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Penalty Removal Search Algorithm for distributed Optimization of Nonconvex Functions in Economic Dispatch

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2025年

作者： Ge, Yiyang Wang, Zhanshan Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China State Key Lab Synthet Automation Proc Ind Shenyang 110819 Peoples R China

Nonconvexity is a usually overlooked factor in economic dispatch (ED). Enhancing the nonconvexity of the objective function leads traditional convex optimization algorithms easily to fall into the local optimum. To address the above problem, a penalty removal search algorithm (PRSA) is proposed for ED nonconvex optimization. It is composed of two distributed optimization algorithms embedded in a reinforcement learning framework. In Phase I of PRSA, a distributed optimization algorithm with projection operators is designed. It uses fewer variables to locate the region where the optimal solution belongs by the cooperative Q-learning. In Phase II of PRSA, the sigmoid function serves as a penalty function to form the second distributed optimization algorithm. This is used to skip the searched solutions and allow the algorithm to continue searching for more feasible solutions. The PRSA solves the problem that the algorithm misses feasible solutions when the nonconvex coefficients increase. Finally, the effectiveness of the PRSA is verified by numerical examples.

关键词： Linear programming Economics distributed algorithms Cost function Q-learning Germanium Costs Valves Fuels Convex functions Collaborative Q-learning (CQL) algorithm distributed optimization economic dispatch (ED) nonconvex optimization penalty removal search algorithm (PRSA)

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distributed Low-Effort Load Balancing in the Presence of Time-Delays

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IFAC-PapersOnLine 2023年第2期56卷 5191-5196页

作者： Themistoklis Charalambous Stefano Panzieri Gabriele Oliva Department of Electrical and Computer Engineering School of Engineering University of Cyprus Nicosia Cyprus University Roma Tre via della Vasca Navale 79 00146 Rome Italy Unit of Automatic Control Department of Engineering Università Campus Bio-Medico di Roma Rome Italy

In this paper, we investigate the problem of distributed load balancing under network capacity constraints, where the participating agents cooperate with the aim of jointly minimizing both the workload disparity among them as well as the overall workload transfer. Classical approaches for asymptotic convergence to the global optimum in a distributed fashion typically assume timely exchange of information between neighboring agents of a given multi-agent system. This assumption is not necessarily valid in practical settings due to non-commensurate (heterogeneous) communication and processing delays that might affect transmissions at different times. More specifically, we consider what effect multiple heterogeneous time-varying delays, among the agents can have on the distributed load balancing problem. We show that the distributed load balancing problem under bounded heterogeneous time-varying delays is globally asymptotically stable, but the rate of convergence is affected. Bounds on the convergence rate are provided with respect to an upper bound on the delays. Simulation examples are provided to show the validity and performance of our theoretical results.

关键词： distributed load balancing time-delays distributed algorithms minimum effort

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distributed Generalized Nash Equilibrium Seeking for Constrained N-Cluster Games with Second-Order Dynamics

Distributed Generalized Nash Equilibrium Seeking for Constra...

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IEEE Conference on Decision and Control

作者： Yan Zhao Min Meng Xiuxian Li Jia Xu Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China Department of Control Science and Engineering College of Electronics and Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems and Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper studies N-cluster games with secondorder dynamics, wherein the players’ decisions are restricted by local set constraints and nonlinear coupled inequality constraints. The presence of second-order dynamics coupled with constraints leads to difficulties in the design and analysis of generalized Nash equilibrium (GNE) seeking algorithms, since it may be impossible to directly determine the decisions of players based on their control inputs. To facilitate the autonomous execution of N-cluster game tasks through secondorder players, by employing state feedback, projection, primaldual, dynamic average consensus, and passivity methods, a distributed algorithm is proposed to find the variational GNE of the studied games, under which the players’ decisions can satisfy the set constraints all the time. Additionally, the algorithm’s convergence is rigorously analyzed, and its efficacy is validated by a simulation example.

关键词： State feedback Heuristic algorithms Games Nash equilibrium Time factors distributed algorithms Convergence

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distributed Algorithm for Edge Agreement Over Nonlinear Constraints

Distributed Algorithm for Edge Agreement Over Nonlinear Cons...

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American Control Conference (ACC)

作者： Ayush Rai Shaoshuai Mou School of Aeronautics and Astronautics Purdue University West Lafayette IN USA

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

In this paper, we introduce a comprehensive framework called edge agreement, where agents collaboratively strive to reach an agreement on non-linear edge constraints de-fined for each of their neighbors on every edge. This provides a novel perspective on the traditional ‘global consensus' problem, extending the goal beyond reaching the same value to achieving agreement over the whole multi-agent team. We present a dis-tributed algorithm for achieving edge agreement, along with a convergence guarantee based on certain assumptions. Addition-ally, we illustrate how this framework encompasses a variety of cooperation problems, including global consensus, recent results on edge agreement with linear constraints, distributed solutions to linear algebraic equations, and formation control (distance and displacement-based) as special cases. Finally, to validate and demonstrate the significance of this framework, we provide numerical simulations for all the mentioned applications.

关键词： Collaboration Numerical simulation Mathematical models Formation control distributed algorithms Convergence

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distributed Consensus algorithms for Collaborative Temperature Control in Smart Buildings

Distributed Consensus Algorithms for Collaborative Temperatu...

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American Control Conference

作者： Santosh K. Gupta Koushik Kar Sandipan Mishra John T. Wen Department of Electrical Computer & Systems Engineering Department of Mechanical Aerospace and Nuclear Engineering Rensselaer Polytechnic Institute Troy NY 12180 USA

ISBN: (纸本)9781479917730

Buildings with shared spaces such as corporate office buildings, university dorms, etc. involve multiple occupants who are likely to have different temperature preferences. The building temperature control system needs to balance the desire of all users, as well as take the building energy cost into account. Centralized temperature management is challenging as the use comfort function is held privately and not centrally known. This paper proposes a distributed temperature control algorithm which ensures that a consensus is attained among all occupants, irrespective of their temperature preferences. Occupants are only assumed to be rational, in that they choose their own temperature set-points to minimize a combination of their individual energy cost and discomfort. We establish the convergence of the proposed algorithm to the optimal temperature set-point that minimizes the sum of the energy cost and the aggregate discomfort of all occupants. Simulations with realistic parameter settings illustrate the performance of the algorithm and provide insights on the dynamics of the system with a mobile user population.

关键词： Energy costs Temperature control Temperature preferences parameter setting Buildings ON-SET TEMPERATURE Set of points distributed algorithms algorithms

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A distributed Algorithm for Large-Scale Multi-Agent MINLPs

A Distributed Algorithm for Large-Scale Multi-Agent MINLPs

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IEEE Conference on Decision and Control

作者： Sheng Dong Weiguo Xia Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education and School of Control Science and Engineering Dalian University of Technology China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this paper, we focus on the optimization of large-scale multi-agent systems, where agents collaboratively optimize the sum of local objective functions through their own continuous and/or discrete decision variables, subject to global coupling constraints and local constraints. The resulting Mixed-Integer Nonlinear Programmings (MINLPs) are NP-hard, non-convex, and large-scale. Therefore, this paper aims to design distributed algorithms to find feasible suboptimal solutions with a guaranteed bound. To this end, considering dual decomposition as an effective method to decompose large-scale constraint-coupled optimization problems, we first show, based on the convexification effects of large-scale MINLPs, that the primal solutions from the dual are near-optimal under certain conditions. This expands recent results in Mixed-Integer Linear Programmings (MILPs) to the nonlinear case but requires additional efforts on the proof. Utilizing this result to tighten the coupling constraints, we develop a fully distributed algorithm for the tightened problem, based on dual decomposition and consensus protocols. The algorithm is guaranteed to provide feasible solutions for the original MINLP. Moreover, asymptotic suboptimality bounds are established for the obtained solution. Finally, the efficacy of the method is verified through numerical simulations.

关键词： Couplings Programming Numerical simulation Linear programming Mixed integer linear programming Consensus protocol distributed algorithms Optimization Standards Multi-agent systems

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Resilient Projection-based distributed Leader-Follower Consensus Against Integrity Cyberattacks

Resilient Projection-based Distributed Leader-Follower Conse...

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IEEE Conference on Decision and Control

作者： Mahdieh S. Sadabadi Department of Electrical and Electronic Engineering University of Manchester Manchester United Kingdom

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper focuses on the problem of distributed leader-follower consensus in multi-agent systems in which some agents are subject to adversarial attacks. We develop a resilient distributed leader-follower control strategy subject to integrity attacks, where agents’ updates of their states can be compromised by injecting false signals to control inputs. Under such a threat model, we design a resilient distributed leader-follower framework for agents with continuous-time dynamics to resiliently track a reference state propagated by a leader. In the design of the resilient framework, projection-based operators are used as dynamic controllers to estimate the dynamics of uncertainties on the control inputs of each agent. By use of the properties of projection operators and Lyapunov stability theory, the uniform ultimate boundedness of the closed-loop multi-agent system in the presence of integrity attacks is guaranteed. The proposed resilient distributed scheme does not impose any limitations on the maximum tolerable number of cyberattacks and does not require high network connectivity. The effectiveness of the proposed resilient distributed consensus algorithm is verified by a numerical example.

关键词： Threat modeling Uncertainty Numerical simulation Sensor systems Trajectory Sensors Computer crime distributed algorithms Time-varying systems Multi-agent systems

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A Novel distributed Prescribed-Time Convergence Acceleration Algorithm with Time Rescaling

A Novel Distributed Prescribed-Time Convergence Acceleration...

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Neuromorphic Computing (ICNC), International Conference on

作者： Pengrui Zhang Xuehui Mei Haijun Jiang Zhiyong Yu College of Mathematics and System Science Xinjiang University Xinjiang China

ISBN: (数字)9798331508661

ISBN: (纸本)9798331508678

In this article, we present a novel distributed algorithm for prescribed-time convergence acceleration with time rescaling. Two novel prescribed-time acceleration algorithms are designed to tackle distributed unconstrained optimization problems and distributed optimization problems with constraints, effectively mirroring centralized optimization tasks within the framework of a closed convex set. We introduce several pivotal theorems, and the convergence of these algorithms are rigorously demonstrated through the application of Lyapunov functions. Finally, some comprehensive numerical simulations are conducted to validate the practicality and robustness of our theoretical findings.

关键词： Neuromorphic engineering Numerical simulation Linear programming Robustness distributed algorithms Optimization Convergence Lyapunov methods

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distributed Energy Management Strategy for Integrated Energy System Towards Lower Carbon Emission

Distributed Energy Management Strategy for Integrated Energy...

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Cyber-Energy Systems and Intelligent Energy (ICCSIE), International Conference on

作者： Qingyuan Dong Yanhong Xu Feng Long Shitong Wang Liwen Gu Linjiang Deng State Grid Yingkou Power Supply Company Yingkou China

ISBN: (数字)9798350373318

ISBN: (纸本)9798350373325

To decrease the degree of dependence on fuel-based resources, it is necessary to reform the energy system. Integrated energy system (IES) combined heating and power networks, realizes the cooperation of heterogeneous energies. To improve the efficiency of energy utilization, an energy management problem is investigated in this paper. Firstly, an energy management model of IES is constructed with the considerations of economy benefits and carbon emissions. Moreover, the carbon capture device and carbon storage device are installed in IES as well, which introduces a tax trading mechanism. Then, based on the method of alternating multipliers, a fully distributed energy management algorithm is designed to acquire the optimal solutions. Finally, simulation results prove the effectiveness of the constructed model and the distributed algorithm,

关键词： Couplings Simulation Cogeneration Finance Carbon capture and storage Energy management distributed algorithms Optimization Carbon tax Load modeling

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distributed Coordinated Optimal Control for Synchronous Operation of Virtually Coupled Train Set

Distributed Coordinated Optimal Control for Synchronous Oper...

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International Conference on Intelligent Transportation

作者： Xiaolin Luo Dongming Wang Wei Wang Yindong Ji Hongjie Liu State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering University of California Riverside CA USA Traffic Control Technology Co. Ltd Beijing China Department of Automation Tsinghua University Beijing China School of Automation and Intelligence Beijing Jiaotong University Beijing China

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Virtual coupling technologies have been widely studied in recent years, due to their merits of flexibility and efficiency. However, to operate a virtually coupled train set (VCTS) as a single train, the synchronous operation is still a critical problem to be addressed, especially in arriving. To this end, we propose a distributed coordinated optimal control (DCOC) approach integrated with distributed optimization and model predictive control. First, we formulate a globally coordinated optimal control problem for VCTS, where the states of units in VCTS are coupled in the cost and constraints. Then, we design a distributed algorithm to solve the global problem, where local problems are decoupled into ones with fewer dimensions. Next, we employ distributed model predictive control to guarantee the satisfaction of safety constraints before the convergence of the distributed algorithm. Finally, experimental results demonstrate that our approach can achieve stable and synchronous operation for VCTS and save by 90% computation time than existing centralized approaches.

关键词： Optimal control Robustness Safety Topology Synchronization distributed algorithms Optimization Intelligent transportation systems Predictive control Convergence

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