非仿射多智能体系统的自适应神经网络控制

针对有向拓扑图下一类控制方向未知的非仿射非线性多智能体系统的输出一致性问题,综合运用中值定理、RBF神经网络及其特性、Nussbaum增益函数方法和动态面控制技巧,提出一种分布式自适应神经网络控制协议,保证跟随者的输出能与领导者的输出同步,跟踪误差能保持在零点的小邻域内.采用新的非线性滤波器代替传统动态面控制方法(CDSC)的一阶线性滤波器,改善控制性能.通过一致性分析及仿真例子验证了所提控制方法的有效性.     

非仿射非线性多智能体系统迭代学习一致跟踪

为了解决非仿射非线性多智能体系统在给定时间区间上一致性完全跟踪问题,基于迭代学习控制方法设计一种分布式一致性跟踪控制算法.首先,由引入的虚拟领导者与所有跟随者组成多智能体系统的通信拓扑,其中虚拟领导者的作用是提供期望轨迹.然后,在只有部分跟随者能够获得领导者信息的条件下,利用每个跟随者及其邻居的跟踪误差构造每个跟随者的迭代学习一致性跟踪控制器.同时采用中值定理将非仿射非线性多智能体系统转化仿射形式,并基于压缩映射方法证明所提算法的收敛性,给出算法的收敛条件.理论分析表明,在智能体的非线性函数未知情况下,利用所提算法可以使非仿射非线性多智能体系统在给定时间区间上随迭代次数增加逐次实现一致性完全跟踪.最后,通过仿真算例进一步验证所提算法的有效性.     

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。     

自适应Terminal滑模控制及其在UASV 再入中的应用

针对传统模糊干扰观测器在观测误差较小时学习速度缓慢的缺点，提出一种新型快速模糊干扰观测器；然后设计了基于快速模糊干扰观测器的自适应Terminal滑模控制方案，严格证明了跟踪误差和观测误差均在有限时间内收敛到非常小的区域；最后，仿真结果表明了所设计干扰观测器的优越性和闭环控制方案的有效性．     

一类严反馈块控非线性MIMO系统的动态面控制

针对一类具有未知不确定性的严反馈块控非线性多输入多输出(MIMO)系统,提出一种满足L∞跟踪性能的动态面鲁棒控制律设计方法.通过非线性阻尼项对未知不确定性进行补偿,动态面控制方法消除了反向递推(backstepping)设计方法中由于对虚拟控制反复求导而导致的复杂性问题.基于李亚普诺夫稳定性定理证明了闭环系统的所有信号半全局一致最终有界,通过适当选择设计参数及初始化动态面变量,跟踪误差可收敛到原点的一个任意小邻域内,且可以保证系统各个输出跟踪误差的L∞性能.数值仿真验证了方法的有效性.     

一类严格反馈系统变比例增益精确微分补偿控制

针对包含不确定函数和未知外部扰动的一类严格反馈型非线性系统,提出基于精确扰动观测器的变比例增益自适应模糊控制器.系统中的未知不确定函数由模糊逻辑系统在线逼近,同时将模糊逻辑系统的逼近误差和未知外部扰动定义为总扰动,利用精确扰动观测器进行精确微分补偿控制. 将非线性函数应用于设计可调节的输出反馈增益,有效消除系统的稳态误差,使得系统跟踪误差可以控制在零的任意小邻域内.最后,通过Lyapunov定理证明闭环系统中所有信号均是有界的.数值仿真表明了所提出方案的有效性.     

基于非线性干扰观测器的一类欠驱动系统跟踪控制

针对一类欠驱动系统的跟踪控制问题,提出一种基于非线性干扰观测器的控制策略.首先给出一种基于跟踪误差的输出函数,通过等式变形和Butterworth低通滤波器解决未知控制方向问题;其次,引入一种新型非线性干扰观测器,对系统未知模型进行补偿,使控制器的设计无需知道系统的结构和参数;再次,通过对系统的内部动态和外部动态的分析,证明闭环系统的输出收敛于原点,跟踪误差信号一致最终有界;最后,将该方法应用于小车倒立摆模型,仿真结果表明了所提出方法的有效性.     

有向图下非线性无人机群自适应合围控制

本文研究了有向图下具有非线性和干扰的无人机群的分布式合围控制问题. 其中仅部分跟随者是领导者的邻 居, 对于每一个跟随者, 至少存在一条从领导者到这个跟随者有向路径. 文中假设无人机的空气动力学特性是非线性不 确定的, 并且领导者的输出是时变的. 结合反推设计方法提出了仅利用邻居信息的分布式合围控制方法, 使得跟随者的 状态收敛于领导者状态所张成的凸包里. 利用神经网络函数逼近技术补偿无人机系统中的非线性不确定项, 通过李雅普 诺夫稳定性理论证明了合围误差可以以任意收敛速度收敛到原点任意小的邻域. 最后通过仿真结果验证了控制协议的 有效性.     

非线性多智能体磁滞系统的分布式输出反馈渐近一致控制

针对一类具有磁滞输入且状态未知的非线性多智能体系统, 本文提出了一种基于领导者–跟随者的分布式 输出反馈渐近一致自适应控制方案. 首先, 构造了具有动态高增益的K-滤波器以估计多智能体系统的未知状态. 然 后, 采用一种新型的动态面控制策略设计控制器. 不同于传统动态面控制策略所采用的一阶低通滤波器, 本文设计 了含正时变积分函数的非线性滤波器, 该滤波器不仅能解决“微分爆炸”问题、降低计算负担, 而且能补偿传统动态 面的边界层误差, 使跟踪误差收敛到零. 理论分析表明: 该控制方案能有效地消除未知磁滞的影响, 确保整个闭环系 统的稳定性, 并使跟踪误差达到渐近收敛的目标. 最后, 通过仿真对所提出控制方案的有效性进行了分析和验证.     

基于模糊逻辑的分散自适应变结构控制

研究了一类具有未知常数控制增益的耦合大系统的分散自适应模糊控制问题，提出了能够利用专家的语言信息和数字信息的适应模糊变结构控制器的设计方案。通过理论分析，证明了分散自适应模糊控制系统是全局稳定的，跟踪误差可收敛到零的一个邻域内。     

Homogeneous finite-time consensus tracking of high-order-integrator agents by parametric approach

Finite-time consensus tracking of high-order-integrator multi-agent systems (MAS) is investigated under an undirected topology. When the leader's control input is known to all followers, the homogeneous finite-time control for a high-order integrator is extended to a distributed protocol for the corresponding MAS, and a set of control gains are found by the parametric approach for robust control, such that the multiple agents are simultaneously stabilised in finite time by keeping all characteristic polynomials Hurwitz. When it is only known to the leader's neighbouring followers, a distributed observer is presented for each follower to estimate it in finite time, and the combined observer-based protocol achieves finite-time consensus tracking in a fully distributed fashion. Simulation examples illustrate the effectiveness of the proposed scheme.     

多航天器系统分布式固定时间输出反馈姿态协同跟踪控制

针对角速度信息不可测条件下的多航天器系统姿态协同跟踪控制问题,提出一种基于积分滑模的分布式固定时间姿态协同跟踪控制方法.基于bi-limit齐次性理论设计一种新型的固定时间收敛的积分滑模面.为估计出不可测的航天器信息,提出一种固定时间观测器,同时为减少航天器间的通信信息流,引入滑模估计器估计领航者的姿态信息.结合固定时间观测器、滑模估计器和积分滑模方法设计分布式固定时间输出反馈姿态协同跟踪控制器,保证各跟随航天器在固定时间内实现对领航者姿态的协同跟踪.通过代数图论和Lyapunov理论证明闭环系统的固定时间稳定性.通过对比仿真结果验证所提出的控制方法的有效性和优越性.     

Global finite-time attitude consensus tracking control for a group of rigid spacecraft

The problem of finite-time attitude consensus for multiple rigid spacecraft with a leader–follower architecture is investigated in this paper. To achieve the finite-time attitude consensus, at the first step, a distributed finite-time convergent observer is proposed for each follower to estimate the leader's attitude in a finite time. Then based on the terminal sliding mode control method, a new finite-time attitude tracking controller is designed such that the leader's attitude can be tracked in a finite time. Finally, a finite-time observer-based distributed control strategy is proposed. It is shown that the attitude consensus can be achieved in a finite time under the proposed controller. Simulation results are given to show the effectiveness of the proposed method.     

Distributed event‐triggered tracking control with a dynamic leader for multiple Euler‐Lagrange systems under directed networks

The distributed tracking control for multiple Euler‐Lagrange systems with a dynamic leader is investigated in this article via the event‐triggered approach. Only a portion of followers have access to the leader, and the communication topology among all agents is directed that contains a directed spanning tree rooted at the leader. The case that the leader's generalized velocity is constant is first considered, and a distributed event‐based control law is developed by using a velocity estimator. When the leader's generalized velocity is time‐varying, novel distributed continuous estimators are proposed to avoid the undesirable chattering effect while guaranteeing that the estimate errors converge to zeros. With the designed distributed estimators, another distributed event‐based control protocol is provided. Controller update frequency and resource consumption in our work can be reduced by applying the aforementioned two distributed control laws, and the tracking errors can converge to zeros. In addition, it is rigorously proved that no agent exhibits Zeno behavior. Finally, the effectiveness of the proposed distributed event‐based control laws is elucidated by a number of simulation examples.     

Consensus-based distributed cooperative learning control for a group of discrete-time nonlinear multi-agent systems using neural networks

This paper focuses on the cooperative learning capability of radial basis function neural networks in adaptive neural controllers for a group of uncertain discrete-time nonlinear systems where system structures are identical but reference signals are different. By constructing an interconnection topology among learning laws of NN weights in order to share their learned knowledge on-line, a novel adaptive NN control scheme, called distributed cooperative learning control scheme, is proposed. It is guaranteed that if the interconnection topology is undirected and connected, all closed-loop signals are uniform ultimate bounded and tracking errors of all systems can converge to a small neighborhood around the origin. Moreover, it is proved that all estimated NN weights converge to a small neighborhood of their common optimal value along the union of all state trajectories, which means that the estimated NN weights reach consensus with a small consensus error. Thus, all learned NN models have the better generalization capability than ones obtained by the deterministic learning method. The learned knowledge is also adopted to control a class of uncertain systems with the same structure but different reference signals. Finally, a simulation example is provided to verify the effectiveness and advantages of the distributed cooperative learning control scheme developed in this paper.     

Decentralized adaptive neural control of nonlinear interconnected large-scale systems with unknown time delays and input saturation

In this paper, a novel decentralized adaptive neural control scheme is proposed for a class of interconnected large-scale uncertain nonlinear time-delay systems with input saturation. RBF neural networks (NNs) are used to tackle unknown nonlinear functions, then the decentralized adaptive NN tracking controller is constructed by combining Lyapunov–Krasovskii functions and the dynamic surface control (DSC) technique along with the minimal-learning-parameters (MLP) algorithm. The stability analysis subject to the effect of input saturation constrains are conducted with the help of an auxiliary design system based on the Lyapunov–Krasovskii method. The proposed controller guarantees uniform ultimate boundedness (UUB) of all the signals in the closed-loop large-scale system, while the tracking errors converge to a small neighborhood of the origin. An advantage of the proposed control scheme lies in that the number of adaptive parameters for each subsystem is reduced to one, and three problems of “computational explosion”, “dimension curse” and “controller singularity” are solved, respectively. Finally, a numerical simulation is presented to demonstrate the effectiveness and performance of the proposed scheme.     

Distributed fixed‐time attitude coordination control for multiple rigid spacecraft

In this paper, the fixed‐time attitude coordination control for multiple rigid spacecraft under an undirected communication graph is investigated. By using the backstepping technique, the distributed fixed‐time observer, and the method of “adding a power integrator,” a distributed fixed‐time attitude coordination control law is designed for a group of spacecraft. The proposed control scheme is nonsingular and can guarantee a group of rigid spacecraft simultaneously tracking a common desired attitude within fixed time even when the time‐varying reference attitude is available only to a subset of the group members. Rigorous analysis is provided to show that the attitude consensus tracking errors can converge to the origin in finite time which is bounded by a fixed constant independent of initial conditions. Numerical simulations are carried out to demonstrate the effectiveness of the proposed control law.     

状态观测的未知死区非线性系统的自适应神经网络跟踪控制

研究一类包含不确定项和未知死区特性的严格反馈系统跟踪控制问题.首先,设计状态观测器估计不可测量的系统状态;然后,利用RBF神经网络逼近未知的系统动态;最后,基于Backstepping技术构造自适应神经网络输出反馈控制器,并减少更新参数以减轻运算负荷.所提出的控制器可以保证闭环系统中所有信号半全局最终一致有界,跟踪误差能收敛到零值小的领域内.两个仿真例子进一步验证了所提出方法的有效性.     

Adaptive neural control of nonlinear MIMO systems with unknown time delays

In this paper, a novel adaptive NN control scheme is proposed for a class of uncertain multi-input and multi-output (MIMO) nonlinear time-delay systems. RBF NNs are used to tackle unknown nonlinear functions, then the adaptive NN tracking controller is constructed by combining Lyapunov-Krasovskii functionals and the dynamic surface control (DSC) technique along with the minimal-learning-parameters (MLP) algorithm. The proposed controller guarantees uniform ultimate boundedness (UUB) of all the signals in the closed-loop system, while the tracking error converges to a small neighborhood of the origin. An advantage of the proposed control scheme lies in that the number of adaptive parameters for each subsystem is reduced to one, triple problems of “explosion of complexity”, “curse of dimension” and “controller singularity” are solved, respectively. Finally, a numerical simulation is presented to demonstrate the effectiveness and performance of the proposed scheme.     

Adaptive Decentralized NN Control of Nonlinear Interconnected Time‐Delay Systems with Input Saturation

In this paper, a novel decentralized adaptive neural control scheme is proposed for a class of interconnected large‐scale uncertain nonlinear time‐delay systems with input saturation. Radial basis function (RBF) neural networks (NNs) are used to tackle unknown nonlinear functions. Then, the decentralized adaptive NN tracking controller is constructed by combining Lyapunov–Krasovskii functions and the dynamic surface control (DSC) technique, along with the minimal‐learning‐parameters (MLP) algorithm. The stability analysis subject to the effect of input saturation constraints are conducted with the help of an auxiliary design system based on the Lyapunov–Krasovskii method. The proposed controller guarantees uniform ultimate boundedness (UUB) of all of the signals in the closed‐loop large‐scale system, while the tracking errors converge to a small neighborhood around the origin. An advantage of the proposed control scheme lies in the number of adaptive parameters of the whole system being reduced to one and in the solution of the three problems of “computational explosion,” “dimension curse,” and “controller singularity”. Finally, simulation results along with comparisons are presented to demonstrate the advantages, effectiveness, and performance of the proposed scheme.