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Data-Driven Control of Distributed Event-Triggered Network systems

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arXiv 2022年

作者： Wang, Xin Sun, Jian Wang, Gang Allgöwer, Frank Chen, Jie State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China Institute for Systems Theory and Automatic Control University of Stuttgart Stuttgart70550 Germany Department of Control Science and Engineering Tongji University Shanghai201804 China State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China

The present paper deals with data-driven event-triggered control of a class of unknown discrete-time interconnected systems (a.k.a. network systems). To this end, we start by putting forth a novel distributed event-triggering transmission strategy based on periodic sampling, under which a model-based stability criterion for the closed-loop network system is derived, by leveraging a discrete-time looped-functional approach. Marrying the model-based criterion with a data-driven system representation recently developed in the literature, a purely data-driven stability criterion expressed in the form of linear matrix inequalities (LMIs) is established. Meanwhile, the data-driven stability criterion suggests a means for co-designing the event-triggering coefficient matrix and the feedback control gain matrix using only some offline collected state-input data. Finally, numerical results corroborate the efficacy of the proposed distributed data-driven ETS in cutting off data transmissions and the co-design procedure. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear matrix inequalities

Path Planning for Robot Based on IACO-SFLA Hybrid Algorithm

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Path Planning for Robot Based on IACO-SFLA Hybrid Algorithm

Chinese Control and decision Conference, CCDC

作者： Xingcheng Pu Chaowen Xiong Longlong Zhao Key Laboratory of Intelligent Analysis and Decision on Complex Systems Chongqing University of Posts and Telecommunications Chongqing China School of Automation Chongqing University of Posts and Telecommunications Chongqing China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

In order to solve the problem of slow convergence, low efficiency and easily getting into local optimal solution in robot path planning which is based on basic ant colony algorithm, a new algorithm combining improved ant colony algorithm (IACO) with shuffled frog leaping algorithm (SFLA) is proposed. In the novel ant colony algorithm, dynamic state transition strategy, dynamic global pheromone updating strategy and dynamic pheromone evaporation factor updating strategy are used to ensure the accuracy of solution and the convergence speed of the algorithm. Furthermore, conditional fallback strategy is adopted to ensure the global search ability of the novel algorithm. Numerical experiments show that the optimal path can be more effectively generated by using the improved leapfrog algorithm and the simplified operator in the last step.

关键词： Heuristic algorithms Path planning Convergence System recovery Robots Telecommunications Optimization

Event-triggered Consensus Control of Heterogeneous Multi-agent systems: Model- and Data-based analysis

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arXiv 2022年

作者： Wang, Xin Sun, Jian Wang, Gang Chen, Jie The School of Automation The Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing10081 China The Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China The Department of Control Science and Engineering Tongji University Shanghai201804 China The State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China

This article deals with model- and data-based consensus control of heterogenous leader-following multi-agent systems (MASs) under an event-triggering transmission scheme. A dynamic periodic transmission protocol is developed to significantly alleviate the transmission frequency and computational burden, where the followers can interact locally with each other approaching the dynamics of the leader. Capitalizing on a discrete-time looped-functional, a model-based consensus condition for the closed-loop MASs is derived in form of linear matrix inequalities (LMIs), as well as a design method for obtaining the distributed controllers and event-triggering parameters. Upon collecting noise-corrupted state-input measurements during open-loop operation, a data-driven leader-following MAS representation is presented, and employed to solve the data-driven consensus control problem without requiring any knowledge of the agents' models. This result is then extended to the case of guaranteeing an H∞ performance. A simulation example is finally given to corroborate the efficacy of the proposed distributed event-triggering scheme in cutting off data transmissions and the data-driven design method. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear matrix inequalities

Solving Large-scale systems of Random Quadratic Equations via Stochastic Truncated Amplitude Flow

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Solving Large-scale Systems of Random Quadratic Equations vi...

European Signal Processing Conference

作者： Gang Wang Georgios B. Giannakis Jie Chen Dept. of ECE and Digital Tech. Center Univ. of Minnesota Mpls MN USA State Key Lab of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China

This work develops a new iterative algorithm, which is called stochastic truncated amplitude flow (STAF), to recover an unknown signal x ∈ R~n from m "phaseless" quadratic equations of the form ψ_i = |a_i~T x|, 1 ≤ i ≤ m. This problem is also known as phase retrieval, which is NP-hard in general. Building on an amplitude-based nonconvex least-squares formulation, STAF proceeds in two stages: s1) Orthogonality-promoting initialization computed using a stochastic variance reduced gradient algorithm;and, s2) Refinements of the initial point through truncated stochastic gradient-type iterations. Both stages handle a single equation per iteration, therefore lending STAF well to Big Data applications. Specifically for independent Gaussian {a_i}_(i=1)~m vectors, STAF recovers exactly any x exponentially fast when there are about as many equations as unknowns. Finally, numerical tests demonstrate that STAF improves upon its competing alternatives.

关键词： Stochastic processes Signal processing algorithms Runtime Mathematical model Computational complexity Europe Signal processing

An efficient framework for sentence similarity inspired by quantum computing

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An efficient framework for sentence similarity inspired by q...

IEEE International Conference on Big Knowledge (ICBK)

作者： Yan Yu Dong Qiu Ruiteng Yan School of computer science and technology Chongqing University of Posts and Telecommunications Nan'an Chongqing P.R. China Key Laboratory of Intelligent Analysis and Decision on Complex Systems Chongqing University of Posts and Telecommunications Nan'an Chongqing P.R. China

ISBN: (纸本)9781665438599

Accurately extracting the semantic information and the syntactic structure of sentences is important in natural language processing. Existing methods mainly combine the dependency tree to deep learning with complex computation time to achieve enough semantic information. It is essential to obtain sufficient semantic information and syntactic structures without any prior knowledge excepting word2vec. This paper proposes a model on sentence representation inspired by quantum entanglement using the tensor product to entangle both two consecutive notional words and words with depen-dencies. Inspired by quantum entanglement coefficients, we construct two different entanglement coefficients to weight the different semantic contributions of words with different relations. Finally, the proposed model is applied to SICK_train to verify their performances. The experimental results show that the provided methods achieve perfect results.

关键词： Dimensionality reduction Deep learning Tensors Quantum computing Quantum entanglement Computational modeling Semantics

Model Reconstruction Based on Physical-Informed Polynomial Neural ODEs

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Model Reconstruction Based on Physical-Informed Polynomial N...

Data Driven Control and Learning systems (DDCLS)

作者： Zhang Chen Wei Zhu Jingsui Li Hanlin Bian Chao Pei School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing P. R. China Key Laboratory of Intelligent Analysis and Decision on Complex Systems Chongqing University of Posts and Telecommunications Chongqing P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Neural networks are capable function approximators for data, but their lack of interpretability poses challenges. Physical-informed Polynomial Neural Ordinary Differential Equations (PIPNODEs) are proposed. They combine the constraints of prior knowledge of the data with interpretable Polynomial Neural Ordinary Differential Equations (PolyNODEs) to discover or approximate the governing equations. In this work, Neural Ordinary Differential Equations (NODEs) are categorized into two layers: a physical-informed term, which represents the priori knowledge of the data, and a discrepancy term, which is derived from the approximation of PolyNODEs. Pruning strategies are also used to remove redundant terms with coefficients lower than a certain threshold. We demonstrate the effectiveness and flexibility of this framework in a series of numerical algorithms with varying levels of prior knowledge. We found that having correct but incomplete physical information accelerates training and approaching thresholds compared to having incorrect physical information.

关键词： Training Accuracy Heuristic algorithms Ordinary differential equations Approximation algorithms Mathematical models Data models

Group Consensus of Fractional-Order Heterogeneous Multi-Agent systems with Random Packet Losses and Communication Delays

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SSRN

SSRN 2023年

作者： Sun, Fenglan Han, Yunpeng Wu, Xiaoshuai Zhu, Wei Kurths, Jürgen College of Automation Chongqing University of Posts and Telecommunications Chongqing400065 China Key Lab of Intelligent Analysis and Decision on Complex Systems School of Science Chongqing University of Posts and Telecommunications Chongqing400065 China Department of Complexity Science Potsdam Institute for Climate Impact Research Potsdam14473 Germany

The group consensus problem of heterogeneous fractional-order multi-agent systems with data packet losses and communication delays is investigated in this paper, and data packet losses are described by the Bernoulli-distribution. Inspired by genetic and the infinite memory property of the Caputo fractional derivative, a novel group consensus control protocol based on sampled data is designed. Sufficient conditions for mean-square group consensus of heterogeneous fractional-order multi-agent systems are derived using matrix theory, Gerschgorin disc theorem and graph theory. Finally, numerical simulation examples are given to verify the correctness of the theoretical results. © 2023, The Authors. All rights reserved.

关键词： Multi agent systems

Gauss-Newton unrolled neural networks and data-driven priors for regularized PSSE with robustness

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arXiv 2020年

作者： Yang, Qiuling Sadeghi, Alireza Wang, Gang Giannakis, Georgios B. Sun, Jian State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States

Renewable energy sources, elastic loads, and purposeful manipulation of meter readings challenge the monitoring and control of today’s power systems (PS). In this context, fast and robust state estimation (SE) is timely and of major importance to maintain a comprehensive view of the system in real time. Conventional PSSE solvers typically entail minimizing a nonlinear and nonconvex least-squares cost using e.g., the Gauss-Newton method. Those iterative solvers however, are sensitive to initialization, and may converge to local minima. To overcome these hurdles, the present paper adapts and leverages recent advances on image denoising to introduce a PSSE approach with a regularizer capturing a deep neural network (DNN) prior. For the resultant regularized PSSE objective, a "Gauss-Newton-type" alternating minimization solver is developed first. To accommodate real-time monitoring, a novel end-to-end DNN is constructed subsequently by unrolling the proposed alternating minimization solver. The deep PSSE architecture can further account for the power network topology through a graph neural network (GNN) based prior. To further endow the physics-based DNN with robustness against bad data, an adversarial DNN training method is put forth. Numerical tests using real load data on the IEEE 118-bus benchmark system showcase the improved estimation and robustness performance of the proposed scheme compared with several state-of-the-art alternatives. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

A statistical learning approach to reactive power control in distribution systems

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arXiv 2019年

Pronounced variability due to the growth of renewable energy sources, flexible loads, and distributed generation is challenging residential distribution systems. This context, motivates well fast, efficient, and robust reactive power control. Real-time optimal reactive power control is possible in theory by solving a non-convex optimization problem based on the exact model of distribution flow. However, lack of high-precision instrumentation and reliable communications, as well as the heavy computational burden of non-convex optimization solvers render computing and implementing the optimal control challenging in practice. Taking a statistical learning viewpoint, the input-output relationship between each grid state and the corresponding optimal reactive power control is parameterized in the present work by a deep neural network, whose unknown weights are learned offline by minimizing the power loss over a number of historical and simulated training pairs. In the inference phase, one just feeds the real-time state vector into the learned neural network to obtain the 'optimal' reactive power control with only several matrix-vector multiplications. The merits of this novel statistical learning approach are computational efficiency as well as robustness to random input perturbations. Numerical tests on a 47-bus distribution network using real data corroborate these practical merits. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks