高阶时滞多智能体系统一致性控制

本文讨论了多输入网络化时滞多智能体系统的一致性问题.应用无向或有向图描述网络化系统的拓扑结构.多智能体的一致性收敛问题可以转化为线性时滞系统的稳定性问题.通过双线性矩阵不等式可以得到高阶多智能体系统时滞相依以及时滞独立的一致性准则.最后,举例一个无向拓扑结构的时滞多智能体来说明本文方法的有效性.     

时滞反馈Lorenz系统的混沌特性及其电路实现

在研究时滞反馈Lorenz系统时, 关键是模拟时滞环节的实现. 由于数字时滞电路存在模数转换精度的问题,所以采用数字时滞电路的时滞反馈Lorenz系统容易产生不真实的电路现象. 本文研究了一种用模拟电路实现的时滞电路, 并将其加入到Lorenz系统中构成时滞状态反馈Lorenz系统. 通过对系统的数字仿真和模拟电路的制作和实验, 验证了实验和仿真的结果完全相符, 表明了时滞可以使Lorenz系统产生更复杂拓扑结构的吸引子.     

时变大时滞系统参数辨识的仿真研究

研究了时变大时滞系统的参数辨识问题.大时滞系统大多采用补偿控制方法,但是补偿控制方法需要系统的精确数学模型,因而获得大时滞系统的数学模型成为了补偿控制的关键,时变特性使问题复杂化,从而影响了大时滞系统的控制精度.为解决上述问题,提出了一种神经网络的参数辨识策略,利用一个神经元对系统的时滞参数进行辨识,从而可以将时滞从系统模型中分离出来,可利用一个RBF神经网络模型辨识系统的其它参数,使神经元的输出作为RBF神经网络的一个输入,从而实现了串-并联结构的双神经网络拓扑.拓扑结构可以比串级的神经网络提高训练速度,因而也就更适合于实时控制.针对工业锅炉回水温度控制系统的仿真结果验证了所提辨识算法的正确性.     

时变时滞与切换有向通信拓扑协议下线性多智能体系统一致性的平均驻留时间条件

研究时变时滞与切换有向通信拓扑协议下高阶连续时间线性多智能体系统的一致性问题. 利用一个线性变换将该问题等价转化为一个切换时滞系统的稳定性问题. 假定出现的每一个通信拓扑都是可一致的, 借助时滞切换系统稳定性的平均驻留时间方法, 以线性矩阵不等式(LMIs) 形式给出多智能体系统达到全局一致的充分条件. 数值实例验证了结果的正确性.

     

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.     

切换信息拓扑和时变时滞下离散时间线性多智能体系统一致性的平均驻留时间条件

研究了有向切换信息拓扑和时变时滞下离散时间线性多智能体系统的一致性问题.首先,通过适当的线性变换把一致性问题转化为相应的时变时滞线性切换系统的渐近稳定问题; 然后,利用构建的李亚普诺夫函数和平均驻留时间模式,建立了一致性问题可解的基于线性矩阵不等式的时滞依赖充分条件,研究了如下两种情形: 1)所有信息拓扑都是可一致的,2) 部分信息拓扑是可一致的; 最后,数值实例验证了结果的正确性.     

考虑时变时滞的多移动智能体分布式编队控制

考虑到多移动智能体编队控制中的时变时滞问题,在所设计的编队框架下,基于一致性算法设计了具有不对称时变时滞的分布式编队控制律.该控制律仅使用全局速度导引信息和邻居状态反馈信息;在固定通信拓扑条件下,推导了具有时变时滞的闭环系统状态方程,应用改进的自由权矩阵方法获得了保守性更小的系统稳定条件,并在时变通信拓扑条件下,将拓扑变化处理为系统结构的不确定性,同样获得了时变通信拓扑下的系统稳定条件;进行了6个智能体在平面内编队运动的仿真,实例证明,理论结果是正确的.     

一种纯时滞系统的鲁棒控制优化设计方法

针对工业控制中较常见的单容纯时滞系统难于控制的问题,考虑优化控制对象的鲁棒性强和阶跃响应时间短等综合性能,依据纯时滞系统的两次优化控制方法,利用误差绝对值与时间的积分(ITAE)作为性能评价标准,提出一种纯时滞系统鲁棒控制优化的设计方法.设计方案包括分时模型的选择和参数优化两部分.给出了相应的优化算法,使用克隆和混沌技术进行参数优化.最后给出优化参数的系统阶跃响应的对比结果.仿真结果验证了本文时滞系统的控制优化设计方法有效准确.     

时滞反馈Liu系统的动力学行为

研究了具有时滞反馈项作用的Liu系统.文中针对两种典型的时滞反馈情形,着重分析了时滞参数对系统动力学行为的影响.研究结果表明,当Liu系统存有时滞反馈时,其动力学行为将变得异常丰富;时滞参数的改变可引起系统复杂的动力学演化.此外,数值计算还发现了时滞Liu系统的虫洞吸引子.     

无线网络系统建模综述

建模是研究无线网络系统的一种重要手段,是仿真实验的基础核心,涉及系统各方面统计特征的模型描述、参数估计、建模算法的优化和各类模型组织等问题.首先简要介绍了常见的无线网络系统,归纳了衡量网络质量的性能指标体系,然后着重分析了反映系统信道、拓扑、流量和协议行为等特征的代表性数学模型,评价了主流仿真工具的优势与不足.最后对当前研究中存在的问题进行了总结,展望了未来发展趋势.     

Swarm aggregations using artificial potentials and sliding-mode control

In this paper, we consider a control strategy of multiagent systems, or simply, swarms, based on artificial potential functions and the sliding-mode control technique. First, we briefly discuss a "kinematic" swarm model in n-dimensional space introduced in an earlier paper. In that model, the interindividual interactions are based on artificial potential functions, and the motion of the individuals is along the negative gradient of the combined potential. After that, we consider a general model for vehicle dynamics of each agent (swarm member), and use sliding-mode control theory to force their motion to obey the dynamics of the kinematic model. In this context, the results for the initial model serve as a "proof of concept" for multiagent coordination and control (swarm aggregation), whereas the present results serve as a possible implementation method for engineering swarms with given vehicle dynamics. The presented control scheme is robust with respect to disturbances and system uncertainties.     

Trophallaxis within a robotic swarm: bio-inspired communication among robots in a swarm

This article presents a bio-inspired communication strategy for large-scale robotic swarms. The strategy is based purely on robot-to-robot interactions without any central unit of communication. Thus, the emerging swarm regulates itself in a purely self-organized way. The strategy is biologically inspired by the trophallactic behavior (mouth-to-mouth feedings) performed by social insects. We show how this strategy can be used in a collective foraging scenario and how the efficiency of this strategy can be shaped by evolutionary computation. Although the algorithm works stable enough that it can be easily parameterized by hand, we found that artificial evolution could further increase the efficiency of the swarm’s behavior. We investigated the suggested communication strategy by simulation of robotic swarms in several arena scenarios and studied the properties of some of the emergent collective decisions made by the robots. We found that our control algorithm led to a nonlinear, but graduated path selection of the emerging trail of loaded robots. They favored the shortest path, but not all robots converged to this trail, except in arena setups with extreme differences in the length of the two possible paths. Finally, we demonstrate how the flexibility of collective decisions that arise through this new strategy can be used in changing environments. We furthermore show the importance of a negative feedback in an environment with changing foraging targets. Such feedback loops allow outdated information to decay over time. We found that task efficiency is constrained by a lower and an upper boundary concerning the strength of this negative feedback.     

Asynchronous parallelization of particle swarm optimization through digital pheromone sharing

In this paper, a model for sharing digital pheromones between multiple particle swarms to search n-dimensional design spaces in an asynchronous parallel computing environment is presented. Particle swarm optimization (PSO) is an evolutionary technique used to effectively search multi-modal design spaces. With the aid of digital pheromones, members in a swarm can better communicate with each other to improve search performance. Previous work by the authors demonstrated the capability of digital pheromones within PSO for searching the global optimum in both single and coarse grain synchronous parallel computing environments. In the coarse grain approach, multiple swarms are simultaneously deployed across various processors and synchronization is carried out only when all swarms achieved convergence, in an effort to reduce processor-to-processor communication and network latencies. However, it is theorized that with an appropriate parallelization scheme, the benefits of digital pheromones and communication between swarms can outweigh the network bandwidth latencies resulting in improved search efficiency and accuracy. To explore this idea, a swarm is deployed in the design space across different processors. Through an additional processor, each part of the swarm can communicate with the others. While digital pheromones aid communication within a swarm, the developed parallelization model facilitates communication between multiple swarms resulting in improved search accuracy and efficiency. The development of this method and results from solving several multi-modal test problems are presented.     

Dynamic Multiple Swarms in Multiobjective Particle Swarm Optimization

A multiple-swarm multiobjective particle swarm optimization (PSO) algorithm, named dynamic multiple swarms in multiobjective PSO, is proposed in which the number of swarms is adaptively adjusted throughout the search process via the proposed dynamic swarm strategy. The strategy allocates an appropriate number of swarms as required to support convergence and diversity criteria among the swarms. Additional novel designs include a PSO updating mechanism to better manage the communication within a swarm and among swarms and an objective space compression and expansion strategy to progressively exploit the objective space during the search process. Comparative study shows that the performance of the proposed algorithm is competitive in comparison to the selected algorithms on standard benchmark problems. In particular, when dealing with test problems with multiple local Pareto fronts, the proposed algorithm is much less computationally demanding. Sensitivity analysis indicates that the proposed algorithm is insensitive to most of the user-specified design parameters.     

Towards temporal verification of swarm robotic systems

A robot swarm is a collection of simple robots designed to work together to carry out some task. Such swarms rely on the simplicity of the individual robots; the fault tolerance inherent in having a large population of identical robots; and the self-organised behaviour of the swarm as a whole. Although robot swarms present an attractive solution to demanding real-world applications, designing individual control algorithms that can guarantee the required global behaviour is a difficult problem. In this paper we assess and apply the use of formal verification techniques for analysing the emergent behaviours of robotic swarms. These techniques, based on the automated analysis of systems using temporal logics, allow us to analyse whether all possible behaviours within the robot swarm conform to some required specification. In particular, we apply model-checking, an automated and exhaustive algorithmic technique, to check whether temporal properties are satisfied on all the possible behaviours of the system. We target a particular swarm control algorithm that has been tested in real robotic swarms, and show how automated temporal analysis can help to refine and analyse such an algorithm.     

Cooperative control and communication of intelligent swarms: a survey

Individuals exchange information, experience and strategy based on communication. Communication is the basis for individuals to form swarms and the bridge of swarms to realize cooperative control. In this paper, the multi-robot swarm and its cooperative control and communication methods are reviewed, and we summarize these methods from the task, control, and perception levels. Based on the research, the cooperative control and communication methods of intelligent swarms are divided into the following four categories: task assignment based methods (divided into market-based methods and alliance based methods), bio-inspired methods (divided into biochemical information inspired methods, vision based methods and self-organization based methods), distributed sensor fusion and reinforcement learning based methods, and we briefly define each method and introduce its basic ideas. Based on WOS database, we divide the development of each method into several stages according to the time distribution of the literature, and outline the main research content of each stage. Finally, we discuss the communication problems of intelligent swarms and the key issues, challenges and future work of each method.     

A multi-swarm optimizer based fuzzy modeling approach for dynamic systems processing

Inspired by the phenomenon of symbiosis in natural ecosystems a multi-swarm cooperative particle swarm optimizer (MCPSO) is proposed as a new fuzzy modeling strategy for identification and control of non-linear dynamical systems. In MCPSO, the population consists of one master swarm and several slave swarms. The slave swarms execute particle swarm optimization (PSO) or its variants independently to maintain the diversity of particles, while the particles in the master swarm enhance themselves based on their own knowledge and also the knowledge of the particles in the slave swarms. With four benchmark functions, MCPSO is proved to have better performance than PSO and its variants. MCPSO is then used to automatically design the fuzzy identifier and fuzzy controller for non-linear dynamical systems. The proposed algorithm (MCPSO) is shown to outperform PSO and some other methods in identifying and controlling dynamical systems.     

Sliding mode control-based algorithms for consensus in connected swarms

In this article, finite-time consensus algorithms for a swarm of self-propelling agents based on sliding mode control and graph algebraic theories are presented. Algorithms are developed for swarms that can be described by balanced graphs and that are comprised of agents with dynamics of the same order. Agents with first and higher order dynamics are considered. For consensus, the agents' inputs are chosen to enforce sliding mode on surfaces dependent on the graph Laplacian matrix. The algorithms allow for the tuning of the time taken by the swarm to reach a consensus as well as the consensus value. As an example, the case when a swarm of first-order agents is in cyclic pursuit is considered.     

Stability analysis of one-dimensional asynchronous swarms

Coordinated swarm behavior in certain types of animals can also occur in groups of autonomous vehicles. Swarm "cohesiveness" is characterized as a stability property. Conditions for one-dimensional asynchronous swarms to achieve collision-free convergence even in the presence of sensing delays and asynchronism during movements are provided. Each finite-size swarm member has proximity sensors and neighbor position sensors that only provide delayed position information. Such stability analysis is of fundamental importance if one wants to understand the coordination mechanisms for "platoons" of autonomous vehicles, where intermember communication channels are less than perfect and collisions must be avoided.     

Experiences applying formal approaches in the development of swarm-based space exploration systems

NASA is researching advanced technologies for future exploration missions using intelligent swarms of robotic vehicles. One of these missions is the Autonomous Nano-Technology Swarm (ANTS) mission that will explore the asteroid belt using 1,000 cooperative autonomous spacecraft. The emergent properties of intelligent swarms make it a potentially powerful concept, but at the same time more difficult to design and ensure that the proper behaviors will emerge. NASA is investigating formal methods and techniques for verification of such missions. The advantage of using formal methods is the ability to mathematically verify the behavior of a swarm, emergent or otherwise. Using the ANTS mission as a case study, we have evaluated multiple formal methods to determine their effectiveness in modeling and ensuring desired swarm behavior. This paper discusses the results of this evaluation and proposes an integrated formal method for ensuring correct behavior of future NASA intelligent swarms.