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Adaptive distributed optimization of high-order nonlinear multi-agent systems with predefined accuracy under state observer 认领 引用
1
作者 Xiao-Wen Zhao Tong Shu +2 位作者 Deng-Hao Pang Tao Li Mei Yao 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第3期236-247,共12页
This paper discusses adaptive distributed optimization with predefined accuracy for high-order nonlinear multi-agent systems(MASs)that are subject to disturbances and nonlinear uncertainties.To estimate the global opt... This paper discusses adaptive distributed optimization with predefined accuracy for high-order nonlinear multi-agent systems(MASs)that are subject to disturbances and nonlinear uncertainties.To estimate the global optimal solution in realtime,a distributed proportional-integral optimization technique is used to generate a virtual system for each agent.For the unknown control gain of the controller,the Nussbaum function is employed.Then,a fuzzy adaptive observer is designed to estimate the unmeasured state by leveraging the general approximation capabilities of fuzzy logic systems.Using the Lyapunov stability method and backstepping technique,we develop the adaptive law and a new distributed controller.This ensures that the outputs of multi-agent systems converge to optimal values.Finally,a simulation example is used to confirm the viability of the presented control mechanism. 展开更多
关键词 distributed optimization nonlinear multi-agent systems(MASs) fuzzy state observer predefined accuracy control
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Uncertainty-Aware Distributed Optimization for IoEV Smart Charging and Battery Health Management in Cyber-Physical Smart Grids 认领 引用
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作者 Supriya Wadekar Shailendra Mittal +3 位作者 Ganesh Wakte Mrunali Kite Aditya Ghonmode Riya Devkate 《Energy Engineering》 EI 2026年第9期157-192,共36页
The rapid expansion of electric vehicles(EVs)and the emergence of the Internet of Electric Vehicles(IoEV)have created considerable operational challenges for modern power systems.Large-scale EV charging can cause peak... The rapid expansion of electric vehicles(EVs)and the emergence of the Internet of Electric Vehicles(IoEV)have created considerable operational challenges for modern power systems.Large-scale EV charging can cause peak demand surges,voltage instability,and inefficient utilization of renewable energy resources when charging activities are not effectively coordinated.This study proposes an uncertainty-aware distributed optimization framework for smart EV charging in cyber-physical smart grids,in which charging schedules are coordinated while simultaneously considering grid capacity constraints,stochastic EV arrival patterns,renewable energy variability,and battery degradation effects.A multi-objective optimization model is formulated to minimize peak grid load,charging cost,and battery degradation.The optimization problem is solved using a distributed algorithm based on the Alternating Direction Method of Multipliers,enabling scalable coordination among multiple charging stations.Simulation studies were carried out in the MATLAB-Simulink environment with EV fleet sizes ranging from 100 to 500 vehicles integrated with solar photovoltaic generation.The results indicate a 39.2%reduction in peak feeder load,a 24%decrease in total charging cost,and an improvement in renewable energy utilization to 76.9%.In addition,the proposed framework reduces annual battery capacity loss from 6.7%to 3.8%compared with uncontrolled charging.The primary contribution of this work is the integration of uncertainty modeling,distributed optimization,and battery health prediction within a unified IoEV charging management framework. 展开更多
关键词 Battery degradation distributed optimization electric vehicle charging internet of electric vehicles(IoEV) smart grid uncertainty-aware optimization
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Predefined-Time Distributed Optimization for Resource Allocation Problems With Time-Varying Objective Function and Constraints 认领 引用
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作者 Haotian Wu Yang Liu +1 位作者 Mahmoud Abdel-Aty Weihua Gui 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第11期2353-2355,共3页
Dear Editor,This letter addresses distributed optimization for resource allocation problems with time-varying objective functions and time-varying constraints.Inspired by the distributed average tracking(DAT)approach,... Dear Editor,This letter addresses distributed optimization for resource allocation problems with time-varying objective functions and time-varying constraints.Inspired by the distributed average tracking(DAT)approach,a distributed control protocol is proposed for optimal resource allocation.The convergence to a time-varying optimal solution within a predefined time is proved.Two numerical examples are given to illustrate the effectiveness of the proposed approach. 展开更多
关键词 resource allocation distributed optimization time varying objective function optimal resource allocationthe distributed control protocol time varying constraints predefined time convergence
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Distributed optimization of electricity-Gas-Heat integrated energy system with multi-agent deep reinforcement learning 认领 引用 被引量：6
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作者 Lei Dong Jing Wei +1 位作者 Hao Lin Xinying Wang 《Global Energy Interconnection》 EI CSCD 2022年第6期604-617,共14页
The coordinated optimization problem of the electricity-gas-heat integrated energy system(IES)has the characteristics of strong coupling,non-convexity,and nonlinearity.The centralized optimization method has a high co... The coordinated optimization problem of the electricity-gas-heat integrated energy system(IES)has the characteristics of strong coupling,non-convexity,and nonlinearity.The centralized optimization method has a high cost of communication and complex modeling.Meanwhile,the traditional numerical iterative solution cannot deal with uncertainty and solution efficiency,which is difficult to apply online.For the coordinated optimization problem of the electricity-gas-heat IES in this study,we constructed a model for the distributed IES with a dynamic distribution factor and transformed the centralized optimization problem into a distributed optimization problem in the multi-agent reinforcement learning environment using multi-agent deep deterministic policy gradient.Introducing the dynamic distribution factor allows the system to consider the impact of changes in real-time supply and demand on system optimization,dynamically coordinating different energy sources for complementary utilization and effectively improving the system economy.Compared with centralized optimization,the distributed model with multiple decision centers can achieve similar results while easing the pressure on system communication.The proposed method considers the dual uncertainty of renewable energy and load in the training.Compared with the traditional iterative solution method,it can better cope with uncertainty and realize real-time decision making of the system,which is conducive to the online application.Finally,we verify the effectiveness of the proposed method using an example of an IES coupled with three energy hub agents. 展开更多
关键词 Integrated energy system Multi-agent system Distributed optimization Multi-agent deep deterministic policy gradient Real-time optimization decision
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Fully privacy-preserving distributed optimization in power systems based on secret sharing 认领 引用 被引量：5
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作者 Nianfeng Tian Qinglai Guo +1 位作者 Hongbin Sun Xin Zhou 《iEnergy》 2022年第3期351-362,共12页
With the increasing development of smart grid,multi-party cooperative computation between several entities has become a typical characteristic of modern energy systems.Traditionally,data exchange among parties is inev... With the increasing development of smart grid,multi-party cooperative computation between several entities has become a typical characteristic of modern energy systems.Traditionally,data exchange among parties is inevitable,rendering how to complete multi-party collaborative optimization without exposing any private information a critical issue.This paper proposes a fully privacy-preserving distributed optimization framework based on secure multi-party computation(SMPC)with secret sharing protocols.The framework decomposes the collaborative optimization problem into a master problem and several subproblems.The process of solving the master problem is executed in the SMPC framework via the secret sharing protocols among agents.The relationships of agents are completely equal,and there is no privileged agent or any third party.The process of solving subproblems is conducted by agents individually.Compared to the traditional distributed optimization framework,the proposed SMPC-based framework can fully preserve individual private information.Exchanged data among agents are encrypted and no private information disclosure is assured.Furthermore,the framework maintains a limited and acceptable increase in computational costs while guaranteeing opti-mality.Case studies are conducted on test systems of different scales to demonstrate the principle of secret sharing and verify the feasibility and scalability of the proposed methodology. 展开更多
关键词 Secure multi-party computation privacy preservation secret sharing distributed optimization.
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Random gradient-free method for online distributed optimization with strongly pseudoconvex cost functions 认领 引用
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作者 Xiaoxi Yan Cheng Li +1 位作者 Kaihong Lu Hang Xu 《Control Theory and Technology》 EI CSCD 2024年第1期14-24,共11页
This paper focuses on the online distributed optimization problem based on multi-agent systems. In this problem, each agent can only access its own cost function and a convex set, and can only exchange local state inf... This paper focuses on the online distributed optimization problem based on multi-agent systems. In this problem, each agent can only access its own cost function and a convex set, and can only exchange local state information with its current neighbors through a time-varying digraph. In addition, the agents do not have access to the information about the current cost functions until decisions are made. Different from most existing works on online distributed optimization, here we consider the case where the cost functions are strongly pseudoconvex and real gradients of the cost functions are not available. To handle this problem, a random gradient-free online distributed algorithm involving the multi-point gradient estimator is proposed. Of particular interest is that under the proposed algorithm, each agent only uses the estimation information of gradients instead of the real gradient information to make decisions. The dynamic regret is employed to measure the proposed algorithm. We prove that if the cumulative deviation of the minimizer sequence grows within a certain rate, then the expectation of dynamic regret increases sublinearly. Finally, a simulation example is given to corroborate the validity of our results. 展开更多
关键词 Multi-agent system Online distributed optimization Pseudoconvex optimization Random gradient-free method
Distributed optimization for discrete-time multiagent systems with nonconvex control input constraints and switching topologies 认领 引用
7
作者 Xiao-Yu Shen Shuai Su Hai-Liang Hou 《Chinese Physics B》 SCIE EI CAS CSCD 2021年第12期283-290,共8页
This paper addresses the distributed optimization problem of discrete-time multiagent systems with nonconvex control input constraints and switching topologies.We introduce a novel distributed optimization algorithm w... This paper addresses the distributed optimization problem of discrete-time multiagent systems with nonconvex control input constraints and switching topologies.We introduce a novel distributed optimization algorithm with a switching mechanism to guarantee that all agents eventually converge to an optimal solution point,while their control inputs are constrained in their own nonconvex region.It is worth noting that the mechanism is performed to tackle the coexistence of the nonconvex constraint operator and the optimization gradient term.Based on the dynamic transformation technique,the original nonlinear dynamic system is transformed into an equivalent one with a nonlinear error term.By utilizing the nonnegative matrix theory,it is shown that the optimization problem can be solved when the union of switching communication graphs is jointly strongly connected.Finally,a numerical simulation example is used to demonstrate the acquired theoretical results. 展开更多
关键词 multiagent systems nonconvex input constraints switching topologies distributed optimization
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Distributed Optimization for Heterogenous Second⁃Order Multi⁃Agent Systems 认领 引用
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作者 Qing Zhang Zhikun Gong +1 位作者 Zhengquan Yang Zengqiang Chen 《Journal of Harbin Institute of Technology(New Series)》 CAS 2020年第4期53-59,共7页
A continuous⁃time distributed optimization was researched for second⁃order heterogeneous multi⁃agent systems.The aim of this study is to keep the velocities of all agents the same and make the velocities converge to t... A continuous⁃time distributed optimization was researched for second⁃order heterogeneous multi⁃agent systems.The aim of this study is to keep the velocities of all agents the same and make the velocities converge to the optimal value to minimize the sum of local cost functions.First,an effective distributed controller which only uses local information was designed.Then,the stability and optimization of the systems were verified.Finally,a simulation case was used to illustrate the analytical results. 展开更多
关键词 distributed optimization heterogeneous multi⁃agent system local cost function consensus
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Fully asynchronous distributed optimization with linear convergence over directed networks 认领 引用
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作者 SHA Xingyu ZHANG Jiaqi YOU Keyou 《中山大学学报（自然科学版）（中英文）》 CAS CSCD 北大核心 2023年第5期1-23,共23页
We study distributed optimization problems over a directed network,where nodes aim to minimize the sum of local objective functions via directed communications with neighbors.Many algorithms are designed to solve it f... We study distributed optimization problems over a directed network,where nodes aim to minimize the sum of local objective functions via directed communications with neighbors.Many algorithms are designed to solve it for synchronized or randomly activated implementation,which may create deadlocks in practice.In sharp contrast,we propose a fully asynchronous push-pull gradient(APPG) algorithm,where each node updates without waiting for any other node by using possibly delayed information from neighbors.Then,we construct two novel augmented networks to analyze asynchrony and delays,and quantify its convergence rate from the worst-case point of view.Particularly,all nodes of APPG converge to the same optimal solution at a linear rate of O(λk) if local functions have Lipschitz-continuous gradients and their sum satisfies the Polyak-?ojasiewicz condition(convexity is not required),where λ ∈(0,1) is explicitly given and the virtual counter k increases by one when any node updates.Finally,the advantage of APPG over the synchronous counterpart and its linear speedup efficiency are numerically validated via a logistic regression problem. 展开更多
关键词 fully asynchronous distributed optimization linear convergence Polyak-Łojasiewicz condition
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Online distributed optimization with stochastic gradients:high probability bound of regrets 认领 引用
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作者 Yuchen Yang Kaihong Lu Long Wang 《Control Theory and Technology》 EI CSCD 2024年第3期419-430,共12页
In this paper,the problem of online distributed optimization subject to a convex set is studied via a network of agents.Each agent only has access to a noisy gradient of its own objective function,and can communicate ... In this paper,the problem of online distributed optimization subject to a convex set is studied via a network of agents.Each agent only has access to a noisy gradient of its own objective function,and can communicate with its neighbors via a network.To handle this problem,an online distributed stochastic mirror descent algorithm is proposed.Existing works on online distributed algorithms involving stochastic gradients only provide the expectation bounds of the regrets.Different from them,we study the high probability bound of the regrets,i.e.,the sublinear bound of the regret is characterized by the natural logarithm of the failure probability's inverse.Under mild assumptions on the graph connectivity,we prove that the dynamic regret grows sublinearly with a high probability if the deviation in the minimizer sequence is sublinear with the square root of the time horizon.Finally,a simulation is provided to demonstrate the effectiveness of our theoretical results. 展开更多
关键词 Distributed optimization Online optimization Stochastic gradient High probability
Multi-Agent Swarm Optimization Method With Contribution-Based Cooperation for Distributed Multi-Target Localization and Data Association 认领 引用
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作者 Taiyou Chen Xiaomin Hu +1 位作者 Qiuzhen Lin Weineng Chen 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第7期1673-1688,共16页
With the development of communication and computation capabilities on terminal hardware,it is promising to apply distributed optimization methods to wireless sensor networks to improve the autonomous collaboration abi... With the development of communication and computation capabilities on terminal hardware,it is promising to apply distributed optimization methods to wireless sensor networks to improve the autonomous collaboration ability of sensors.In this work,we study distributed multi-target localization problem with measurement-to-measurement association(DM2M),where each sensor only accesses its own measurement data without the association of measurements from other sensors.We first reformulate DM2M into a distributed bilevel optimization problem to reduce the search space of negotiated variables caused by the data association among sensors.Then,we propose a multiagent swarm optimization method with contribution-based cooperation(MASTER).In MASTER,each sensor maintains a particle swarm to represent candidate solutions of target positions.Sensors evolve their particle swarms through two phases of local optimization and neighbor cooperation to locate the target cooperatively.To address the bilevel local objective function,we combine the Kuhn-Munkres algorithm and the competitive swarm optimization for local optimization.To promote sensors to optimize the global objective,we design a contribution-based cooperation method to guide sensors to learn from their neighbors.Through localization experiments for different target numbers and localization dimensions,the proposed algorithm achieves smaller localization errors and more stable consensus than existing algorithms. 展开更多
关键词 Distributed optimization evolutionary computation measurement-to-measurement association particle swarm optimization(PSO) wireless sensor networks(WSNs)
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Distributed continuous-time aggregative optimization and its applications to power generation systems 认领 引用
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作者 XIAN Chengxin ZHAO Yu LIU Yongfang 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第1期1-8,共8页
This paper investigates the distributed continuoustime aggregative optimization problem for second-order multiagent systems,where the local cost function is not only related to its own decision variables,but also to t... This paper investigates the distributed continuoustime aggregative optimization problem for second-order multiagent systems,where the local cost function is not only related to its own decision variables,but also to the aggregation of the decision variables of all the agents.By using the gradient descent method,the distributed average tracking(DAT)technique and the time-base generator(TBG)technique,a distributed continuous-time aggregative optimization algorithm is proposed.Subsequently,the optimality of the system's equilibrium point is analyzed,and the convergence of the closed-loop system is proved using the Lyapunov stability theory.Finally,the effectiveness of the proposed algorithm is validated through case studies on multirobot systems and power generation systems. 展开更多
关键词 distributed continuous-time aggregative optimization distributed average tracking(DAT) time-base generator(TBG)
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Gradient-free distributed online optimization in networks 认领 引用 被引量：1
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作者 Yuhang Liu Wenxiao Zhao +2 位作者 Nan Zhang Dongdong Lv Shuai Zhang 《Control Theory and Technology》 EI CSCD 2025年第2期207-220,共14页
In this paper,we consider the distributed online optimization problem on a time-varying network,where each agent on the network has its own time-varying objective function and the goal is to minimize the overall loss ... In this paper,we consider the distributed online optimization problem on a time-varying network,where each agent on the network has its own time-varying objective function and the goal is to minimize the overall loss accumulated.Moreover,we focus on distributed algorithms which do not use gradient information and projection operators to improve the applicability and computational efficiency.By introducing the deterministic differences and the randomized differences to substitute the gradient information of the objective functions and removing the projection operator in the traditional algorithms,we design two kinds of gradient-free distributed online optimization algorithms without projection step,which can economize considerable computational resources as well as has less limitations on the applicability.We prove that both of two algorithms achieves consensus of the estimates and regrets of\(O\left(\log(T)ight)\)for local strongly convex objective,respectively.Finally,a simulation example is provided to verify the theoretical results. 展开更多
关键词 Distributed optimization Online convex optimization Gradient-free algorithm Projection-free algorithm
Privacy Distributed Constrained Optimization Over Time-Varying Unbalanced Networks and Its Application in Federated Learning 认领 引用
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作者 Mengli Wei Wenwu Yu +2 位作者 Duxin Chen Mingyu Kang Guang Cheng 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第2期335-346,共12页
This paper investigates a class of constrained distributed zeroth-order optimization(ZOO) problems over timevarying unbalanced graphs while ensuring privacy preservation among individual agents. Not taking into accoun... This paper investigates a class of constrained distributed zeroth-order optimization(ZOO) problems over timevarying unbalanced graphs while ensuring privacy preservation among individual agents. Not taking into account recent progress and addressing these concerns separately, there remains a lack of solutions offering theoretical guarantees for both privacy protection and constrained ZOO over time-varying unbalanced graphs.We hereby propose a novel algorithm, termed the differential privacy(DP) distributed push-sum based zeroth-order constrained optimization algorithm(DP-ZOCOA). Operating over time-varying unbalanced graphs, DP-ZOCOA obviates the need for supplemental suboptimization problem computations, thereby reducing overhead in comparison to distributed primary-dual methods. DP-ZOCOA is specifically tailored to tackle constrained ZOO problems over time-varying unbalanced graphs,offering a guarantee of convergence to the optimal solution while robustly preserving privacy. Moreover, we provide rigorous proofs of convergence and privacy for DP-ZOCOA, underscoring its efficacy in attaining optimal convergence without constraints. To enhance its applicability, we incorporate DP-ZOCOA into the federated learning framework and formulate a decentralized zeroth-order constrained federated learning algorithm(ZOCOA-FL) to address challenges stemming from the timevarying imbalance of communication topology. Finally, the performance and effectiveness of the proposed algorithms are thoroughly evaluated through simulations on distributed least squares(DLS) and decentralized federated learning(DFL) tasks. 展开更多
关键词 Constrained distributed optimization decentralized federated learning(DFL) differential privacy(DP) time-varying unbalanced graphs zeroth-order gradient
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Distributed asynchronous double accelerated optimization for ethylene plant considering delays 认领 引用
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作者 Ting Wang Zhongmei Li Wenli Du 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2025年第2期245-250,共6页
Considering the complexity of plant-wide optimization for large-scale industries, a distributed optimization framework to solve the profit optimization problem in ethylene whole process is proposed. To tackle the dela... Considering the complexity of plant-wide optimization for large-scale industries, a distributed optimization framework to solve the profit optimization problem in ethylene whole process is proposed. To tackle the delays arising from the residence time for materials passing through production units during the process with guaranteed constraint satisfaction, an asynchronous distributed parameter projection algorithm with gradient tracking method is introduced. Besides, the heavy ball momentum and Nesterov momentum are incorporated into the proposed algorithm in order to achieve double acceleration properties. The experimental results show that the proposed asynchronous algorithm can achieve a faster convergence compared with the synchronous algorithm. 展开更多
关键词 Asynchronous distributed optimization Plant-wide optimization Heavy ball Nesterov Inequality constraints
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Distributed control and decision-making algorithms for open multi-agent systems:a brief overview 认领 引用
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作者 Guanghui WEN Meng LUAN +1 位作者 Xiao FANG Xiaodong LI 《ENGINEERING Information Technology & Electronic Engineering》 SCIE EI CSCD 2026年第6期5-19,共15页
Open multi-agent systems(OMASs),characterized by the dynamic joining and leaving of agents,possess distinct attributes such as agent-level autonomy,time-varying network topologies,and environmental openness.These char... Open multi-agent systems(OMASs),characterized by the dynamic joining and leaving of agents,possess distinct attributes such as agent-level autonomy,time-varying network topologies,and environmental openness.These characteristics make them highly applicable to dynamic scenarios like robotic swarms,smart grids,and vehicular networks.However,such dynamism introduces core challenges in maintaining system stability,achieving efficient collaboration,and guaranteeing decision robustness.This paper presents a brief overview of recent advances in distributed control and decision-making algorithms for OMASs.First,the fundamental concepts and control strategies of OMASs are systematically reviewed.Second,distributed decision-making mechanisms encompassing distributed consensus optimization,separable resource allocation,and Nash equilibrium(NE)seeking in non-cooperative games are discussed,highlighting key technologies and typical methods.Finally,an outlook on future perspectives in the field is presented. 展开更多
关键词 Open multi-agent system Time-varying topology Distributed control Distributed optimization Nash equilibrium seeking
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A Stochastic Extremum Seeking Approach for Distributed Optimization with Binary-Valued Intermittent Measurements over Directed Graphs 认领 引用
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作者 ZHANG Yuan LIU Shujun 《Journal of Systems Science & Complexity》 SCIE EI CSCD 2025年第5期1887-1908,共22页
This paper focuses on solving the distributed optimization problem with binary-valued intermittent measurements of local objective functions.In this paper,a binary-valued measurement represents whether the measured va... This paper focuses on solving the distributed optimization problem with binary-valued intermittent measurements of local objective functions.In this paper,a binary-valued measurement represents whether the measured value is smaller than a fixed threshold.Meanwhile,the“intermittent”scenario arises when there is a non-zero probability of not detecting each local function value during the measuring process.Using this kind of coarse measurement,the authors propose a discrete-time stochastic extremum seeking-based algorithm for distributed optimization over a directed graph.As is well-known,many existing distributed optimization algorithms require a doubly-stochastic weight matrix to ensure the average consensus of agents.However,in practical engineering,achieving doublestochasticity,especially for directed graphs,is not always feasible or desirable.To overcome this limitation,the authors design a row-stochastic matrix and a column-stochastic matrix as weight matrices in the proposed algorithm instead of relying on doubly-stochasticity.Under some mild conditions,the authors rigorously prove that agents can reach the average consensus and ultimately find the optimal solution.Finally,the authors provide a numerical example to illustrate the effectiveness of the algorithm. 展开更多
关键词 Binary-valued measurement directed graph distributed optimization intermittent measurement stochastic extremum seeking
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Concurrent topology and fiber distribution optimization of continuous fiber-reinforced polymer(CFRP)structures under thermal-mechanical coupling 认领 引用
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作者 Yongjia Dong Hongling Ye +1 位作者 Jicheng Li Sujun Wang 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第5期326-346,共21页
Continuous fiber-reinforced polymers(CFRPs)have been extensively utilized in aerospace industries,making it imperative for CFRP structural optimization to consider the effects of extreme service environments.In this p... Continuous fiber-reinforced polymers(CFRPs)have been extensively utilized in aerospace industries,making it imperative for CFRP structural optimization to consider the effects of extreme service environments.In this paper,a thermalmechanical coupling concurrent topology and fiber distribution optimization(TM-CTFDO)method is proposed,specifically tailored for CFRP structures subjected to extreme environmental conditions.The mapping relationships between fiber design variables and material properties are deduced based on the rule of mixture to realize the analysis of thermoelastic CFRP structures.The integrated optimization model for CFRP structures is established with minimizing structural compliance,adhering to the volume constraints of structural and fiber under mechanical and temperature loads.Sensitivity analysis and optimization solution are realized by adopting the adjoint method and the method of moving asymptotes,respectively.This approach culminates in the determination of the optimal topology,fiber orientation,and content.In the post-processing phase,a fiber path planning algorithm is investigated to achieve the continuous fiber path based on the optimization results,which also effectively controls the distribution of dense and sparse fiber.Several examples under uniform and varying temperature fields are provided to verify the effectiveness of the TM-CTFDO method.The influence of temperature and mechanical loads on the optimization results is discussed,which will provide guidance on CFRP structural design and fiber path planning under thermal-mechanical coupling. 展开更多
关键词 Continuous fiber-reinforced polymers Topology optimization Fiber distribution optimization Thermalmechanical coupling Continuous fiber path planning
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Optimal distributed control of Cahn-Hilliard-Brinkman system in three dimension 认领 引用
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作者 XIAO Xiang-yu PU Zhi-lin 《Applied Mathematics(A Journal of Chinese Universities)》 SCIE CSCD 2026年第2期430-454,共25页
In this paper,we consider the distributed optimal control of the three-dimensional Cahn-Hilliard-Brinkman system with a more general potential.We obtain the regularity results for the weak solution which are essential... In this paper,we consider the distributed optimal control of the three-dimensional Cahn-Hilliard-Brinkman system with a more general potential.We obtain the regularity results for the weak solution which are essential for an associated optimal control problem.We then show that the control-to-state operator is Frechet differentiable and we derive the first-order necessary optimality conditions in terms of a variational inequality involving the adjoint state variables. 展开更多
关键词 distributed optimal control more general potential rst-order necessary optimality conditions the adjoint state variables
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An Approximate Gradient Algorithm for Constrained Distributed Convex Optimization 认领 引用 被引量：3
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作者 Yanqiong Zhang Youcheng Lou Yiguang Hong 《IEEE/CAA Journal of Automatica Sinica》 EI 2014年第1期61-67,共7页
In this paper,we propose an approximate gradient algorithm for the multi-agent convex optimization problem with constraints.The agents cooperatively compute the minimum of the sum of the local objective functions whic... In this paper,we propose an approximate gradient algorithm for the multi-agent convex optimization problem with constraints.The agents cooperatively compute the minimum of the sum of the local objective functions which are subject to a global inequality constraint and a global constraint set.Instead of each agent can get exact gradient,as discussed in the literature,we only use approximate gradient with some computation or measurement errors.The gradient accuracy conditions are presented to ensure the convergence of the approximate gradient algorithm.Finally,simulation results demonstrate good performance of the approximate algorithm. 展开更多
关键词 Constraints approximate gradient distributed optimization multi-agent systems
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