The measurement range of sensors based on optical resonator is often limited by the cavity linewidth. The usual PDH current feedback can extend the sensor's measurement range to the tuning range of the laser. Howe...
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This paper studies event-triggered consensus control for heterogenous nonlinear multi-agent systems. We present a new distributed nonlinear event-triggered control algorithm integrating basic radial basis function neu...
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ISBN:
(纸本)9781665436601
This paper studies event-triggered consensus control for heterogenous nonlinear multi-agent systems. We present a new distributed nonlinear event-triggered control algorithm integrating basic radial basis function neural network with event-based control. We show that it can handle any unknown dynamics linear in the control input, achieving practical consensus without Zeno behaviour. A numerical example is provided to highlight the effectiveness of the proposed algorithm in terms of learning the unknown nonlinear dynamics.
Noise is inherited in many optimization methods such as stochastic gradient methods, zeroth-order methods and compressed gradient methods. For such methods to converge toward a global optimum, it is intuitive to use l...
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Noise is inherited in many optimization methods such as stochastic gradient methods, zeroth-order methods and compressed gradient methods. For such methods to converge toward a global optimum, it is intuitive to use large step-sizes in the initial iterations when the noise is typically small compared to the algorithm-steps, and reduce the step-sizes as the algorithm progresses. This intuition has been con-firmed in theory and practice for stochastic gradient methods, but similar results are lacking for other methods using approximate gradients. This paper shows that the diminishing step-size strategies can indeed be applied for a broad class of noisy gradient methods. Unlike previous works, our analysis framework shows that such step-size schedules enable these methods to enjoy an optimal O(1/k) rate. We exemplify our results on zeroth-order methods and stochastic compression methods. Our experiments validate fast convergence of these methods with the step decay schedules.
In this paper, we provide a generalized framework for Variational Inference-Stochastic Optimal control by using the non-extensive Tsallis divergence. By incorporating the deformed exponential function into the optimal...
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Discrete abstractions have become a standard approach to assist control synthesis under complex specifications. Most techniques for the construction of a discrete abstraction for a continuous-time system require time-...
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Aiming at the difficulty of direct measurement of rolling bearing signal in offset printing system, a method of diagnosis and locating for rolling bearings based on texture analysis of sound field is proposed. First, ...
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Graph neural networks(GNNs) have shown great popularity and achieved promising performance on various graph-based tasks in the past years. However, there is little work that explores the information fusion mechanism, ...
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Graph neural networks(GNNs) have shown great popularity and achieved promising performance on various graph-based tasks in the past years. However, there is little work that explores the information fusion mechanism, which plays an import role in GNNs. Besides, datasets in the real world often have noises, which make the information fusion difficult. In this paper, we give an information-theoretic explanation. Specifically, we focus on how the information from topological structures and node features fuses and how different information contributes to the downstream task. Furthermore, we propose a general framework named M-GCN to express the fusion process in GNNs. Graph embeddings and feature graph are introduced to extract the information from topological structure and node features separately in M-GCN. Extensive experiments are conducted on several benchmark datasets and experimental results show that our proposed models are more robust and outperform state-of-the-art methods.
In this paper, we present a novel maximum entropy formulation of the Differential Dynamic Programming algorithm and derive two variants using unimodal and multimodal value functions parameterizations. By combining the...
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This paper proposes a maximum wind power injection (MVPI) problem under the engineering operation requirements of modern power systems and voltage stability constraints. To dig the access capacity of power grid for wi...
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ISBN:
(数字)9798350352290
ISBN:
(纸本)9798350352306
This paper proposes a maximum wind power injection (MVPI) problem under the engineering operation requirements of modern power systems and voltage stability constraints. To dig the access capacity of power grid for wind power, dynamic line rating (DLR) and optimal transmission switching (OTS) are jointly co-optimized to provide flexible operation of power systems. A novel DLR technique independent ambient parameter is proposed with the line parameter correction. The proposed problem is a mix-integer nonlinear programming problem that is hard to solve directly. Hence, a three-stage methodology is developed, which has been applied on the IEEE 24-bus RTS system, the IEEE 118-bus power system. The computational results show the effectiveness of the conducted model in maximizing the usage of wind power.
We investigate the problem of designing optimal stealthy poisoning attacks on the control channel of Markov decision processes (MDPs). This research is motivated by the recent interest of the research community for ad...
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