Considering potential applications in power systems, economic dispatch problem (EDP) in time-varying balanced communication networks is studied, aiming to minimise the total cost of generating electricity. This is equ...
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Considering potential applications in power systems, economic dispatch problem (EDP) in time-varying balanced communication networks is studied, aiming to minimise the total cost of generating electricity. This is equivalent to dealing with the problem of optimising the sum of local functions while a single generator possesses only its local function. The variables of generators, satisfying some local constraints, are coupled by a linear constraint. In order to resolve the EDP, the authors design a fully distributed primal-dual optimisation algorithm with time-varying uncoordinated step-sizes. In consideration of saving computation and communication resources, an event-triggeredscheme is introduced into the algorithm, based on which each generator is only allowed to interact with their neighbouring generators at some independent event-triggered sampling time instants. The proposed algorithm is able to achieve a linear convergence rate under the strong convexity and smoothness of the local objective functions. The Zeno-like behaviour is rigorously excluded, which means that the interval between any two consecutive sampling time instants of each generator is not less than two. Effectiveness of the algorithm and correctness of the theoretical analysis are verified by numerical experiments.
This paper introduces a novel approach leveraging a sample -data -based, distributed, dynamic event -triggered framework to develop an H infinity asynchronous consensus controller tailored for singular Markov jump mul...
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This paper introduces a novel approach leveraging a sample -data -based, distributed, dynamic event -triggered framework to develop an H infinity asynchronous consensus controller tailored for singular Markov jump multi -agent systems (MJMASs). This approach primarily focuses on mitigating the communication load inherent in singular multi -agent systems. We employ a hidden Markov model (HMM) to adeptly manage the mode synchronization discrepancies between the controller and the system. We establish the stochastic admissibility with H infinity consensus performance of the singular consensus error system by deriving linear matrix inequalities (LMIs). Furthermore, we introduce a co -design methodology for optimizing controller gains, which simultaneously addresses the requirements of H infinity consensus and the dynamic event -triggered framework. The effectiveness of our proposed method are demonstrated through three numerical examples, each employing a different triggering mechanism.
This study investigates the distributedevent-driven filtering problem for a class of discrete-time systems in sensor networks (SNs) with switching topology. The addressed systems are considered to suffer from the unk...
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This study investigates the distributedevent-driven filtering problem for a class of discrete-time systems in sensor networks (SNs) with switching topology. The addressed systems are considered to suffer from the unknown nonrandom perturbations with bounded peak, and thus the existing filtering or estimation approaches for switched SNs become inapplicable. To tackle this situation, a novel reachable-set-based distributed filtering strategy is established. With the proposed piecewise Lyapunov function approach and the minimum dwell time switching mechanism, sufficient conditions are then formulated for the existence of admissible filters in both the secure and nonsecure communication channels. We prove that the resultant filtering error is ultimately confined to a bounded closed set, which can also be minimized to achieve an optimal perturbation attenuation level in the sense of reachable set. Finally, the practicability and effectiveness of the developed design technique are demonstrated via the distributedevent-driven filtering of a two-spring-mass mechanical system monitored by the SN.
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