混合阶多智能体无向网络的拟平均一致性

为解决混合阶动态多智能体网络的拟平均一致性,将混合阶动态多智能体网络的拟平均一致性转化为增广的一阶多智能体网络的平均一致性,并给出相应一致性问题协议的定义,根据网络的拓扑结构连通性特点,运用代数图论、矩阵理论中的圆盘定理和李雅普诺夫理论证明了定拓扑和变拓扑无向网络下一致性协议的稳定性定理,并以由4个一阶智能体和2个二阶智能体所组成的混合阶智能体网络为例进行具体说明.理论研究和仿真结果都表明所提协议的正确性.     

离散时间多智能体系统一致性的平均驻留时间条件

研究高阶离散时间线性多智能体系统在有向切换信息拓扑下的状态一致性问题。首先通过提出的线性变换将该一致性问题转换为相应离散时间线性切换系统的渐近稳定性问题。然后借助于切换系统稳定性的平均驻留时间方法,分别得到如下两种情形下该一致性问题可解的充分条件：1)信息拓扑集合中的一部分拓扑是可一致的;2)信息拓扑集合中所有信息拓扑是可一致的。最后通过数值仿真验证了所得理论结果的正确性。     

离散异质多智能体系统的分组一致性控制

针对一类混合异质多智能体系统的分组一致性控制问题进行了研究。具体分析了由一阶智能体和二阶智能体组成的混合异质系统,研究其在离散情况下的分组一致性。基于两个合理的假设提出了线性控制协议,运用代数图论、稳定性理论和矩阵理论,分析协议作用下闭环系统的系统矩阵及动态特性,取得了系统渐近实现分组一致性的充分条件,该条件与系统拓扑结构、采样周期以及控制参数有关。结论同时适用于有向拓扑与无向拓扑,最后通过仿真实例对所得分析结果进行了验证。     

离散时间系统的多智能体的一致性

动态多智能体系统的一致性是复杂动力学系统中很有现实意义的问题.假设智能体连接网络拓扑是无向、固定和连通的,而且个体之间信息传递存在通信时廷,分析了一个动态移动多智能体离散时间系统.应用广义Nyquist判据研究具有通信时延的多智能体离散时间系统,得到了保证系统达到一致的充分条件.最后应用计算机仿真验证了该结论的有效性.     

基于Q-learning的离散时间多智能体系统一致性

针对模型未知的一类离散时间多智能体系统,本文提出了一种Q-learning方法实现多智能体系统的一致性控制.该方法不依赖于系统模型,能够利用系统数据迭代求解出可使给定目标函数最小的控制律,使所有智能体的状态实现一致.通过各个智能体所产生的系统数据,采用策略迭代的方法实时更新求解得到多智能体系统的控制律,并对所提Q-le...     

离散时间多智能体系统的一致性

研究了离散时间高维线性系统的一致性问题. 所考虑的系统可视为包含多个个体的多智能体系统, 每个个体的动力学行为与其他个体不同并受其他个体状态的影响. 本文建立了系统具有一致性的若干充分必要条件. 如果一致性函数存在, 文章给出了该函数的显式表达. 文末用一个数值例子说明了所得的理论结果.     

多智能体系统的非震颤固定时间一致性

研究了多智能体系统的非震颤固定时间一致性问题。基于李雅普诺夫稳定性理论,导出了实现固定时间一致性的充分条件,其中到达一致时间的上界估计不再依赖于多智能体系统的初始状态条件。此外,与传统含有符号函数的有限时间和固定时间控制器不同的是,所提出的新颖控制方案是非震颤的,有利于多智能体系统的一致性性能表征。最后,用一个数值例子验证了理论分析结果的有效性和可行性。     

基于部分稳定性方法的离散时间多智能体系统的一致性

研究有向信息拓扑下离散时间线性多智能体系统的一致性分析与设计问题.利用提出的线性变换,将一致性问题转换为相应线性系统的部分变元渐近稳定性问题.基于部分变元稳定性理论,得到有向信息拓扑下离散时间线性多智能体系统达到渐近一致的基于矩阵Schur稳定性的充要条件和状态一致函数的解析表达式.同时设计了反馈增益矩阵.最后数值实例验证了所得理论的有效性.     

离散多智能体系统信息一致性的平衡点

研究了离散多智能体系统信息一致性的平衡点问题.对于固定通信结构系统,基于非负随机矩阵谱半径及其对应的左特征向量,证明了在系统的通信拓扑所含的生成树中,仅根节点对平衡点有作用.对于时变通信结构系统,根据同阶非负矩阵样式的有限性,证明了在动态通信拓扑的联合中,仅那些到任意节点都存在有向路径的顶点对平衡点有作用.数值算例验证了理论结果的正确性.     

离散时间分数阶多自主体系统的时延一致性

复杂工作环境中,许多自然现象的个体动力学特性用整数阶方程不能描述,只能用非整数阶（分数阶）动力学来描述个体的运动行为. 本文假设多自主体系统内部连接组成有向加权网络,个体的动态特性应用分数阶动力学方程描述,个体之间数据传输存在通信时延. 应用分数阶系统的Laplace变换和频域理论,研究了离散时间的分数阶多自主体系统的渐近一致性. 应用Hermit-Biehler 定理,研究了具有样本时延的分数阶多自主体系统的运动一致性,得到保证系统稳定的时延的上界阈值. 最后应用一个实例对结论进行了验证.     

一类离散时间切换混杂系统鲁棒控制

由于切换规则的存在使得切换混杂控制系统的稳定性研究变得极为复杂,如何针对给定的系统设计适当的控制器和切换规则没有统一的方法.本文考虑一类线性不确定离散时间切换混杂系统的鲁棒二次镇定和渐近镇定问题.利用公共李雅普诺夫函数方法和多李雅普诺夫函数方法,分别设计了切换混杂系统鲁棒状态反馈控制器和鲁棒输出反馈控制器,保证了切换混杂系统的二次稳定性和渐近稳定性.仿真结果验证了所提算法的正确有效性.     

Exact hybrid filters in discrete time

Using the reference probability method and the change of measure in discrete time, the state estimator problem is considered for linear systems observed in Gaussian noise when the coefficients are functions of a noisily observed, finite-state Markov chain. The methods are new, and finite-dimensional filters are obtained. However, the number of statistics increases in time. A numerical comparison of this filter with the interactive multiple model algorithm introduced by Blom and Bar-Shalom (1988) is given     

A discrete whale swarm algorithm for hybrid flow-shop scheduling problem with limited buffers

This paper studies a hybrid flow-shop scheduling problem with limited buffers and two process routes that comes from an engine hot-test production line in a diesel engine assembly plant. It extends the classical hybrid flow-shop scheduling problem by considering practical constraints on buffer area resources and alternative process routes. Because of its NP-hardness and large scale, traditional optimization methods and heuristic rules cannot obtain satisfactory solutions. A discrete whale swarm algorithm (DWSA) is proposed to identify near-optimal solutions efficiently. The proposed algorithm adopts an encoding method based on the problem characteristic and a greedy delayed decoding strategy to avoid infeasible solutions. A hybrid initialization is used to ensure the quality of the initial population and diversity. A new way of computing distances and a movement rule between individuals are designed. Five mutation operators and a deduplication strategy are proposed to improve the population diversity. The effectiveness of the proposed DWSA is validated on three groups of instances and a real-world industrial case.     

A stochastic games framework for verification and control of discrete time stochastic hybrid systems

We describe a framework for analyzing probabilistic reachability and safety problems for discrete time stochastic hybrid systems within a dynamic games setting. In particular, we consider finite horizon zero-sum stochastic games in which a control has the objective of reaching a target set while avoiding an unsafe set in the hybrid state space, and a rational adversary has the opposing objective. We derive an algorithm for computing the maximal probability of achieving the control objective, subject to the worst-case adversary behavior. From this algorithm, sufficient conditions of optimality are also derived for the synthesis of optimal control policies and worst-case disturbance strategies. These results are then specialized to the safety problem, in which the control objective is to remain within a safe set. We illustrate our modeling framework and computational approach using both a tutorial example with jump Markov dynamics and a practical application in the domain of air traffic management.     

Stability and switching control design issues for a class of discrete time hybrid systems

For the class of systems considered, necessary and sufficient stabilizability conditions are unknown. However, by considering the same systems with unknown but bounded exogenous disturbances, we give finitely computable conditions, sufficient for stabilizability without disturbances, yet necessary for stabilizability with disturbances.     

A hybrid discrete particle swarm optimization for vehicle routing problem with simultaneous pickup and delivery

Vehicle routing problem (VRP) is an important and well-known combinatorial optimization problem encountered in many transport logistics and distribution systems. The VRP has several variants depending on tasks performed and on some restrictions, such as time windows, multiple vehicles, backhauls, simultaneous delivery and pick-up, etc. In this paper, we consider vehicle routing problem with simultaneous pickup and delivery (VRPSPD). The VRPSPD deals with optimally integrating goods distribution and collection when there are no precedence restrictions on the order in which the operations must be performed. Since the VRPSPD is an NP-hard problem, we present a heuristic solution approach based on particle swarm optimization (PSO) in which a local search is performed by variable neighborhood descent algorithm (VND). Moreover, it implements an annealing-like strategy to preserve the swarm diversity. The effectiveness of the proposed PSO is investigated by an experiment conducted on benchmark problem instances available in the literature. The computational results indicate that the proposed algorithm competes with the heuristic approaches in the literature and improves several best known solutions.     

A hybrid discrete particle swarm optimization for dual-resource constrained job shop scheduling with resource flexibility

In this paper, a novel hybrid discrete particle swarm optimization algorithm is proposed to solve the dual-resource constrained job shop scheduling problem with resource flexibility. Particles are represented based on a three-dimension chromosome coding scheme of operation sequence and resources allocation. Firstly, a mixed population initialization method is used for the particles. Then a discrete particle swarm optimization is designed as the global search process by taking the dual-resources feature into account. Moreover, an improved simulated annealing with variable neighborhoods structure is introduced to improve the local searching ability for the proposed algorithm. Finally, experimental results are given to show the effectiveness of the proposed algorithm.     

Computational aspects of discrete time adaptive control

Stable model reference adaptive control schemes for discrete time systems can be designed which take into account computational time and limited range of computing machines. An algorithm is developed using Lyapunov second method and properties of strictly positive real functions.     

Reachability for partially observable discrete time stochastic hybrid systems

When designing optimal controllers for any system, it is often the case that the true state of the system is unknown to the controller. Imperfect state information must be taken into account in the controller’s design in order to preserve its optimality. The same is true when performing reachability calculations. To estimate the probability that the state of a stochastic system reaches, or stays within, some set of interest in a given time horizon, it is necessary to find a controller that drives the system to that set with maximum probability, given the controller’s knowledge of the true state of the system. To date, little work has been done on stochastic reachability calculations with partially observable states. The work that has been done relies on converting the reachability optimization problem to one with an additive cost function, for which theoretical results are well known. Our approach is to preserve the multiplicative cost structure when deriving a sufficient statistic that reduces the problem to one of perfect state information. Our transformation includes a change of measure that simplifies the distribution of the sufficient statistic conditioned on its previous value. We develop a dynamic programming recursion for the solution of the equivalent perfect information problem, proving that the recursion is valid, an optimal solution exists, and results in the same solution as to the original problem. We also show that our results are equivalent to those for the reformulated additive cost problem, and so such a reformulation is not required.     

混沌时间序列的混合粒子群优化预测

提出一种混合粒子群优化算法,即在改进粒子群优化算法全局搜索模型参数的基础上,利用梯度下降法进一步确定径向基神经网络模型参数,以提高网络的收敛精度和网络性能.采用基于RBFNN的混合粒子群优化算法进行离散Henon和连续Mackey-Glass混沌时间序列预测仿真,结果表明该算法能快速精确地预测混沌时间序列,是研究复杂非线性动力系统辨识和控制的一种有效方法.