具有群集行为的时变函数分布式优化

本文对具有群集行为的连续时间多智能体系统的优化问题进行了研究. 考虑具有二阶动力学的多智能体系统, 每个智能体都具有一个局部的时变代价函数. 本文的目标是仅仅依靠局部信息交流使得多智能体在运动的过程中保持 连通性、避免碰撞、总体代价函数最小. 为此本文设计了一种具有群集行为的控制协议, 该协议仅仅依赖于自己和邻居 的速度. 可以证明在该控制协议作用下, 所有智能体在保持连通、避免碰撞的同时, 速度能够跟踪上最优速度. 最后, 通 过一个仿真来说明本文的结果.     

具有群集行为的时变函数分布式优化（英文）

本文对具有群集行为的连续时间多智能体系统的优化问题进行了研究.考虑具有二阶动力学的多智能体系统,每个智能体都具有一个局部的时变代价函数.本文的目标是仅仅依靠局部信息交流使得多智能体在运动的过程中保持连通性、避免碰撞、总体代价函数最小.为此本文设计了一种具有群集行为的控制协议,该协议仅仅依赖于自己和邻居的速度.可以证明在该控制协议作用下,所有智能体在保持连通、避免碰撞的同时,速度能够跟踪上最优速度.最后,通过一个仿真来说明本文的结果.     

二阶多智能体系统包含控制的时滞范围优化

研究具有未知时滞的二阶多智能体系统的鲁棒包含控制问题,考虑智能体之间同时具有通信延时和自延时,分别在无向拓扑和有向拓扑通信下,获得多智能体系统保持鲁棒包含控制所能允许的最大时滞范围.借助函数的凸分析和非线性规划方法解析地获得了无向图下包含控制所允许的最大时滞范围.对于有向图,通过遗传算法求解非光滑的优化问题获得最大时滞范围的数值解,同时结果可退化为领导-跟随控制所能获得的最大时滞范围.最后,通过仿真例子验证所提出理论和算法的有效性.     

一类控制系数有界的时变非线性系统的自适应鲁棒控制

针对一类带有有界控制系数和有界扰动的时变参数严反馈非线性系统, 将Nussbaum函数增益及光滑投影算法与自适应逆推设计工具相结合, 提出一种自适应鲁棒非线性控制方案. 在此方案中无需知道控制系数的符号, 以及时变参数和扰动的界. 借助Lyapunov函数及相关引理证明了所设计的自适应鲁棒非线性控制器能保证闭环系统中的所有信号全局一致有界. 可以通过恰当地选取设计参数, 保证系统具有任意指定的控制性能. 仿真研究证明了所提出算法的可行性和有效性.     

求解最优潮流KKT系统的一类新模型及算法设计

电力工业的市场化改革对最优潮流(optim al pow er flow,OPF)的计算精度和速度提出了更高的要求.本文针对OPF模型中存在大量的无功界约束的特性,把一般非线性不等式约束和界约束分开处理,通过引入一个对角矩阵和非线性互补函数,建立了与OPF问题的K arush-Kuhn-Tucker(KKT)系统等价的约束非光滑方程新模型.进一步,基于新建立的模型,提出了一类具有理论上收敛性保证的投影半光滑N ew ton型算法.相对于传统的解OPF的KKT系统和非线性互补函数方法,新方法一方面保持了非线性互补函数法无需识别有效集的优点,同时又减少了问题的维数,且投影计算保持了无功界约束的可行性.IEEE多个算例的数值试验显示本文所提出的模型和算法具有较好的计算效果.     

不确定离散广义时滞系统的鲁棒非脆弱H∞控制

针对一类参数不确定离散时滞广义系统,研究其鲁棒非脆弱H∞状态反馈控制器的设计问题.系统中的不确定项参数与控制器的增益变化同时具有线性分式形式的范数有界.首先,利用Lyapunov函数理论,研究该标称系统的鲁棒H∞控制问题;其次,以线性矩阵不等式(LMI)形式给出该系统的鲁棒非脆弱H∞控制器存在的充分条件及设计方法.该控...     

切换非线性系统的分布式模型预测控制

针对一类具有预先指定切换序列的切换非线性系统,研究了具有通信信道干扰和时滞测量的分布式模型预测控制问题.在每个子系统都存在镇定控制器的假设下,利用基于Lyapunov函数的模型预测控制器设计了分布式模型预测控制器,并给出了闭环切换非线性系统最终有界的充分条件.最后,通过仿真结果表明了分布式模型预测控制策略的有效性.     

基于屏障控制函数的轮式机器人系统多目标分布式协同控制

受多目标优化理论的启发,针对非完整约束轮式机器人设计基于屏障控制函数的多目标协同控制算法.该方法可实现队形控制主目标、连通性次级目标以及避碰次级目标,其中将连通性保持和避碰问题建模为两个系统约束,屏障控制函数作为约束对应的惩罚函数,可解决系统有输入或状态约束的问题.通过获取的局部信息将系统状态约束转化为屏障控制函数,利用屏障控制函数的类李雅普诺夫特性对其导数引入约束,再通过保证约束集的正不变性,达到控制目标.所提出方法可有效地避免控制器在连通性约束和避碰约束边界处的频繁切换,减小机械疲劳,在理论上可进一步扩展次级目标的数目,实现多目标控制.另外,所提出的协同控制算法对编队队形没有特殊要求,适用于不同编队需求和通信拓扑情况.最后通过数值仿真验证了所提出算法在不同情况下的有效性.     

基于事件触发的离散 MIMO 系统自适应评判容错控制

本文针对具有执行器故障的一类离散非线性多输入多输出(Multi-input multi-output, MIMO)系统, 提出了一种基于事件触发的自适应评判容错控制方案. 该控制方案包括评价和执行网络. 在评价网络里, 为了缓解现有的非光滑二值效用函数可能引起的执行网络跳变问题, 利用高斯函数构建了一个光滑的效用函数, 并采用评价网络近似最优性能指标函数. 在执行网络里, 通过变量替换将系统状态的将来信息转化成关于系统当前状态的函数, 并结合事件触发机制设计了最优跟踪控制器. 该控制器引入了动态补偿项, 不仅能够抑制执行器故障对系统性能的影响, 而且能够改善系统的控制性能. 稳定性分析表明所有信号最终一致有界且跟踪误差收敛于原点的有界小邻域内. 数值系统和实际系统的仿真结果验证了该方案的有效性.     

一类多变量非线性系统的自适应模糊控制

针对一类具有干扰和不确定性的多变量非线性系统, 提出了一种自适应模糊控制方法. 该多变量系统由 m 个互连子系统组成, 每个互连子系统中的未知函数是非仿射的. 由于不确定非仿射函数的存在和互连子系统之间的耦合, 这类系统是很难控制的. 通过利用均值定理、模糊系统、Backstepping 设计方法以及引入 Nussbaum 类型函数, 克服了这个困难. 另外, 与大多数研究结果相比较, 提出的方法减少了在线调节参数的数量. 提出的控制方法能实现闭环系统的所有信号是有界的. 仿真实验表明该控制方法的有效性.     

A connectivity-preserving flocking algorithm for multi-agent systems based only on position measurements

Most existing flocking algorithms rely on information about both relative position and relative velocity among neighbouring agents. In this article, we investigate the flocking problem with only position measurements. We propose a provably-stable flocking algorithm, in which an output vector is produced by distributed filters based on position information alone but not velocity information. Under the assumption that the initial interactive network is connected, the flocking algorithm not only can steer a group of agents to a stable flocking motion, but also can preserve the connectivity of the interactive network during the dynamical evolution. Moreover, we investigate the flocking algorithm with a virtual leader and show that all agents can asymptotically attain a desired velocity even if only one agent in the team has access to the information of the virtual leader. We finally show some numerical simulations to illustrate the theoretical results.     

Formation tracking of multi-vehicle systems in unknown environments using a multi-region control scheme

In this paper, a multi-region control scheme is proposed for a formation of nonholonomic vehicles to track a reference trajectory while avoiding collisions and preserving network connectivity in unknown environments. The proposed control scheme defines three regions, safe region, dangerous region and transition region. In different regions, priority is given to different control objectives. In safe region where trajectory tracking holds the priority, the proposed control scheme guarantees bounded tracking of the reference trajectory for each vehicle. In dangerous region where avoidance control is the main objective, a new bounded potential function is designed to characterise constraints of obstacle and inter-vehicle collision avoidance as well as connectivity maintenance. By introducing a series of transition functions, smooth switching between trajectory tracking and avoidance control is achieved in transition region. It has been proved that each vehicle can track its reference trajectory while satisfying the constraints simultaneously with a bounded controller which means that the proposed control scheme satisfies input constraints by properly tuning parameters. Simulation results demonstrate the effectiveness of the proposed method.     

Flocking with connectivity preservation for disturbed nonlinear multi-agent systems by output feedback

This paper considers the problem of flocking with connectivity preservation for a class of disturbed nonlinear multi-agent systems. In order to deal with the nonlinearities in the dynamic of all agents, some auxiliary variables are introduced into the state observer for stability analysis. By proposing a bounded potential function and using adaptive theory, a novel output feedback consensus algorithm is developed to guarantee that the states of all agents achieve flocking with connectivity preservation.     

Using Fuzzy Logic to Design Separation Function in Flocking Algorithms

Flocking algorithms essentially consist of three components: alignment, cohesion, and separation. To track a desired trajectory, the flock center should move along the desired trajectory, and thus, the fourth component, navigation, is necessary. The alignment, cohesion, and navigation components are well implemented through consensus protocols and tracking controls, while the separation component is designed through heuristic-based approaches. This paper proposes a fuzzy logic solution to the separation component. The TS rules and Gaussian membership functions are used in fuzzy logic. For fixed network flocking, a standard stability proof by using LaSalle's invariance principle is provided. For dynamic network flocking, a Filipov solution definition is given for nonsmooth dynamics. Then, a LaSalle's invariance principle for nonsmooth dynamics is used to prove the stability. A group of mobile robots with double integrator dynamics is simulated for the flocking algorithms in a 2-D environment.      

Hybrid Control for Connectivity Preserving Flocking

In this technical note, we address the combined problem of motion and network topology control in a group of mobile agents with common objective the flocking behavior of the group. Instead of assuming network connectivity, we enforce it by means of distributed topology control that decides on both deletion and creation of communication links between agents, adapting the network to the group's spatial distribution. With this protocol ensuring network connectivity, a decentralized motion controller aligns agent velocity vectors and regulates inter-agent distances to maintain existing network links. The stability of the flocking controller is established in continuous time by means of an observability argument on a quadratic form of the graph Laplacian that exploits the time delay between link deletion and creation caused by the topology control protocol, which induces a dwell time between network switches.      

基于局部估计的功率驱动多智能体网络牵制蜂拥控制算法

针对复杂功率驱动系统控制中现有牵制控制策略在保持拓扑结构连通性和降低功率消耗方面存在的不足,提出一种基于局部估计的功率驱动多智能体网络的牵制蜂拥控制算法。该算法首先利用幂迭代一致估计算法动态估计多智能体网络的代数连通度;然后根据代数连通度的局部估计值及最大功率约束自适应调整个体发射半径,以保持多智能体网络在演化过程中拓扑结构的连通性,并有效降低功率消耗;最后通过仿真实验验证了所提出控制算法的有效性。     

多无人机三维群集算法的适用性研究与改进

针对多无人机群三维空间运动的复杂群集控制问题,提出了基于生物群集行为、依据Reynolds规则描述的三维群集控制算法。已有的研究大多将无人机群集运动简化为二维平面运动,但这不符合实际控制需求。为此,将群集控制算法和人工势场算法推广到三维无人机群集控制中,建立了三维无人机群空间运动模型,通过多种不同条件下的仿真,研究了两种算法在三维群集控制中的有效性。结果显示两种算法用于三维群集控制均具有一定效果,但相对二维所需要的条件更为苛刻。同时,注意到智能算法具有更好的群体聚集效果,而人工势场算法则避碰效果更迅速明显。据此,对人工势场算法和智能算法进行了改进,通过在距离大于平衡点时采用智能算法聚集,在距离小于平衡点时采用人工势场算法避碰,得到能同时获得更好的聚集、避碰效果的新的群集控制算法。     

Dynamic target tracking and observing in a mobile sensor network

This paper presents novel approaches to (1) the problem of flocking control of a mobile sensor network to track and observe a moving target and (2) the problem of sensor splitting/merging to track and observe multiple targets in a dynamic fashion. First, to deal with complex environments when the mobile sensor network has to pass through a narrow space among obstacles, we propose an adaptive flocking control algorithm in which each sensor can cooperatively learn the network’s parameters to decide the network size in a decentralized fashion so that the connectivity, tracking performance and formation can be improved. Second, for multiple dynamic target tracking, a seed growing graph partition (SGGP) algorithm is proposed to solve the splitting/merging problem. To validate the adaptive flocking control we tested it and compared it with the regular flocking control algorithm. For multiple dynamic target tracking, to demonstrate the benefit of the SGGP algorithm in terms of total energy and time consumption when sensors split, we compared it with the random selection (RS) algorithm. Several experimental tests validate our theoretical results.     

Localized fault-tolerant topology control in wireless ad hoc networks

Topology control algorithms have been proposed to maintain network connectivity while improving energy efficiency and increasing network capacity. However, by reducing the number of links in the network, topology control algorithms actually decrease the degree of routing redundancy. As a result, the derived topology is more susceptible to node failures or departures. In this paper, we resolve this problem by enforcing k-vertex connectivity in the topology construction process. We propose a fully localized algorithm, fault-tolerant local spanning subgraph (FLSS), that can preserve k-vertex connectivity and is min-max optimal among all strictly localized algorithms (i.e., FLSS minimizes the maximum transmission power used in the network, among all strictly localized algorithms that preserve k-vertex connectivity). It can also be proved that FLSS outperforms two other existing localized algorithms in terms of reducing the transmission power. We also discuss how to relax several widely used assumptions in topology control to increase the practical utility of FLSS. Simulation results indicate that, compared with existing distributed/localized fault-tolerant topology control algorithms, FLSS not only has better power-efficiency, but also leads to higher network capacity. Moreover, FLSS is robust with respect to position estimation errors.     

基于可连Cell的无线传感器网络拓扑控制算法

在无线传感器网络的拓扑控制(TC)中,基于Cell的TC算法被认为是一类可以节省传感器节点能量并延长网络生命周期的方法,但是其需要较多的骨干网节点并且无法保证连通性.通过分析现今算法的内在局限性,提出了一种1-Con思想:当一个Cell的头节点被加入当前骨干网时,所有其可以连接的Cell使用该节点连入拓扑结构,然后此新骨干网递归地继续扩大.基于此思想,设计了一种基于可连Cell的拓扑控制算(CCTC),并从理论上证明:1)CCTC可以保证其所形成的拓扑结构维持网络连通;2)每一轮用于形成骨干网的工作节点非常少.CCTC的计算复杂度是线性的,空间复杂度和信息交换量都是常数量级.仿真实验同样显示,CCTC可以在提供良好鲁棒性和较少的消息交换的情况下,更有效地节省节点能耗并延长网络生命周期.