal {H}_\infty$ full‐order dynamic filter for a cyber‐physical system (CPS) that is subject to polytopic uncertainties. The CPS operates on an unreliable network that is susceptible to transmission failures and Denial of Service (DoS) attacks. The attackers have limited energy resources, and the duration of the DoS attack is limited to a maximum number of consecutive time instants. The network is modeled after a proposed non‐homogeneous Markov chain whose transition probability matrix may feature uncertain and unknown probabilities, which are dependent on time‐varying parameters. The design conditions for the filter are obtained using parameter‐dependent linear matrix inequalities. The proposed filter is shown to be effective in reducing the impact of DoS attacks and transmission failures on the CPS. Numerical experiments are presented to illustrate the efficacy of the proposed filter design method, demonstrating its ability to mitigate the effects of uncertainties and attacks on the CPS. [ABSTRACT FROM AUTHOR]

Low-order ignition models are important tools in the design of aviation gas turbines. In this paper, a stochastic model that predicts the ignition probability in a combustor based on a time-averaged cold-flow solution is extended to include local fuel concentration fluctuations due to the polydisperse nature of the spray. For this, a stochastic approach to modeling such fluctuations is considered, and the effects of the flow and mixture parameters on the resulting equivalence ratio pdfs are investigated. The concentration of fuel in large droplets results in a high variation of the local equivalence ratio, hence affecting the local flammability factor at the model's cell scale. The extinction criterion of the ignition model based on a critical Karlovitz number is calibrated based on ignition probability data from canonical experiments using jet fuel, suggesting critical Karlovitz values of spray flames between 0.2 and 0.6, which is to be contrasted with values of 1.5 for gaseous fuels. [ABSTRACT FROM AUTHOR]

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