Adaptive Consensus of Uncertain Multi-Agent Systems With Unified Prescribed Performance

Dear Editor,An adaptive consensus control algorithm for uncertain multi-agent systems(MAS),capable of guaranteeing unified prescribed performance,is presented in this letter.Unlike many existing prescribed performance related works,the developed control exhibits some features.Firstly,a distributed prescribed time observer is introduced so that not only each follower is able to estimate the leader’s signal within a predetermined time,but also the control design for each agent is independent with its neighbors.

Hepatic perivascular epithelioid cell tumors:Benign,malignant,and uncertain malignant potential

In 2013,the World Health Organization defined perivascular epithelioid cell tumor(PEComa)as“a mesenchymal tumor which shows a local association with vessel walls and usually expresses melanocyte and smooth muscle markers.”This generic definition seems to better fit the PEComa family,which includes angiomyolipoma,clear cell sugar tumor of the lung,lymphangioleiomyomatosis,and a group of histologically and immunophenotypically similar tumors that include primary extrapulmonary sugar tumor and clear cell myomelanocytic tumor.Clear cell tumors with this immunophenotypic pattern have also had their malignant variants described.When localizing to the liver,preoperative radiological diagnosis has proven to be very difficult,and most patients have been diagnosed with hepatocellular carcinoma,focal nodular hyperplasia,hemangioma,or hepatic adenoma based on imaging findings.Examples of a malignant variant of the liver have been described.Finally,reports of malignant variants of these lesions have increased in recent years.Therefore,we support the use of the Folpe criteria,which in 2005 established the criteria for categorizing a PEComa as benign,malignant,or of uncertain malignant potential.Although they are not considered ideal,they currently seem to be the best approach and could be used for the categorization of liver tumors.

Reinforcement learning based adaptive control for uncertain mechanical systems with asymptotic tracking

This paper mainly focuses on the development of a learning-based controller for a class of uncertain mechanical systems modeled by the Euler-Lagrange formulation.The considered system can depict the behavior of a large class of engineering systems,such as vehicular systems,robot manipulators and satellites.All these systems are often characterized by highly nonlinear characteristics,heavy modeling uncertainties and unknown perturbations,therefore,accurate-model-based nonlinear control approaches become unavailable.Motivated by the challenge,a reinforcement learning(RL)adaptive control methodology based on the actor-critic framework is investigated to compensate the uncertain mechanical dynamics.The approximation inaccuracies caused by RL and the exogenous unknown disturbances are circumvented via a continuous robust integral of the sign of the error(RISE)control approach.Different from a classical RISE control law,a tanh(·)function is utilized instead of a sign(·)function to acquire a more smooth control signal.The developed controller requires very little prior knowledge of the dynamic model,is robust to unknown dynamics and exogenous disturbances,and can achieve asymptotic output tracking.Eventually,co-simulations through ADAMS and MATLAB/Simulink on a three degrees-of-freedom(3-DOF)manipulator and experiments on a real-time electromechanical servo system are performed to verify the performance of the proposed approach.

Guaranteed Cost Attitude Tracking Control for Uncertain Quadrotor Unmanned Aerial Vehicle Under Safety Constraints

In this paper,guaranteed cost attitude tracking con-trol for uncertain quadrotor unmanned aerial vehicle(QUAV)under safety constraints is studied.First,an augmented system is constructed by the tracking error system and reference system.This transformation aims to convert the tracking control prob-lem into a stabilization control problem.Then,control barrier function and disturbance attenuation function are designed to characterize the violations of safety constraints and tolerance of uncertain disturbances,and they are incorporated into the reward function as penalty items.Based on the modified reward function,the problem is simplified as the optimal regulation problem of the nominal augmented system,and a new Hamilton-Jacobi-Bellman equation is developed.Finally,critic-only rein-forcement learning algorithm with a concurrent learning tech-nique is employed to solve the Hamilton-Jacobi-Bellman equa-tion and obtain the optimal controller.The proposed algorithm can not only ensure the reward function within an upper bound in the presence of uncertain disturbances,but also enforce safety constraints.The performance of the algorithm is evaluated by the numerical simulation.

Practical prescribed-time fuzzy tracking control for uncertain nonlinear systems with time-varying actuators faults

The paper investigates the practical prescribed-time fuzzy tracking control problem for a category of nonlinear system subject to time-varying actuator faults.The presence of unknown nonlinear dynamics and actuator faults makes achieving tracking control within a prescribed-time challenging.To tackle this issue,we propose a novel practical prescribed-time fuzzy tracking control strategy,which is independent of the initial state of the system and does not rely on precise modeling of the system and actuators.We apply the approximation capabilities of fuzzy logic systems to handle the unknown nonlinear functions and unidentified actuator faults in the system.The piecewise controller and adaptive law constructed based on piecewise prescribed time-varying function and backstepping technique method establish the theoretical framework of practical prescribed-time tracking control,and extend the range of prescribed-time tracking control to infinity.Regardless of the initial conditions,the proposed control strategy can guarantee that all signals remain uniformly bounded within the practical prescribed time in the presence of unknown nonlinear item and time-varying actuator faults.Simulation example is presented to demonstrate the effectiveness of the proposed control strategy.

A Data-Based Feedback Relearning Algorithm for Uncertain Nonlinear Systems

In this paper,a data-based feedback relearning algorithm is proposed for the robust control problem of uncertain nonlinear systems.Motivated by the classical on-policy and off-policy algorithms of reinforcement learning,the online feedback relearning(FR)algorithm is developed where the collected data includes the influence of disturbance signals.The FR algorithm has better adaptability to environmental changes(such as the control channel disturbances)compared with the off-policy algorithm,and has higher computational efficiency and better convergence performance compared with the on-policy algorithm.Data processing based on experience replay technology is used for great data efficiency and convergence stability.Simulation experiments are presented to illustrate convergence stability,optimality and algorithmic performance of FR algorithm by comparison.

Quantum Fuzzy Regression Model for Uncertain Environment

In the era of big data,traditional regression models cannot deal with uncertain big data efficiently and accurately.In order to make up for this deficiency,this paper proposes a quantum fuzzy regression model,which uses fuzzy theory to describe the uncertainty in big data sets and uses quantum computing to exponentially improve the efficiency of data set preprocessing and parameter estimation.In this paper,data envelopment analysis(DEA)is used to calculate the degree of importance of each data point.Meanwhile,Harrow,Hassidim and Lloyd(HHL)algorithm and quantum swap circuits are used to improve the efficiency of high-dimensional data matrix calculation.The application of the quantum fuzzy regression model to smallscale financial data proves that its accuracy is greatly improved compared with the quantum regression model.Moreover,due to the introduction of quantum computing,the speed of dealing with high-dimensional data matrix has an exponential improvement compared with the fuzzy regression model.The quantum fuzzy regression model proposed in this paper combines the advantages of fuzzy theory and quantum computing which can efficiently calculate high-dimensional data matrix and complete parameter estimation using quantum computing while retaining the uncertainty in big data.Thus,it is a new model for efficient and accurate big data processing in uncertain environments.

A review of uncertain factors and analytic methods in long-term energy system optimization models

A larger number of uncertain factors in energy systems influence their evolution.Owing to the complexity of energy system modeling,incorporating uncertainty analysis to energy system modeling is essential for future energy system planning and resource allocation.This study focusses on long-term energy system optimization model.The important uncertain parameters in the model are analyzed and divided into policy,economic,and technical factors.This study specifically addresses the challenges related to carbon emission reduction and energy transition.It involves collecting and organizing relevant research on uncertainty analysis of long-term energy systems.Various energy system uncertainty modeling methods and their applications from the literature are summarized in this review.Finally,important uncertainty factors and uncertainty modeling methods for long-term energy system modeling are discussed,and future research directions are proposed.

Reachable set estimation for discrete-time Markovian jump neural networks with unified uncertain transition probability

This paper focuses on the reachable set estimation for Markovian jump neural networks with time delay.By allowing uncertainty in the transition probabilities,a framework unifies and enhances the generality and realism of these systems.To fully exploit the unified uncertain transition probabilities,an equivalent transformation technique is introduced as an alternative to traditional estimation methods,effectively utilizing the information of transition probabilities.Furthermore,a vector Wirtinger-based summation inequality is proposed,which captures more system information compared to existing ones.Building upon these components,a novel condition that guarantees a reachable set estimation is presented for Markovian jump neural networks with unified uncertain transition probabilities.A numerical example is illustrated to demonstrate the superiority of the approaches.

考虑锚节点位置不确定的水下目标定位算法研究

考虑时钟异步和声波分层效应的影响,该文研究了当测量过程受到未知噪声干扰,且锚节点位置不确定时水下目标节点的定位问题。首先构造了水下节点间飞行时间模型,设计了一种交互式异步通信协议,建立了最小化定位误差的优化目标函数。然后提出了一种基于深度强化学习的水下目标定位算法,并采用层归一化来改进深度神经网络,进一步提高模型的泛化能力。最后,仿真和实验结果验证所提方法的有效性。

新能源电力系统不确定优化调度方法研究现状及展望

风电场和光伏电站出力的不确定性给电力系统优化调度带来很大技术挑战。文中主要介绍了考虑新能源不确定性的电力系统优化调度方法的研究现状及后续研究方向展望。首先,重点论述了各种不确定优化调度(UOD)方法,包括随机优化方法、鲁棒优化方法、随机鲁棒优化结合方法和基于人工智能技术的方法。其中,随机优化方法包括场景法、机会约束规划法和近似动态规划法;鲁棒优化方法包括传统鲁棒优化法和分布鲁棒优化法;随机鲁棒优化结合方法包括采样鲁棒优化法和分布鲁棒机会约束规划法。然后,介绍了每一种方法的优化模型形式、模型的转化和求解原理及其优缺点。最后,对UOD的后续重点研究方向进行展望,包括兼顾多个目标的UOD问题及多目标不确定优化方法、输配系统UOD问题及分布式不确定优化方法、考虑稳定性约束的UOD问题及含常微分方程约束的不确定优化方法、考虑管道传输动态的综合能源系统UOD问题及含偏微分方程约束的不确定优化方法。

新时代韧性社区建设路径研究

面对不确定性风险的扰动与冲击,传统的理论框架难以回应社会现实样态中确定性与不确定性的耦合效应,韧性理念逐渐成为社区治理研究的理论共识。随着中国式现代化道路的不断推进,要求反思和探索基层社区治理在应对风险社会、供给公共服务、培育社会资本、提升治理效能等维度的价值取向和实践机制。研究发现,现阶段推进韧性社区建设,还面临着组织能力滞后、资源整合松散、情感维系游离等现实梗阻。因此,提升社区治理能力以及推进韧性社区的持续性发展,需要把握韧性社区的功用和治理逻辑,在结构要素、功能效应、社会价值等维度建构富有韧性的治理体系,以回应不确定性情境下社区治理现代化的现实要求。

基于随机抽样的衰变热不确定度量化研究

为研究核数据引起的核素存量及导出量的不确定度,在自主程序GNET上实现了基于随机抽样的不确定度量化方法。利用贝叶斯更新方法获得裂变产物独立产额的协方差数据,弥补裂变产额协方差数据缺失。对热中子引起的235U一次裂变后衰变热不确定度进行了计算。结果表明,裂变产物产额的不确定度贡献占主要部分。该算例表明GNET程序具备了核素存量的不确定度量化功能。

移动边缘计算不确定性任务持续卸载及资源分配方法

移动边缘计算场景中任务的不确定性增加了任务卸载及资源分配的复杂性和难度.鉴于此,提出一种移动边缘计算不确定性任务持续卸载及资源分配方法.首先,构建一种移动边缘计算不确定性任务持续卸载模型,通过基于持续时间片划分的任务多批次处理技术应对任务的不确定性,并设计多设备计算资源协同机制提升对计算密集型任务的承载能力.其次,提出一种基于负载均衡的自适应策略选择算法,避免计算资源过度分配导致信道拥堵进而产生额外能耗.最后,基于泊松分布实现了对不确定任务场景模型的仿真,大量实验结果表明时间片长度减小能够降低系统总能耗.此外,所提算法能够更有效地实现任务卸载及资源分配,相较于对比算法,最大可降低能耗11.8%.

不确定环境下高技术船舶产业产学研知识共享机制研究

在创新环境存在不可控因素情景下,高技术船舶产业产学研异质创新主体间稳定的知识共享机制,能够有效降低创新风险、提高产业整体创新能力。基于生态系统视角,分析高技术船舶产业内部企业和学研机构的知识共享过程,充分考虑环境的不确定性,将随机过程与演化博弈理论相结合,构建高技术船舶企业和学研机构的知识共享随机演化博弈模型,研究产学研双方在不确定环境下知识共享的稳定策略和影响因素,并对最优策略进行数值仿真分析。研究发现:在不确定环境下,随机扰动带来的不稳定性能提升博弈双方共享策略的演化速率,高技术船舶企业总是先于学研机构演化至共享稳定策略,罚金和知识共享成本对学研机构的影响更大,高技术船舶企业对收益分配系数更敏感。

不确定时期城市基层社区的情感治理

在不确定时期,城市基层呈现出不受掌控的特征,人们渴望依赖附近、回到附近,重建社会连接并构筑集体情感。在此情境下,聚焦个体情绪、群体心理以及社会心态的情感治理,就显得尤为重要。不确定时期城市基层社区的情感治理旨在找回日常生活的掌控感,增强基层社区的关系韧性,从而促进社会团结。这为促进城市基层社区的情感治理以应对新的不确定性情境提供了新思路。

含时滞的不确定性交直流微电网直流母线电压H_∞控制

针对含通信网络延时的不确定性交直流微电网的直流母线电压稳定问题,提出了一种将状态空间法和阻抗分析法相融合的方法,构建具有时滞的范数有界不确定交直流微电网模型。基于系统鲁棒二次稳定的求解,获得了满足H_∞控制性能的时滞无关型状态反馈控制器的条件。然后应用S-procedure定理,提出一种降低时滞依赖型Lyapunov方法的泛函构造难度且无须再次求导的方法,引用Wirtinger积分不等式和自由矩阵的积分不等式,得到时滞依赖型的H_∞控制器。仿真分析验证了所提控制方法能在一定的通信延时约束下抑制不确定参数对直流母线电压的影响。

一类不确定切换模糊系统的记忆保成本控制

针对一类具有状态时滞和不确定性的切换模糊系统,提出了该类系统的记忆保成本控制问题。利用引入系统过去的状态信息,构造有记忆状态反馈控制器,以消除系统时滞对于系统稳定性产生的不良影响。在保证成本函数指标小于某个上界及允许的不确定性条件下,结合多Lyapunov函数方法和线性矩阵不等式(linear matrix inequality,LMI)处理方法得到最终的充分条件,保证闭环系统能够渐近稳定;且得到满足设计的切换律的记忆状态反馈控制器增益。最后通过Simulink仿真验证了所提方法的正确性。

大时滞不确定系统的滞后时间削弱与自抗扰控制

针对具有变时滞、变参数和扰动的大时滞不确定系统的控制问题,本文提出了滞后时间削弱与自抗扰控制方法,综合应用了前馈控制、反馈控制和自抗扰补偿控制.为了提高系统的稳定性,在前馈控制器的设计中采用了系统的边界模型确定控制器参数取值范围;采用系统边界模型输出与系统实际输出的动态加权和作为反馈控制器的输入.为了提高系统控制的性能,自抗扰补偿控制回路的设计基于标称模型的补偿控制器.理论证明和仿真结果表明,所提出的方法是有效的,其在具有模型参数变化、滞后时间变化和外部扰动情况下,能保证系统的稳定性和良好的控制性能.

重大传染病疫情下基于服务水平的疫苗分配及储备研究

为缓解重大传染病疫情下疫苗的短缺现状,本文结合传染病模型(susceptible-infected-recovered-deceased,SIRD)考虑疫苗需求与疫区医院收治容量的关系,以期望短缺数最小为目标建立疫苗分配模型,推导分析了最佳服务水平和储备量,并给出了不同情形下疫苗的最优分配方案。此外,通过数值模拟,进一步探究了紧急调配成本、资金预算、需求变化、疫区数量以及疫区间相关性等外生变量带来的影响,验证了模型推导结果,为疫苗分配和储备策略提供了科学依据。研究发现:疫苗接种有助于促进病毒感染曲线平坦化和降低疫情峰值,从而减轻医疗系统超负荷运转的现象,降低因感染而死亡的人数;在不考虑储备疫苗的情况下,无论需求的不确定性程度如何,为每个地区提供同等的服务水平有利于最小化疫苗期望短缺量;考虑储备疫苗的情况下,向需求波动幅度较大的疫区提供更高的服务水平可以减少期望短缺,但疫区数量较多时,为每个疫区提供同等服务水平更具公平性,即使会导致疫苗的次优覆盖;是否考虑储备疫苗取决于紧急调配成本、预算的高低以及疫区需求情况等。