An integrated distributed moving horizon estimation (DMHE) and model predictive control (DMPC) approach is developed for complex process networks using an adaptive spectral community detection-based decomposition. The...
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An integrated distributed moving horizon estimation (DMHE) and model predictive control (DMPC) approach is developed for complex process networks using an adaptive spectral community detection-based decomposition. The proposed approach employs the weighted graph representation of the process network model to identify optimal communities for distributed estimation and control architectures. The resulting decomposition dynamically adapts as the network transitions across different operating conditions. Consequently, adjustments are made to the integrated DMHE and DMPC architecture to optimize closed-loop performance and enhance robustness. A benchmark benzene alkylation process under various operating conditions is employed to substantiate the proposed methodology's efficacy. Simulation results demonstrate the effectiveness of the proposed method, showing improved closed-loop performance and computational efficiency compared to traditional unweighted hierarchical community detection-based decompositions. Copyright (c) 2024 The Authors.
In the present paper, we show two results concerning stability for a class of single linkage class chemical reaction networks (CRNs) with distributed time delays, all complexes of which are distinct. The dynamics of c...
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ISBN:
(纸本)9781713872344
In the present paper, we show two results concerning stability for a class of single linkage class chemical reaction networks (CRNs) with distributed time delays, all complexes of which are distinct. The dynamics of concentrations of species of the CRN with mass action kinetics (MAK) are described by the functional differential equations (FDEs) with distributed time delays for each reaction. As the first result, we show that any positive solution to the FDE of weakly reversible CRN globally converges to a positive equilibrium point in the functional state space. As the second result, we prove that any positive solution to the FDE of non-weakly reversible CRNs globally converges to a non-negative equilibrium point on the boundary of the positive orthant by decomposing the whole network into weakly reversible subnetworks and analyzing the stability of each subnetwork. Copyright (c) 2023 The Authors.
An integrated distributed moving horizon estimation (DMHE) and model predictive control (DMPC) approach is developed for complex process networks using an adaptive spectral community detection-based decomposition. The...
详细信息
An integrated distributed moving horizon estimation (DMHE) and model predictive control (DMPC) approach is developed for complex process networks using an adaptive spectral community detection-based decomposition. The proposed approach employs the weighted graph representation of the process network model to identify optimal communities for distributed estimation and control architectures. The resulting decomposition dynamically adapts as the network transitions across different operating conditions. Consequently, adjustments are made to the integrated DMHE and DMPC architecture to optimize closed-loop performance and enhance robustness. A benchmark benzene alkylation process under various operating conditions is employed to substantiate the proposed methodology’s efficacy. Simulation results demonstrate the effectiveness of the proposed method, showing improved closed-loop performance and computational efficiency compared to traditional unweighted hierarchical community detection-based decompositions.
Recently, a scalable approach to system analysis and controller synthesis for homogeneous multi-agent systems with Bernoulli distributed packet loss has been proposed. As a key result of that line of work, it was show...
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Recently, a scalable approach to system analysis and controller synthesis for homogeneous multi-agent systems with Bernoulli distributed packet loss has been proposed. As a key result of that line of work, it was shown how to obtain upper bounds on the H 2 -norm that are robust with respect to uncertain interconnection topologies. The main contribution of the current paper is to show that the same upper bounds hold not only for uncertain but also time-varying topologies that are superimposed on the stochastic packet loss. Because the results are formulated in terms of linear matrix inequalities that are independent of the number of agents, multi-agent systems of any size can be analysed efficiently. The applicability of the approach is demonstrated on a numerical first-order consensus example, on which the obtained upper bounds are compared to estimates from Monte-Carlo simulations.
In the present paper, we show two results concerning stability for a class of single linkage class chemical reaction networks (CRNs) with distributed time delays, all complexes of which are distinct. The dynamics of c...
详细信息
In the present paper, we show two results concerning stability for a class of single linkage class chemical reaction networks (CRNs) with distributed time delays, all complexes of which are distinct. The dynamics of concentrations of species of the CRN with mass action kinetics (MAK) are described by the functional differential equations (FDEs) with distributed time delays for each reaction. As the first result, we show that any positive solution to the FDE of weakly reversible CRN globally converges to a positive equilibrium point in the functional state space. As the second result, we prove that any positive solution to the FDE of non-weakly reversible CRNs globally converges to a non-negative equilibrium point on the boundary of the positive orthant by decomposing the whole network into weakly reversible subnetworks and analyzing the stability of each subnetwork.
In this paper, we extend a constraint-based coverage control for robotic sensor networks based on control barrier functions (CBFs) for environments with known obstacles and different types of unmanned vehicles (UV). T...
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In this paper, we extend a constraint-based coverage control for robotic sensor networks based on control barrier functions (CBFs) for environments with known obstacles and different types of unmanned vehicles (UV). To this end, we use a sensing function that considers the vertices of the obstacles to compute the route and the distance regarding the UVs. This way, obstacle avoidance becomes intrinsic to the CBF ensuring the coverage performance. Moreover, we consider different obstacles and speeds for each type of UV. Finally, the proposed algorithm is illustrated with a heterogeneous fleet of UVs and obstacles in a simulated thermosolar power plant.
In this paper, we address the problem of leader selection in a network of agents when the input signal can originate from different sources (for instance communication/control towers) and incur different costs. We ass...
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In this paper, we address the problem of leader selection in a network of agents when the input signal can originate from different sources (for instance communication/control towers) and incur different costs. We assume that each agent's state is defined by a scalar that evolves according to linear dynamics involving the states of its neighbors and its own state. We propose an algorithm to determine the minimum number of agents that should behave as leaders (i.e., agents whose states serve as references (or inputs) to the remaining agents-the followers), while incurring the lowest cost. Finally, illustrative examples using the main results of the paper are provided.
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