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Fixed-time synchronization of neural networks with time delay via quantized intermittent control

ASIAN JOURNAL OF CONTROL 2023年第3期25卷 1823-1833页

作者： Yang, Wenqiang Huang, Junjian Wen, Shiping He, Xing Southwest Univ Chongqing Key Lab Nonlinear Circuits & Intelligen Coll Elect & Informat Engn Chongqing 400715 Peoples R China Univ Technol Sydney Fac Engn & Informat Technol Ctr Artificial Intelligence Sydney NSW Australia

This paper presents an approach for fixed-time synchronization (FIXTS) of neural networks (NNs) by designing quantized intermittent controller. Under the intermittent controller, the synchronization between neural network systems with time delay can be realized. Based on intermittent strategy, FIXTs theory is proposed, and a sufficient condition is established to realize the FIXTS of the master-slave NNs. At the same time, the establishment time of FIXTS is estimated. Finally, the simulation of Gilli attractor to prove the validity of the proposed method.

关键词： delay fixed-time neural networks synchronization quantized intermittent controller

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Accelerated Row-stochastic Optimization over Directed Graphs with Uncoordinated Step Sizes 16

Accelerated Row-stochastic Optimization over Directed Graphs...

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16th IEEE International Conference on Control and Automation (ICCA)

作者： Zhou, Kang Du, Zhenyuan Li, Huaqing Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China

ISBN: (纸本)9781728190938

This paper investigates a distributed optimization problem over a multiagent network, in which the target of agents is to collaboratively optimize the sum of all local objective functions. The case discusses that the network topology among agents is described by a strongly connected directed graph. The proposed algorithm utilizes row-stochastic weight matrices and uncoordinated step sizes. Under conditions that the objective functions are strong convex, and have Lipschitz continuous gradients, we manifest that proposed algorithm faster linearly converges to the global optimization solution than other algorithms as long as the chosen step sizes do not exceed an exact characterized upper bound. Numerical experiments are also provided to testify the theoretical analysis.

关键词： Directed graphs

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A Distributed Dual Proximal Algorithm for Non-Smooth Composite Constrained Optimization and Its Application 40

A Distributed Dual Proximal Algorithm for Non-Smooth Composi...

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40th Chinese Control Conference (CCC)

作者： Ran, Liang Hu, Jinhui Liu, Hongli Li, Huaqing Southwest Univ Chongqing Key Lab Nonlinear Circuits & Intelligen Coll Elect & Informat Engn Chongqing 400715 Peoples R China

ISBN: (纸本)9789881563804

Distributed optimization is a promising prospect that shows high potential in various applications, such as machine learning, power systems and others. This paper studies a class of constrained composite optimization problems over a network of agents, in which the local cost functions that are privately maintained by agents can be split into a strongly convex term and a convex (or possibly non-smooth) one. To tackle this problem in a distribute manner, a class of synchronous distributed algorithms based on dual proximal methods (SynDis-DuPro) is presented, and its asynchronous version (AsynDis-DuPro) without the global clock is developed as well. Under the proposed schemes, each agent in the network not only possesses the individual step-sizes, but also conducts local computation and communication without leaking its private information. In addition, the practicability and effectiveness of the proposed algorithms are demonstrated by the simulations on distributed energy resources coordination (DERC) based on power systems.

关键词： Convex optimization multi-agent systems distributed algorithms non-smooth constrained optimization problems

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Consensus of a novel heuristic nonlinear multi-agent system in DOS attack network environment via saturation impulse control mechanism

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ISA TRANSACTIONS 2024年 147卷 1-12页

作者： Hu, Xiang Xiong, Yu Zhang, Zufan Li, Chuandong Chongqing Univ Posts & Telecommun Sch Automat Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing 400065 Peoples R China Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China

This paper mainly studies the consensus control strategy for a novel heuristic nonlinear multi-agent system. Compared with most existing related researches, firstly, the novel heuristic nonlinear multi-agent system has the ability to construct its communication network topology heuristically, and can withstand long-term DOS(Denial of Service) attacks, with the advantages of high practicality and security. Secondly, in order to control the multi-agent system, a control protocol based on both saturation effect and impulse control mechanism is studied, which has the advantages of high efficiency, low cost and wide applicability. Thirdly, for the multi-agent system, its dynamic model is constructed and analyzed by Lyapunov stability theory and matrix measure theory, and some sufficient conditions for achieving consensus are obtained. Finally, through two simulation experiments and some corresponding comparative analysis, the correctness, efficiency, and superiority of the theories proposed in this paper were verified.

关键词： Consensus of multi-agent system Heuristic communication network Saturation effect Impulse control mechanism DOS attack

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Convergence of an accelerated distributed optimisation algorithm over time-varying directed networks

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IET CONTROL THEORY AND APPLICATIONS 2021年第1期15卷 24-39页

作者： Hu, Jinhui Yan, Yu Li, Huaqing Wang, Zheng Xia, Dawen Guo, Jing Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China Guizhou Minzu Univ Coll Data Sci & Informat Engn Guiyang 550025 Peoples R China

In this article, studying distributed optimisation over time-varying directed networks where a group of agents aims at cooperatively minimising a sum of local objective functions is focused on. Each agent uses only local computation and communication in the overall process without leaking their private information. Via incorporating both a distributed heavy-ball method and a distributed Nesterov method, a double accelerated distributed algorithm leveraging a gradient-tracking technique and using uncoordinated step-sizes, is developed. By employing both row- and column-stochastic weight matrices, the proposed algorithm can bypass the implementation of doubly stochastic weight matrices and avoid eigenvector estimation existing in some algorithms using only row- or column-stochastic weight matrices. Under the assumptions that the agents' local objective functions are smooth and strongly convex, and the aggregated directed networks of every finite consecutive directed network are strongly connected, the proposed algorithm is proved to converge linearly to the global optimal solution when the largest step-size is positive and sufficiently small, and the largest momentum parameter is non-negative. The proposed algorithm is also applied to fixed directed networks which are considered as a special case of time-varying directed networks. Simulation results further verify the effectiveness of the proposed algorithm and correctness of the theoretical findings.

关键词： Optimisation techniques Interpolation and function approximation (numerical analysis) Optimisation techniques Other topics in statistics Combinatorial mathematics

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Distributed optimal consensus algorithms in multi-agent systems

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NEUROCOMPUTING 2019年 339卷 26-35页

作者： Wang, Aijuan Dong, Tao Liao, Xiaofeng Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China Chongqing Changan Automobile Co Ltd Informat Technol Ctr Chongqing 401220 Peoples R China

This paper investigates the distributed optimal consensus problem by designing the multi-agent communication systems, where each agent has local access to its own strongly convex utility function. Here, we design two distributed consensus-based optimization algorithms to tackle the optimization problem of multi-agent systems. One is the second-order optimization consensus algorithm, and another is the third-order algorithm. Moreover, the two algorithms can ensure that all agents achieve consensus at a common state which minimizes the sum of the objective function under the assumption that interaction graph is strongly connected and detailed balanced. For each of the proposed algorithms, it can be found that the equilibrium point of the algorithm is the optimal point and it is proved to be asymptotically stable under given conditions. Finally, the performance of the proposed algorithms are verified by two simulations based on an IEEE 14-bus test system and an IEEE 57-bus test system. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Multi-agent systems (MASs) Second-order (Third-order) optimal consensus algorithm Detailed balanced graph

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Event-Based ADP Tracking Control of Complex-Valued nonlinear Systems

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2024年第2期8卷 1086-1096页

作者： Dong, Tao Li, Kai Huang, Tingwen Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Southwest Univ Coll Westa Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China Texas A&M Univ Qatar Dept Math Doha 23874 Qatar

This paper addresses the tracking control problem of a complex-valued nonlinear system. First, an augmented complex-valued tracking error system is constructed. By separating the real part and the imaginary part, the augmented complex-valued system is translated into a real-valued augmented system. The tracking control problem of a complex-valued system is converted into an optimal regulation of the real-valued augmented system. An event-based controller is proposed to solve the optimal regulation of the augmented system. Then, an ADP-based framework is used to realize the controller, which is composed of an action network and a critic network. The action network is specially formulated for obtaining control input and the critic network is specially formulated for generating performance value. Both two networks are complex-valued neural networks. Through well-designed event-triggered conditions, the tracking error is proved to be uniformly ultimately bounded (UUB) and the Zeno behaviors are excluded. The algorithm is applied to complex-valued signal tracking to demonstrate its effectiveness.

关键词： ADP augmented system complex-valued system event-triggered tracking control

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Existence of solution, pulse phenomena and stability criteria for state-dependent impulsive differential equations with saturation

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COMMUNICATIONS IN nonlinear SCIENCE AND NUMERICAL SIMULATION 2019年第0期77卷 312-323页

作者： Li, Liangliang Li, Chuandong Zhang, Wei Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China Chongqing Three Gorges Univ Sch Math & Stat Chongqing 404100 Peoples R China Chongqing Three Gorges Univ Inst Higher Educ Key Lab Intelligent Informat Proc & Control Chong Chongqing 404100 Peoples R China

This paper discusses the problem of the state-dependent impulsive differential equations (SDIDE) with saturation. First, the existence of solution for the system and some sufficient conditions which can be guaranteed every solution intersecting each impulsive surface exactly once are derived. Second, combining with comparison principle and some inequality techniques, we give some local quasi-exponential stability for SDIDE with saturation. Moreover, this paper proposes some succinct results by comparing to those for fixed time impulsive dynamical systems. Finally, some examples with simulations are given to demonstrate the effectiveness of our results. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Impulsive differential equations (IDE) State-dependent impulses Saturation Quasi-exponential stability Collision surface

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Distributed output feedback leader-following consensus for nonlinear multiagent systems with time delay

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nonlinear DYNAMICS 2021年第2期105卷 1673-1687页

作者： Tan, Lihua Li, Chuandong He, Xing Huang, Tingwen Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China Texas A&M Univ Qatar POB 23874 Doha Qatar

This paper concentrates on the output feedback control problem for a class of nonlinear multiagent systems governed by the high-order strict-feedback model with time delay. Within the dynamic gain technique and the Lyapunov-like method, the dynamic gain state observer for each agent is put forward with the hope to compensate the impact induced by the immeasurable state variables, and then the distributed leader-following consensus protocols which are independent of the time delay on the agent state are designed such that the output of each follower can asymptotically track that of the leader. Besides, the problem considered is extended into the general case where the Lipschitz growth rates of the nonlinear function are unknown time-varying functions. Finally, simulation examples are performed to illustrate the validity and effectiveness of the proposed approach.

关键词： Leader-following consensus Dynamic gain Output feedback Time delay

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A Neurodynamic Algorithm for Energy Scheduling Game in Microgrid Distribution Networks

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NEURAL PROCESSING LETTERS 2022年第1期54卷 369-385页

作者： Wen, Shifan He, Xing Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China Southwestern Univ Finance & Econ Sch Econ Informat & Engn Chengdu 611130 Peoples R China

In this paper, a competitive energy scheduling strategy game of N-microgrids (MGs) inside a distributed network is considered. Each microgrid (MG) aims to maximize its profit under the noncooperative game frame. The strategy-making of each MG depends on its equipment constraints, the aggregate energy supplies of all MGs, and the energy balance of supplies and demands. To solve above discussed problem, a noncooperative game with linear coupled constraints and a distributed neurodynamic algorithm are proposed to seek the generalized Nash equilibrium (GNE). Besides, the correctness and convergence of the proposed algorithm are analyzed in detail. The effectiveness and feasibility of the proposed method are also illustrated via the simulation example.

关键词： Neurodynamic algorithm Energy scheduling game Microgrid distribution network

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