This paper formulates and studies the problem of controlling a networked SIS model using a single input in which the network structure is described by a connected undirected graph. A necessary and sufficient condition...
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Quantum Annealing (QA) was originally intended for accelerating the solution of combinatorial optimization tasks that have natural encodings as Ising models. However, recent experiments on QA hardware platforms have d...
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An improved active disturbance rejection controller based on the fractional order extended state observer(FOESO)is proposed in this *** the proposed FOESO,a second order plant model is converted into a cascaded frac...
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An improved active disturbance rejection controller based on the fractional order extended state observer(FOESO)is proposed in this *** the proposed FOESO,a second order plant model is converted into a cascaded fractional order integrator(1/s,0<α<1).Thus,a stable closed-loop feedback control system with enough phase margin for stability can be realized using a simple proportional ***,the open-loop phase-frequency characteristic of the system is flat around the gain crossover frequency,namely,the system is robust to loop gain ***,without the differential action in the designed controller,the control system achieves the robustness to high-frequency noise.
This paper investigates the wireless-powered hierarchical fog-cloud computing networks, where multiple energy-constrained users harvest energy from a hybrid access point (HAP) firstly and then use their harvested ener...
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Quantum error correction (QEC) is crucial for numerous quantum applications, including fault-tolerant quantum computation, which is of great scientific and industrial interest. Among various QEC paradigms, topological...
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This paper considers distributed online nonconvex optimization with time-varying inequality constraints over a network of agents, where the nonconvex local loss and convex local constraint functions can vary arbitrari...
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This paper considers distributed online nonconvex optimization with time-varying inequality constraints over a network of agents, where the nonconvex local loss and convex local constraint functions can vary arbitrarily across iterations, and the information of them is privately revealed to each agent at each iteration. For a uniformly jointly strongly connected time-varying directed graph, we propose two distributed bandit online primal–dual algorithm with compressed communication to efficiently utilize communication resources in the one-point and two-point bandit feedback settings, respectively. In nonconvex optimization, finding a globally optimal decision is often NP-hard. As a result, the standard regret metric used in online convex optimization becomes inapplicable. To measure the performance of the proposed algorithms, we use a network regret metric grounded in the first-order optimality condition associated with the variational inequality. We show that the compressed algorithm with one-point bandit feedback establishes an O(Tθ1) network regret bound and an O(T7/4−θ1) network cumulative constraint violation bound, where T is the number of iterations and θ1 ∈ (3/4,5/6] is a user-defined trade-off parameter. When Slater’s condition holds (i.e, there is a point that strictly satisfies the inequality constraints at all iterations), the network cumulative constraint violation bound is reduced to O(T5/2−2θ1). In addition, we show that the compressed algorithm with two-point bandit feedback establishes an O(Tmax{1−θ1,θ1}) network regret and an O(T1−θ1/2) network cumulative constraint violation bounds, where θ1 ∈ (0,1). Moreover, the network cumulative constraint violation bound is reduced to O(T1−θ1) under Slater’s condition. The bounds are comparable to the state-of-the-art results established by existing distributed online algorithms with perfect communication for distributed online convex optimization with inequality constraints. To the best of our knowledge, thi
Fuel cell hybrid vehicles are environmentally friendly and have good development prospects. Their energy management is very important, but there are currently few studies on this topic. In this paper, an optimized ene...
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We draw on the data collected by the Integrated Crisis Early Warning System on millions of international and regional public news stories, and this system's indicators of the orientation toward a specific nation-s...
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This paper establishes a methodology based on linear matrix inequalities (LMIs) to design a shifting H ∞ linear parameter varying (LPV) state-feedback controller for systems affected by time-varying input saturations...
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This paper establishes a methodology based on linear matrix inequalities (LMIs) to design a shifting H ∞ linear parameter varying (LPV) state-feedback controller for systems affected by time-varying input saturations. By means of the shifting paradigm, the instantaneous saturation values are linked to a scheduling parameter vector. Then, the disturbance rejection is dealt with the quadratic boundedness concept and the shifting H ∞ methodology. The design conditions are obtained within the LPV framework using ellipsoidal invariant sets, thus obtaining an LMI-based feasibility problem that can be solved via available solvers. Finally, the main characteristics of the proposed approach are validated by means of an illustrative example.
Discovering the antecedents of individuals’ influence in collaborative environments is an important, practical, and challenging problem. In this paper, we study interpersonal influence in small groups of individuals ...
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