基于MAS的无人机新型编队算法

无人机群极大地拓宽了无人机的应用领域,有效地弥补了单架无人机执行任务的缺陷。本文针对无人机群的运动一致性和编队控制,将领导者-跟随者法中领导者与跟随者的控制算法进行集成,设计了一种基于多智能体系统(MAS)的新型无人机编队控制算法。该算法既适用于领导者又适用于跟随者,并且可随机指定无人机为领导者,领导者可以受到跟随者导航反馈的影响,提高了系统的适应性和可伸缩性。理论分析表明,无人机群不会发生碰撞并且能够收敛到期望的编队队形。再经过数值模拟验证了算法的速度和轨迹跟踪能力。结果表明,该算法能保证一群无人机按预定的编队和航迹飞行。     

基于滑模观测器的非线性系统迭代学习控制

针对相当广泛的一类非线性系统有限时间轨迹跟踪问题,提出了一种基于滑模观测器的迭代学习控制算法。根据系统的非线性特性,利用一种滑模观测器对系统的状态进行估计,根据估计信号设计了一种类D型开环迭代学习控制律。这种控制方法不需要对系统的跟踪误差信号进行微分,从而对系统的量测噪声不敏感。给出了控制算法的收敛性证明,通过仿真实验证明了这种算法的有效性。     

带有初态学习的指数变增益迭代学习控制

针对一类非线性时变系统在有限时间区间上的轨迹跟踪问题,提出一种新的迭代学习控制算法,该算法对系统的控制输入和初始状态同时采用闭环指数变增益迭代学习律。基于算子理论,对具有任意初始状态的系统,在该迭代学习律作用下的收敛性进行严格证明,同时给出该迭代学习算法收敛的谱半径形式的充分条件。该算法与固定增益的迭代学习控制相比较,不仅加快了收敛速度,而且还解决了指数变增益迭代学习控制要求初始状态严格重复的问题。仿真结果表明了该算法的有效性。     

一类线性时变系统组合自适应迭代学习控制

针对一类有限时间区间上具有可重复性的BIBO稳定的一阶线性时变系统,将模型参考自适应控制方法与迭代学习相结合,提出了组合模型参考自适应迭代学习控制算法.基于Lyapunov方法推导出迭代学习控制律以及针对时变惯性参数与时不变高频增益的组合自适应参数更新律.该算法适于控制快时变系统,并使跟踪误差、参数估计误差和控制信号有界.当迭代次数趋于无穷时,跟踪误差关于有限时间区间一致收敛到零.系统仿真验证了所提控制算法的有效性.     

非线性系统在任意初态下的闭环迭代学习控制

针对一类在有限时间区间上重复运行的非线性系统,提出了一种带有初态学习的闭环D型迭代学习控制算法,并利用算子理论证明了系统在任意初态条件下经过迭代学习后,其输出能够完全跟踪期望轨迹,同时得到了该算法收敛的谱半径形式的充分条件。该算法不仅解决了闭环D型迭代学习控制的初态问题,而且还放宽了收敛条件。最后通过simulink、matlab语言和S函数相结合的方法对控制系统进行了仿真,仿真结果验证了该算法的有效性。     

具有不可微输入信号的改进型重复变结构控制

针对一类具有结构不确定性和周期性外部扰动的非线性系统,提出了一种改进型重复变结构控制（modified repetitive variable structure control, MRVSC）算法。在传统积分滑模面的基础上构造了改进型积分滑模面（modified integral sliding mode surface, MISMS）。基于MISMS,采用Lyapunov方法设计的MRVSC算法保证了非线性系统的运动轨迹在有限时间内到达MISMS并保持在MISMS上,并且使得滑模动力学系统变为重复控制系统。根据重复控制系统的稳定性条件及灵敏度函数选择MISMS的参数。仿真结果显示,与传统积分滑模控制算法进行比较,MRVSC算法使得非线性系统具有更好的跟踪性能。     

一类非线性系统在任意初值下的开环D型迭代学习控制

给出了一类非线性时变系统在任意初值条件下采用开环D型迭代学习控制算法时的收敛条件,运用算子理论进行收敛性证明。该算法克服了系统输出信号跟踪期望输出依赖于期望状态和期望输入的缺陷,解决了迭代学习控制中的初始状态问题,而且收敛条件放宽了,给出了仿真研究实例,研究结果说明该算法的有效性。     

考虑时延的多Euler-Lagrange系统自适应神经网络协调跟踪控制

考虑存在通讯时延,在有向通讯拓扑结构下研究多Euler-Lagrange系统的协调跟踪控制问题。仅有部分跟随者可以获得静态领航者信息。对每一个跟随者设计了一种分布式观测器,以获得领航者的状态量。针对系统模型具有非线性不确定性和外部扰动情况,基于神经网络方法提出了两种分布式自适应协调控制律,分别使每一个跟随者对领航者的跟踪误差最终有界和渐近收敛到零。运用Lyapunov稳定性理论对两种控制律的稳定性进行了证明。数值仿真验证了本文提出的控制律的有效性。     

多智能体系统的非光滑二阶一致性协议

针对二阶积分器多智能体系统,提出了一类非光滑一致性协议设计方法。首先,基于反步设计方法,将速度看成虚拟控制量,并设计虚拟速度,使得状态一致性可以渐近达到。然后,基于有限时间控制方法设计控制律使得真实速度在有限时间内跟踪上虚拟速度。该控制律使得:当系统不存在外部扰动时,一致性可以渐近达到;当系统存在外部扰动时,任意两个智能体间的稳态误差渐近收敛到原点附近的一个小邻域内。仿真结果表明了该方法的有效性。     

基于奇异摄动理论的非仿射非线性系统的渐近稳定

基于奇异摄动理论结合动态逆设计的控制方法,研究了一类非仿射非线性系统的渐近稳定问题.通过将控制律定义为一个快动态系统的解,使原系统的状态能够准确地跟踪参考系统,且跟踪误差为零.同时给出了系统吸引区内的一个正不变子集,并证明了系统状态能够在有限时间内进入该子集,最终实现渐进稳定.用该方法构造的控制器实现起来较为简单,仿真结果表明了理论方法的有效性.     

DISTRIBUTED FINITE-TIME χ-CONSENSUS ALGORITHMS FOR MULTI-AGENT SYSTEMS WITH VARIABLE COUPLING TOPOLOGY

<正> This paper considers finite-time χ-consensus problem for a multi-agent system with firstorderindividual dynamics and switching interaction topologies.Several distributed finite-time consensusrules are constructed for multi-agent dynamics in a unified way with the help of Lyapunovfunction and graph theory as well as homogeneity.Time-invariant non-smooth forms of finite-timeneighbor-based controllers are proposed and a numerical example is shown for illustration.     

Homogeneous Finite-Time Consensus Control for Higher-Order Multi-Agent Systems by Full Order Sliding Mode

This paper investigates the distributed finite-time consensus tracking problem for higher-order nonlinear multi-agent systems (MAS_s). The distributed finite-time consensus protocol is based on full order sliding surface and super twisting algorithm. The nominal consensus control for the MASs is designed based on the geometric homogeneous finite time control technique. The chattering is avoided by designing a full order sliding surface. The switching control is constructed by integrating super twisting algorithm, hence a chattering alleviation protocol is obtained to maintain a smooth control input. The finite time convergence analysis for the leader follower network is presented by using strict Lyapunov function. Finally, the numerical simulations validate the proposed homogeneous full-order sliding mode control for higher-order MAS_s.     

单边Lipschitz非线性多智能体系统一致性追踪控制

针对单边Lipschitz非线性多智能体系统,提出了一种分布式一致性控制方法.首先,构建了领导-跟随者动力学结构,用于实现单边Lipschitz多智能体系统的追踪控制.然后,设计了单边Lipschitz非线性多智能体系统的一致性控制协议,可根据智能体之间局部交互信息构建分布式反馈控制,并将系统的一致性追踪问题转化为系统...     

Consensus for Heterogeneous Multi-Agent Systems with Directed Network Topologies

In this paper, the consensus problem for heterogeneous multi-agent systems composed of first-order and second-order agents is investigated with directed network topologies. Based on a system transformation method, this consensus problem is turned into a consensus problem for homogeneous multi-agent systems. With certain assumption on the control parameters, firstly, necessary and sufficient condition for consensus is proposed with fixed topology. Secondly, sufficient condition is proposed for heterogeneous multi-agent systems to achieve consensus with switching topologies. Finally, simulation examples are presented to verify the effectiveness of the theoretical results.     

Robust consensus tracking of heterogeneous multi-agent systems under switching topologies

This paper considers a robust consensus tracking problem of heterogeneous multi-agent systems with time-varying interconnection topologies. Based on common Lyapunov function and internal model techniques, both state and output feedback control laws are derived to solve this problem. The proposed design is robust by admitting some parameter uncertainties in the multi-agent system.     

Consensus control for leader-following multi-agent systems with measurement noises

<正> This work is concerned with consensus control for a class of leader-following multi-agentsystems (MASs).The information that each agent received is corrupted by measurement noises.Toreduce the impact of noises on consensus,time-varying consensus gains are adopted,based on whichconsensus protocols are designed.By using the tools of stochastic analysis and algebraic graph theory,asufficient condition is obtained for the protocol to ensure strong mean square consensus under the fixedtopologies.This condition is shown to be necessary and sufficient in the noise-free case.Furthermore,by using a common Lyapunov function,the result is extended to the switching topology case.     

Time-Variant Consensus Tracking Control for Networked Planar Multi-Agent Systems with Non-Holonomic Constraints

A time-variant consensus tracking control problem for networked planar multi-agent systems with non-holonomic constraints is investigated in this paper. In the time-variant consensus tracking problem, a leader agent is expected to track a desired reference input, simultaneously, follower agents are expected to maintain a time-variant formation. To solve the time-variant consensus tracking problem of planar multi-agent systems with non-holonomic constraints, a time-variant consensus tracking control strategy is designed on the basis of an unidirectional topology structure. One of main contributions of this paper is the time-variant consensus tracking protocol for general time-variant formations of planar multi-agent systems with non-holonomic constraints, the other main contribution of this paper is an active predictive control strategy, where predictions of agents are generated actively, so that the computational efficiency is improved than passive approaches. The proposed control strategy is verified by two types of time-varying formations of wheeled mobile robots, and the experimental results show that the proposed control strategy is effective for general time-variant consensus tracking problems of planar multi-agent systems with non-holonomic constraints in local and worldwide networked environments.     

Finite-time tracking control for motor servo systems with unknown dead-zones

A finite-time tracking control scheme is proposed in this paper based on the terminal slid- ing mode principle for motor servo systems with unknown nonlinear dead-zone inputs. By using the differential mean value theorem, the dead-zone is represented as a time-varying system and thus the inverse compensation approach is avoided. Then, an indirect terminal sliding mode control （ITSMC） is developed to guarantee the finite-time convergence of the tracking error and to overcome the singu- larity problem in the traditional terminal sliding mode control. In the proposed controller design, the unknown nonlinearity of the system is approximated by a simple sigmoid neural network, and the ap- proximation error is diminished by employing a robust term. Comparative experiments on a turntable servo system are conducted to show the superior performance of the proposed method.     

并联式运载器上升段广义超螺旋有限时间控制

针对受模型不确定和外部干扰影响的并联式运载器上升段姿态控制问题, 提出了一种基于广义超螺旋算法的自适应滑模有限时间控制方法。首先, 将姿态跟踪控制问题转化为跟踪误差系统的镇定问题, 建立了面向控制的模型。其次, 将单输入单输出(single input single output, SISO)固定时间广义超螺旋算法拓展应用到多输入多输出(multiple input multiple output, MIMO)耦合非线性系统上, 基于该算法设计了固定时间状态观测器和自适应滑模有限时间控制器, 利用Lyapunov稳定性理论证明了闭环系统的有限时间稳定特性。最后, 通过与传统比例-微分(proportional and differential, PD)控制器仿真对比, 验证了该方法具有更优的控制精度和鲁棒性。     

Network robustness depth and topology management of networked dynamic systems

Networked control systems are subject to adversary conditions that affect their network topologies. To ensure reliable system operations, network topologies need to be characterized and managed for their impact on the overall system performance. This paper introduces the concept of network robustness depth for this pursuit. Discrete event systems are used as a foundation to model dynamic behavior of network topologies, support their analysis, and carry out their management. Stochastic analysis relates the link reliability probabilities to a probabilistic characterization of network robustness depth. Several topology management strategies are discussed, including passive methods, random strategies, and optimization methodologies. Their respective benefits and limitations are quantified. By using platoon control as a platform of hybrid (continuous and discrete event) systems and packet erasure channels as a communication protocol, the results are demonstrated with case studies.