有向图中模块化航天器系统相对轨道的自适应分布式一致性

基于多智能体系统一致性理论,对模块化航天器相对轨道的分布式一致性问题进行了研究.各模块之间的信息交互拓扑结构为更具一般性的有向图.当存在模块质量不确定性的情形下,设计了仅依赖模块自身及其邻近模块信息且无需模块间相对速度信息的自适应控制算法.针对模型中存在外部干扰的情形,通过引入带有时变自适应参数的变结构控制项,实现了对未知上界干扰的补偿,并且证明了闭环系统是渐近稳定的.此外,本文所设计的算法具有分布式的特点,不会因为模块数量的增多而增加所提算法的复杂度.最后对6个模块组成的模块化航天器的编队飞行进行了仿真分析,仿真结果表明本文设计的控制律是有效可行的.     

多航天器相对轨道与姿态耦合分布式自适应协同控制

基于一致性理论,在有向通讯拓扑结构下对多航天器系统相对轨道及姿态的耦合协同控制问题进行了研究.本文考虑近地航天器相对轨道的非线性方程以及用罗德里格参数描述的航天器姿态运动方程,建立了考虑控制输入耦合的六自由度航天器运动模型.在仅有部分跟随航天器可获取参考状态(记为领航航天器)的情形下,针对航天器存在未建模动态以及外部环境干扰等问题,提出了一种基于切比雪夫神经网络(Chebyshev neural networks,CNN)的自适应增益控制律,使得各跟随航天器在轨道交会的同时姿态保持一致.因为每个航天器上的控制算法仅依赖其自身及相邻航天器的信息,因此控制算法是分布式的.同时考虑到航天器之间的相对速度及相对角速度难以测量,提出了无需相对速度及角速度信息的分布式自适应协同控制律使得各航天器保持一定的队形且具有期望的相对指向.最后对6颗航天器的编队飞行进行了仿真分析,仿真结果表明本文设计的分布式自适应协同控制律是有效可行的.     

基于相对位置信息的多智能体系统自适应跟踪控制

针对带有动态领导者的多智能体系统,为了使其达到跟踪一致性,设计只依赖于相对位置信息的自适应跟踪控制律.根据接收到的相对位置信息为每个跟随者设计动态输出反馈控制律,并根据控制律估计出智能体之间的相对速度信息.在此基础上设计自适应跟踪控制律,并且通过Lyapunov 稳定性理论和矩阵理论分析得到使系统达到跟踪一致性的充分条件.最后通过数值仿真验证了所提出的设计方法的有效性.     

控制增益未知的多变量极值搜索系统神经网络自适应协同控制

针对一类控制增益未知的多变量极值搜索系统,提出了一种神经网络自适应协同控制方法.该方法利用协同控制实现状态变量之间的协同收敛,并确保对系统内部参数扰动和外界干扰具有不变性;以极值搜索控制方法得到的搜寻变量作为输入量,设计多层神经网络逼近状态变量的极值变化率和未知的变量与函数;采用Nussbaum函数解决系统控制增益未知的问题;同时运用自适应参数抵消神经网络逼近误差的影响.稳定性分析证明了系统的状态跟踪误差、输出量与其极值之间的误差、极值搜索变量的跟踪误差以及神经网络各参数的估计误差均指数收敛至原点的一个有界邻域.理论分析与仿真结果验证了该方法的有效性.     

带未知干扰的模块化航天器系统相对轨道的队形控制

基于多智能体系统一致性理论,在有向拓扑结构中对模块化航天器相对轨道的队形控制问题进行研究.考虑与状态相关的未知外部干扰,在存在模块质量不确定性的情形下,基于自适应增益技术,设计仅依赖模块自身及其邻近模块信息的分布式控制算法,并通过Lyapunov稳定性方法证明闭环系统是渐近稳定的.最后在Matlab/Simulink中对6个模块组成的模块化航天器系统的队形进行仿真分析,仿真结果表明所设计的控制律是有效且可行的.     

多智能体系统自适应跟踪控制

基于带有非线性动态的二阶多智能体系统,研究了在有动态领导者条件下的跟踪一致性问题。假设跟随者只能获取邻居智能体的相对状态信息,只有一部分跟随者可以获得领导者的位置和速度信息,领导者的控制输入非零且不被任何一个跟随者可知。在通信拓扑为无向连通图的条件下,为了避免全局信息的不确定性,设计了分布式自适应控制协议。将系统的一致性问题转化为误差系统的一致性问题,通过Lyapunov稳定性理论和矩阵理论分析得到了该协议使系统达到一致的充分条件。最后用仿真例子证明了设计方法的有效性。     

基于Backstepping 方法的多变量系统自适应控制

基于Backstepping设计方法,对线性多变量系统设计出一种新的自适应控制器,使某些系统关于增益矩阵的假定条件便于检验,该控制器能保证系统的全局稳定性和跟踪误差趋于零。     

基于观测器的多智能体系统自适应跟踪控制

基于带有非线性动态的二阶多智能体系统,研究在有参考领导者条件下的跟踪一致性问题。假设跟随者之间的网络拓扑为有向图。针对跟随者不能得到自己的速度信息,为每个跟随者设计分布式观测器来估计自己的速度,在网络拓扑为有向图且在切换拓扑图的情况下给出基于观测器的自适应控制协议。利用Lyapunov稳定性理论和矩阵理论分析,得到使系统实现一致性的充分条件。仿真结果表明,在局部观测器和控制协议及自适应控制下,跟随者可以跟踪到领导者。     

多智能体系统及其协同控制研究进展

对多智能体系统及其协同控制理论研究和应用方面的发展现状进行了简要概述.首先给出 Agent 及多 Agent 系统的概念和特性等,介绍了研究多Agent系统协同控制时通常用到的代数图论;然后综述了近年来多Agent系统群集运动和协同控制一致性方面的研究状况,并讨论了其在军事、交通运输、智能机器人等方面的成功应用;最后,对多Agent系统未来的发展方向进行了探讨和分析,提出几个具有理论和实践意义的研究方向,以促使多Agent系统及其协同控制理论和应用的深入研究.     

无线网络化系统自适应能量调度与协同控制

无线网络化系统（wireless networked control system,WNCS）中节点能量受限是影响系统性能的重要因素．本文提出了一种自适应能量调度方法解决节点能量与控制需求问的矛盾,在采样周期与能量消耗之间关系的基础上,利用动态采样周期实现能耗的实时调节,满足节点生存时间的需求．进而,将自适应采样的WNCS建模为一类具有短暂不确定切换信号的离散切换系统,采用切换状态反馈控制律,利用切换系统理论分析了系统的稳定性,给出了系统渐近稳定时控制器增益与滞留时间需要满足的约束条件．最后在Truetimel．5和MATLAB仿真平台上验证了文中提出的方法和结论．     

Distributed cooperative control of multi-converter systems via networks

Modular converters are employed for ultra-large ship degaussing systems to improve the magnetic stealth capability. However, the degaussing performance could be compromised by the inconsistent current in various modules. Therefore, current synchronization is a crucial issue in the control of distributed multi-converter systems (DMCSs). To meet the high requirement of such an industrial scenario, a distributed cooperative control scheme is conceived for DMCSs in this paper. The consensus and stability of the DMCS are analyzed, in contrast to existing works that only take consensus into account, and the necessary and sufficient conditions are derived with the constructed closed-loop control system. Then, the tailor-made controller design guideline is presented. Different cases of a cyber-physical DMCS in the MATLAB/SimPowerSystems environment are examined to confirm the viability. Furthermore, experiments with a multi-converter prototype system are carried out to confirm the efficiency of the proposed control method. Results from simulations and experiments are in line with the theoretical analysis, validating the efficacy.     

Adaptive output feedback consensus tracking for linear multi-agent systems with unknown dynamics

In this paper, the consensus tracking problem with unknown dynamics in the leader for the linear multi-agent systems is addressed. Based on the relative output information among the agents, decentralised adaptive consensus protocols with static coupling gains are designed to guarantee that the consensus tracking errors converge to a small neighbourhood around the origin and all the signals in the closed-loop dynamics are uniformly ultimately bounded. Moreover, the result is extended to the case with dynamic coupling gains which are independent of the eigenvalues of the Laplacian matrix. Both of the protocols with static and dynamic coupling gains are designed by using the relative outputs, which are more practical than the state-feedback ones. Finally, the theoretical results are verified through an example.     

Dynamic cooperative learning control of output-feedback systems with output constraints

This paper investigates the dynamic cooperative learning control for high-order output-feedback systems under the output constraints. To avoid complex computation, we use a system transformation strategy in control design. Only one neural network (NN) is employed to approximate the unknown synthetic function for each agent. Subsequently, a NN-based cooperative learning control mechanism is designed by introducing the barrier Lyapunov function (BLF). The proposed mechanism expands the NN approximation domain of the transformed systems, and the output of all subsystems remains constrained. Further, the NNs on identified uncertain system dynamics are used to construct the experience-based controllers to carry out same control tasks. Finally, the theoretical authenticity is demonstrated by a numerical example.     

多智能体网络系统的自适应协调控制

为实现多智能体网络系统的协调控制,设计了一种新型的带有自适应协调器的控制器.基于动态图建立了多智能体网络系统的模型,并考虑了系统的非线性互联和不可避免存在的时变时滞.应用分布式控制策略,设计了自适应参数估计的协调器,用于调节智能体之间的互联强度,使网络达到稳定的预设水平.并基于Lyapunov-Kra-sovskii泛函和自适应动态偏差反馈控制技术,根据拉萨尔不变集原理证明了偏差控制系统的渐近收敛性.这种控制方法,可在系统参数不确定的情况下,同时完成参数估计和协调控制.所设计的控制律和自适应律简单,易于实现,仿真示例验证了所提方法的有效性.     

Adaptive consensus control for stochastic nonlinear multiagent systems with full state constraints

In this paper, the consensus tracking problem is investigated for stochastic nonlinear multiagent systems with full state constraints and time delays. The barrier Lyapunov functions proposed for single‐agent constrained systems are constructively extended to solve the consensus problem for multiagent systems with the full state constraints. Some Lyapunov‐Krasovskii functionals are introduced to compensate for state time delays, which are inherent in the complicated nonlinear systems. Based on the variable separation technique, the difficulty arising from the nonstrict‐feedback structure is overcome. Under a directed communication topology, the distributed neuroadaptive control protocols are proposed to guarantee that all the follower agents follow the trajectory of the leader agent and the full state constraints are not violated. The effectiveness of the proposed distributed adaptive control approach is verified via simulation examples.     

多智能体系统协调控制一致性问题研究综述*

本文综述了多智能体系统协调控制一致性问题的发展情况,介绍了解决一致性问题的主要原理和适用范围,对一致性协议进行了总结,对一致性问题的研究的主要领域进行了深入阐述,对群集、蜂涌、聚集、传感器网络估计等问题进行分析和阐述。最后讨论了以上领域尚未解决的问题和未来的研究方向。     

Adaptive consensus control of leader-following systems with transmission nonlinearities

In this paper, the leader-following consensus problem of multi-agent systems is studied. It is assumed that there exist gain and bias transmission nonlinearities in the links in the communication network. Two distributed adaptive control schemes, where the transmission nonlinearities are compensated, are designed to guarantee that the consensus problem is solved and synchronisation errors are cooperatively uniformly and ultimately bounded. By using Lyapunov stability theory, the stability of closed-loop system is proven. The effectiveness of the proposed control methods is demonstrated by simulation results.