Event-triggered Sliding Mode Fault-tolerant Consensus for a Class of Leader-follower Multi-agent Systems

This paper concerns with the robust sliding mode fault-tolerant consensus problem for a class of undertain second-order leader-follower multi-agent systems based on event-triggering strategies. First, a sliding mode fault-tolerant controller which uses the bound information of actuator failure rate and the event-triggering threshold is designed to ensure the robust consensus of the multi-agent systems, and the range of the sliding mode band is also shown. Second, by constructing an equivalent relationship, the upper bound of robust consensus error is also given. Third, the minimum event-triggered execution time for the second-order leader-follower multi-agent systems is calculated. Finally, the simulation results verify the effectiveness of the proposed event-triggered sliding mode fault-tolerant consensus algorithm.

     

Optimal Consensus Control for Heterogeneous Nonlinear Multiagent Systems with Partially Unknown Dynamics

International Journal of Control, Automation and Systems - This paper focuses on an optimal consensus problem for heterogeneous discrete-time nonlinear multi-agent systems (MASs) with partially...     

基于RBF神经网络的减摇鳍自适应滑模控制

为减轻船舶在大风浪中剧烈的横摇,减摇鳍是目前应用最广泛的减摇装置.针时船舶减摇鳍系统的非线性和不确定性,在系统不确定性函数结构未知的情况下,提出一种RBF神经网络自适应滑模控制方法.采用RBF神经网络逼近系统不确定动态,并设计权值的自适应律,结合滑模控制增强系统的鲁棒性.在不同有义波高和不同浪向角下,建立随机海浪的干扰模型,应用simulink对系统进行仿真.仿真结果表明,该控制策略在各种海况下,均具有良好的减摇效果和较强的鲁棒性.     

Adaptive Backstepping Sliding Mode Control of Tractor-trailer System with Input Delay Based on RBF Neural Network

In this paper, an adaptive sliding mode neural network(NN) control method is investigated for input delay tractor-trailer system with two degrees of freedom. An uncertain camera-object kinematic tracking error model of a tractor car with n trailers with input delay is proposed. Radial basis function neural networks(RBFNNs) are applied to approximate the unknown functions in the error model. A sliding mode surface with variable structure control is designed by using backstepping method. Then, an adaptive NN sliding mode control method is thus obtained by combining Lyapunov-Krasovskii functionals. The controller realizes the global asymptotic trajectories tracking of the kinematics system. The stability of the closed-loop system is strictly proved by the Lyapunov theory. Matlab simulation results demonstrate the feasibility of the proposed method.

     

基于动态K均值的RBF神经网络日降水预报模型

针对优化径向基函数神经网络的各参数问题,提出一种动态K均值混合优化RBF神经网络并应用于广西降水数据进行建立预报模型,该模型与传统的K均值RBF模型和同期的T213降水预报进行对比,结果表明。该模型建立的5月3个区域的逐日降水预报,预测的精确度明显高于同期的T213降水预报。     

基于RBF神经网络的非线性控制系统

针对时滞、非线性多变量耦合系统控制中串级控制存在滞后，并且PI控制参数初始设置困难，很多时候只能手动控制的缺陷，本文采用内模控制方隶．通过RBF神经网络训练获得内部模型，同时利用最小二乘模型降解，简化解耦矩阵的求取，实现了两输入两输出系统的解耦控制。仿真结果表明．该方案可以消除变量间的耦合，并且解决了时滞问题、降低过程超调量，使得控制系统更加平稳，改善了过程控制的品质。同时．当对象特性发生一定改变时．系统具备良好的鲁棒性能。     

Distributed Adaptive Neural Consensus Control for Stochastic Nonlinear Multiagent Systems with Whole State Delays and Multiple Constraints

International Journal of Control, Automation and Systems - This paper presents a distributed adaptive neural tracking consensus control strategy for a class of stochastic nonlinear multiagent...     

An Adaptive Neural Sliding Mode Controller for MIMO Systems

Here, a novel adaptive neural sliding mode controller (ANSMC) is proposed to handle the coupling and dynamic uncertainty of MIMO systems. The structure of this model-free new controller is based on a radial basis function neural network (RBFNN) which is derived from Lyapunov stability theory and relaxing Kalman–Yacubovich lemma to monitor the system for tracking a user-defined reference model. The weights of RBFNN can be initialized at zero, then, a novel online tuning algorithm is developed based on Lyapunov stability theory. A boundary layer function is introduced into the updating law to cover the parameter errors and modeling errors, and to guarantee the state errors converge into a specified error bound. An e-modification is added into the updating law to release the assumption of persistent excitation and obtain the appropriate values of the connecting weights of a RBFNN. To evaluate the control performance of the proposed controller, a two-link robot system is chosen as the simulation case. The numerical simulations results show that this novel controller has very good tracking accuracy, stability and robustness.     

Adaptive Continuous Sliding Mode Control for Fractional-order Systems with Uncertainties and Unknown Control Gains

International Journal of Control, Automation and Systems - The main purpose of this paper is to design the continuous sliding mode control, which resolves the stability problem of fractional-order...     

基于RBF神经网络模型的信用评估

采用神经网络模型研究信用风险评估问题,鉴于RBF神经网络计算量小、学习速度快,不易陷入局部极小而且具有很强的分类能力等优点,提出基于RBF神经网络的信用评估模型,通过试验该模型展现了良好的性能.     

一种基于RBF神经网络的预测器模型及其研究

非线性复杂系统的预测控制是一种高性能的控制方法,其关键在于非线性预测器模型的实现。论文从径向基函数(RBF)神经网络原理分析出发,探讨了一种用于神经网络的预测模型设计方法,并将此方法用于实际非线性系统的预测控制。结果表明,基于RBF的神经网络预测模型可快速准确地完成对非线性动态过程的预测描述,因而可以在非线性系统的预测控制中得到良好的应用。     

Event-triggering Consensus for Second-order Leader-following Multiagent Systems with Nonlinear Time-delayed Dynamics

International Journal of Control, Automation and Systems - This work focuses on the leader-following consensus problem for networks of dynamic agents, each of which has second-order nonlinear...     

Sliding Mode Control for Robust Consensus of General Linear Uncertain Multi-agent Systems

International Journal of Control, Automation and Systems - This paper investigates the robust consensus problem for general high-order linear multi-agent systems with external disturbances and...     

RBF神经网络在转炉提钒预报中的应用

该文用RBF神经网络建立了转炉提钒冷却剂预报模型。RBF网络的中心的选取采用了可以在线学习的最近邻聚类算法。为了进一步优化网络中心，提出了基于密度排名的最近邻聚类算法。该算法聚类前先将样本按其在样本空间的密度进行了排序，聚类过程始于样本空间最密集处。实践证明，该算法应用于提钒冷却剂预报模型的建立是合理的，可行的。     

Synchronization of Non-Identical Unknown Chaotic Delayed Neural Networks Based on Adaptive Sliding Mode Control

The synchronization problem is studied in this paper for non-identical chaotic neural networks with time delays and fully unknown parameters, where the mismatched parameters, activation functions and neural network architectures are taken into account. To overcome the difficulty that complete synchronization of non-identical chaotic neural networks cannot be achieved only by utilizing output feedback control, we design an adaptive sliding mode controller to realize the synchronization. Our synchronization criteria are easily verified and do not need to solve any linear matrix inequality. These results generalize a few previous known results and remove some restrictions on the parameters, activation functions and neural network architectures. This paper also presents an illustrative example and uses simulated results of this example to show the feasibility and effectiveness of the proposed scheme.     

基于径向基函数神经网络的非线性模型辨识

从径向基函数（RBF）神经网络原理分析出发,提出了一种基于RBF神经网络学习算法,用于对非线性对象模型的拟合与辩识,并将此方法用于实际非线性模型的学习与辩识。结果表明,基于RBF的神经网络可快速完成对样本的学习与拟合,对具有连续特性的线性与非线性模型,具有快速实时的学习速度和优良的学习性能。     

Fixed-time Consensus Tracking for Second-order Leader-follower Multi-agent Systems with Nonlinear Dynamics Under Directed Topology

In this paper, we address the fixed-time consensus tracking problem of second-order leader-follower multi-agent systems with nonlinear dynamics under directed topology. The consensus tracking algorithm consists of distributed observer and observer-based decentralized controller. The fixed-time distributed observer guarantees that each follower estimates the leader’s state under directed topology within a fixed time, where the upper bound of convergence time is independent on the initial conditions. The fixed-time decentralized controller makes each follower converge to the leader’s state in fixed time via tracking the distributed observer’s state and overcome the nonlinear dynamics without adding linear control terms. Finally, the numerical example is provided to illustrate the effectiveness of the results.

     

RBF网络和RBF核支持向量机比较研究

RBF网络是模式识别中应用最为广泛的一和神经网络.RBF核函数型支持向量机是一种性能卓越的新型学习机.将这两种学习机进行对比分析,以期在实际应用中做出更好的选择.首先,在理论上分析了这两种学习机在分类原理上的异同.接着,将它们应用于人脸识别,利用ORL人脸图像数据库进行了仿真实验,对比分析它们各自的识别率和泛化能力等性能指标.最后,提出了在应用这两种学习机进行模式识别时应注意的方面.实验结果表明,按照本文提出的两种训练模式,RBF型支持向量机在识别准确率上比RBF网络高出2%到4%.这说明RBF型支持向量机的性能要优于RBF网络.但是RBF网络易于实现,在样本数日足够多的情况下也不不失为一种好的算法.     

终端滑模约束的时滞系统模型预测控制

终端约束区域和终端代价项在模型预测控制中起着关键的作用,针对输入受限的时滞系统,提出了终端滑模约束的模型预测控制．将满足输入约束的滑模面作为终端约束区域,使得终端约束区域扩大,有效缩短预测时域,减少计算量,有利于在线应用．最后通过仿真验证了所提方法的有效性．     

基于RBF神经网络的临近空间气温预测模型

针对大气系统,特别是大气温度系统的复杂非线性动力学系统特性,对现有的临近空间(Near Space)的温度资料进行分析,并利用RBF神经网络建立了临近空间温度场预测模型。根据北纬10度到北纬50度、海拔20 km到80 km之间的临近空间的气温观测数据,利用遗传算法寻优RBF神经网络的隐层节点中心值,最后通过RBF神经网络预测临近空间的大气气温。仿真结果和实际的温度数据对比分析表明,使用该方法建模取得了比较好的效果。