切换拓扑下非线性多智能体系统自适应神经网络一致性

本文研究一类具有未知控制系数的非线性多智能体系统自适应神经网络分布式控制策略.首先,针对切换拓扑下具有未知控制系数的非线性多智能体系统一致性问题,提出一类自适应神经网络一致性控制算法.其中,采用神经网络函数逼近方法解决系统中的不确定性问题,并设计一项自适应光滑项处理有界扰动和神经网络函数逼近误差.随后,证明了切换拓扑下具有未知控制系数的非线性多智能体系统的一致性,并保证了闭环系统的有界性.此外,本文把相关的一致性算法扩展到了一般有向图含有一个有向生成树的情形.最后,通过仿真实例验证了本文所提算法的有效性.     

非线性多智能体系统的无模型自适应聚类一致性控制

针对一类模型未知的离散时间非线性多智能体系统聚类一致性问题,提出一种无模型自适应控制算法.首先,假设系统具有固定拓扑,利用伪偏导数概念得到系统的数据关系模型,在考虑多智能体之间耦合系数条件下给出聚类一致性误差,在此基础上设计一种数据驱动的聚类一致性跟踪控制协议;然后,采用压缩映射方法在理论上分析了跟踪误差的收敛性,结果表明所提出算法不需要智能体模型信息即可完成跟踪任务,是一种数据驱动的控制方法;最后,将结果拓展至随机切换拓扑结构的多智能体系统中,数值仿真结果验证了所提出算法的有效性.     

一类工业运行过程多模型自适应控制方法

针对一类动态未知的工业运行过程,提出一种基于神经网络补偿和多模型切换的自适应控制方法.为充分考虑底层跟踪误差对整个运行过程优化和控制的影响,将底层极点配置控制系统和上层运行层动态模型相结合,作为运行过程动态模型.针对参数未知的运行过程动态模型,设计由线性鲁棒自适应控制器、基于神经网络补偿的非线性自适应控制器以及切换机制组成的多模型自适应控制算法.采用带死区的递推最小二乘算法在线辨识控制器参数,克服了投影算法收敛速度慢、对参数初值灵敏的局限.理论分析和仿真实验结果表明了所提方法的有效性.     

不确定非线性多智能体系统的分布式模糊自适应镇定控制

针对一类具有未知非线性和未知参数摄动的非线性多智能体系统, 提出一种分布式模糊自适应镇定控制方法. 基于邻接智能体信息和部分智能体的自身信息, 分别设计静态耦合和动态耦合的分布式模糊自适应控制律. 基于Lyapunov 稳定性理论, 证明了所提出的控制器能使得系统状态最终稳定于原点的邻域内. 仿真实例验证了所提出方法的有效性.

     

二阶非线性多智能体系统有限时间分布式优化

本文研究一类具有未知常参数的二阶非线性多智能体系统的有限时间自适应分布式优化.首先,通过给定各个智能体的二次目标函数,并结合多智能体系统达到一致性的条件,构造含有惩罚因子的惩罚函数,提出加速智能体状态收敛至目标函数最优解的控制策略.其次,在给定惩罚因子下,基于幂积分方法和有限时间稳定理论,设计有限时间分布式自适应控制协...     

二阶多智能体系统参数自适应的有限时间一致性算法

目前二阶多智能体系统尚未明确给出自适应参数的确定方法,且系统的收敛速度较慢.为在实际应用中预测飞行器多智能体系统下一时刻的状态并提高收敛速度,提出一种参数自适应的一致性算法.将当前智能体间位置和速度的差值作为一致性协议的反馈参数,研究固定拓扑和切换拓扑情形下二阶多智能体系统的有限时间一致性问题,构造Lyapunov函数,同时利用LaSalle不变集原理和齐次理论,得到系统在有限时间内达到稳定的条件,实现对不同飞行器输入状态的自适应调节.仿真结果表明,该算法能够保证多智能体系统在有限时间内实现一致跟踪,且收敛速度较快.     

基于神经网络逼近的异构多智能体系统二分拟一致性

研究一类具有未知动力学的二阶异构非线性多智能体系统二分拟一致性问题.针对二阶多智能体系统中未知的非线性动态,基于神经网络逼近理论设计一类自适应控制协议,以保证所有智能体最终收敛到有界区域内.借助Lyapunov稳定性理论和不等式技巧得到异构多智能体系统实现领导-跟随二分拟一致性的充分性条件,并给出一致性误差的上界.最后通过数值仿真验证了理论结果的有效性.     

基于观测器的多智能体系统一致性控制与故障检测

针对一类受到未知干扰的非线性多智能体系统,提出了一种鲁棒一致性控制与故障检测算法.首先,针对每个智能体系统设计了一个未知输入非线性观测器.然后,基于观测器的状态估计信息,设计了鲁棒一致性控制协议.控制协议保证了给定的干扰抑制性能指标.接着,考虑智能体出现故障的情形,采用自适应阈值法,提出了一种分布式故障检测算法.最后,以多个直流电机驱动的单摆系统为例进行了仿真实验,仿真结果表明了一致性控制与故障检测算法的有效性.     

随机多智能体系统一致性增益的设计与分析

在固定和切换拓扑中通信网络含有加性随机噪声的情况下,针对随机多智能体系统一致性跟踪控制问题,本文采用自适应控制方法给出了一种新的一致性增益设计方法.在基于邻居智能体状态设计的分布式自适应控制协议中,每个跟随者的一致性增益自适应律仅仅依赖于跟踪误差,并且与通信网络全局信息无关.结合代数图论,随机理论工具和自适应控制得到两个结论:1)每个跟随者以均方意义下跟踪上领导者; 2)每个跟随者的一致性增益趋于一个理想估计值.通过两个仿真实例验证算法的有效性.     

不确定非线性系统的鲁棒自适应输出反馈控制

基于自适应非线性阻尼,提出一种鲁棒自适应输出反馈控制方法。该方法适用于带有未建模动态、未知非线性、有界扰动、未知非线性参数和不确定控制系数的多输入多输出非线性系统。理论证明,在一定的假设条件下,该方法能保证闭环系统所有动态信号有界;不论有多少不确定非线性参数、多高阶的非线性系统,只需要一个自适应控制参数和观察参数;而且通过选择适当的控制器和观测器参数,能使控制误差和估计误差达到任意小。仿真结果表明了所提出方法的有效性。     

Adaptive consensus of multi-agents in networks with jointly connected topologies

In this paper, the consensus problem of multi-agent following a leader is studied. An adaptive design method is presented for multi-agent systems with non-identical unknown nonlinear dynamics, and for a leader to be followed that is also nonlinear and unknown. By parameterizations of unknown nonlinear dynamics of all agents, a decentralized adaptive consensus algorithm is proposed in networks with jointly connected topologies by incorporating local consensus errors in addition to relative position feedback. Analysis of stability and parameter convergence of the proposed algorithm are conducted based on algebraic graph theory and Lyapunov theory. Finally, examples are provided to validate the theoretical results.     

Decentralised consensus for multiple Lagrangian systems based on event-triggered strategy

This paper considers the decentralised event-triggered consensus problem for multi-agent systems with Lagrangian dynamics under undirected graphs. First, a distributed, leaderless, and event-triggered consensus control algorithm is presented based on the definition of generalised positions and velocities for all agents. There is only one triggering function for both the generalised positions and velocities and no Zeno behaviour exhibited under the proposed consensus strategy. Second, an adaptive event-triggered consensus control algorithm is proposed for such multi-agent systems with unknown constant parameters. Third, based on sliding-mode method, an event-triggered consensus control algorithm is considered for the case with external disturbance. Finally, simulation results are given to illustrate the theoretical results.     

Distributed adaptive output feedback tracking control for a class of uncertain nonlinear multi-agent systems

This paper addresses the distributed output feedback tracking control problem for multi-agent systems with higher order nonlinear non-strict-feedback dynamics and directed communication graphs. The existing works usually design a distributed consensus controller using all the states of each agent, which are often immeasurable, especially in nonlinear systems. In this paper, based only on the relative output between itself and its neighbours, a distributed adaptive consensus control law is proposed for each agent using the backstepping technique and approximation technique of Fourier series (FS) to solve the output feedback tracking control problem of multi-agent systems. The FS structure is taken not only for tracking the unknown nonlinear dynamics but also the unknown derivatives of virtual controllers in the controller design procedure, which can therefore prevent virtual controllers from containing uncertain terms. The projection algorithm is applied to ensure that the estimated parameters remain in some known bounded sets. Lyapunov stability analysis shows that the proposed control law can guarantee that the output of each agent synchronises to the leader with bounded residual errors and that all the signals in the closed-loop system are uniformly ultimately bounded. Simulation results have verified the performance and feasibility of the proposed distributed adaptive control strategy.     

Event-triggered adaptive consensus control of nonlinear multi-agent systems with unknown control directions and actuator saturation

This article addresses the event-triggered adaptive consensus control of nonlinear multi-agent systems with unknown control direction and actuator saturation. A new robust adaptive control algorithm based on an event-triggered mechanism is designed. The smooth Lipschitz function approximates the saturated nonlinear function, while the Nussbaum function handles unknown control directions and residual terms. The event-triggered mechanism is designed to determine the time of communication, significantly reducing the communication burden. An additional estimator is utilized to deal with unknown parameters involved in neighbor dynamics and prevent information exchange to consistency errors between connected subsystems. The results show that all the signals of the closed-loop system are uniformly bounded, and the consensus tracking error converges to a bounded set. Meanwhile, Zeno's behavior is eliminated. Simulation results confirm the superiority of the proposed method.     

Adaptive Event-triggered Control for Stochastic Nonlinear Multi-agent Systems with Unknown Control Directions

This paper focuses on the adaptive event-triggered consensus control problem for a class of stochastic nonlinear multi-agent systems with unknown nonlinear control directions and external disturbances. The heterogeneous nonlinear dynamics and non-identical unknown control directions are discussed for different agents. The application of the event-triggered mechanism can effectively decrease the update frequency of the controller, and unknown nonlinear dynamics are solved by using fuzzy logic systems. Under the action of the designed distributed controller, all signals of this stochastic multi-agent systems can reach semi-globally uniformly ultimately bounded (SGUUB) in mean square. Furthermore, Zeno behavior can be ruled out by the existence of positive inter-event intervals. Finally, a simulation example is presented to verify the feasibility of the algorithm.

     

Prescribed performance global stable adaptive neural dynamic surface consensus tracking control of stochastic multi-agent systems with hysteresis inputs and nonlinear dynamics

This paper focuses on the leader-following consensus control problem of stochastic multi-agent systems with hysteresis inputs and nonlinear dynamics. A leader-following consensus scheme is presented for stochastic multi-agent systems directions under directed graphs, which can achieve predefined synchronisation error bounds. By mainly activating an auxiliary robust control component for pulling back the transient escaped from the neural active region, a multi-switching robust neuro adaptive controller in the neural approximation domain, which can achieve globally uniformly ultimately bounded tracking stability of multi-agent systems recently. A specific Nussbaum-type function is introduced to solve the problem of unknown control directions. Using a dynamic surface control technique, distributed consensus controllers are developed to guarantee that the outputs of all followers synchronise with that of the leader with prescribed performance. Based on Lyapunov stability theory, it is proved that all signals in closed-loop systems are uniformly ultimately bounded and all the follower agents can keep consensus with the leader. Two simulation examples are provided to illustrate the effectiveness and advantage of the proposed control scheme.     

Consensus problems for multi-agent systems with nonlinear algorithms

The paper investigates consensus problems for multi-agent systems with nonlinear algorithms. Group consensus algorithms with actuator saturation for the first-order and second-order multi-agent systems are proposed. In addition, the adaptive consensus algorithm with nonlinear dynamic is also given. By applying the graph theory, Lyapunov function, and LaSalle’s invariance principle, consensus conditions for multi-agent systems are derived. Finally, three simulation examples are provided to denote the effectiveness of obtained theoretical results.     

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.     

Consensus in multi-agent systems with nonlinear uncertainties under a fixed undirected graph

This paper presents consensus algorithms by integrating cooperative control and adaptive control laws for multi-agent systems with unknown nonlinear uncertainties. An ideal multi-agent system without uncertainties is introduced first. The cooperative control law, based on an artificial potential function, is designed to make the ideal multi-agent system achieve consensus under a fixed and connected undirected graph. The presence of uncertainties will degenerate the performance, or even destabilize the whole multi-agent system. The L ₁ adaptive control law is therefore introduced to handle unknown nonlinear uncertainties. Two different consensus cases are considered: 1) normal consensus—where all agents reach an agreement on an initially undetermined position and velocity, and 2) consensus with a virtual leader—where all agents’ states converge to the virtual leader’s states. Under a fixed and connected undirected graph, the presented consensus algorithms enable the real multi-agent system to stay close to the ideal multi-agent system which achieves consensus with or without a virtual leader. Simulation results of 2-D consensus with nonlinear uncertainties are provided to demonstrate the presented algorithms.