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.     

Leader-follower Consensus of Upper-triangular Nonlinear Multi-agent Systems

This paper is concerned with the leader-follower consensus problem by using both state and output feedback for a class of nonlinear multi-agent systems. The agents considered here are all identical upper-triangular nonlinear systems which satisfy the Lipschitz growth condition. First, it is shown that the leader-follower consensus problem is equivalent to the control design problem of a high-dimensional multi-variable system. Second, by introducing an appropriate state transformation, the control design problem can be converted into the problem of finding a constant parameter, which can be obtained by solving the Lyapunov equation and estimating the nonlinear terms of the given system. At last, an example is given to verify effectiveness of the proposed consensus algorithms.      

基于RBF神经网络-滑模观测器的PMSM无传感器矢量控制

为了解决传统滑模观测器方法应用在永磁同步电机无传感器矢量控制时所产生的抖振问题,使用RBF神经网络动态调节观测器的切换增益,即使其输入为传统滑模估计方案中的电流估计误差,输出为滑模增益;同时为了简化系统结构、提高方案可行性,将RBF神经网络设计为单输入单输出的结构,并将网络的学习和工作过程融合,使其在自身网络参数的不断优化中实时输出滑模增益,以增强系统鲁棒性。最后通过Matlab/Simulink软件对该系统进行建模仿真,并将该方法与传统滑模观测器方法进行对比。实验结果表明,该方案能够为矢量控制提供更加准确的转子位置及速度信息,提高了整个电机控制系统的稳定性。     

带有通信时滞的二阶多智能体系统一致问题

针对具有双向等时延的二阶无向通信拓扑系统,采用带有通信时滞的线性一致控制率协议,分析了使系统稳定的条件。由于系统的阶次较高,直接对其特征方程进行分析是比较困难的,提出了一种新的分析方法,把系统的特征方程分解为多个子系统的乘积,然后利用CTCR方法,求得每个子系统对应的时滞最大值,比较后得出使系统达到一致稳定的最大时滞,作出了控制率边界曲线图并标出了稳定区域。结果表明,在有向生成树的情况下,当时滞小于决策值时,系统能达到稳定。最后,数值仿真验证了所得结果的有效性。     

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。     

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.

     

一种使用RBF 网络辨识不定性上界的滑模控制器

研究使用RBF网络辨识满足匹配条件外干扰的未知上界,设计控制系统的滑模控制器问题。基于Lyapunov稳定性理论更新RBF网络的参数,证明了闭环系统的全局渐近稳定性。仿真结果表明了设计方法的有效性。     

Quantization Effect on Sliding Mode Control of Uncertain Dynamical Systems

This paper studies the quantization effect on dynamical behaviors of the sliding mode control system with matched uncertainties and disturbances. The sufficient condition for stability of the system is given. The performance of both the sliding mode and the system trajectories with quantized state feedback are discussed. We show that the sliding mode with quantization is piecewise constant and the trajectory chatters in the boundary layer of the designed sliding manifold with thickness O(μ), where μ is the quantization level. Simulation results are presented to show the effectiveness of the theoretical results.     

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。     

基于RBF神经网络滑模控制的车辆横向控制研究

针对车辆横向控制系统中滑模控制器存在的抖振现象对转向机械结构带来的损耗问题,提出了一种基于RBF神经网络的滑模控制算法。利用RBF神经网络较强的自学习能力实时在线调节滑模控制器的切换项增益参数,增强系统的抗干扰能力与动态性能。将车辆实际参数代入仿真数学模型中,在Simulink仿真环境中进行对比仿真实验,仿真结果表明:该控制算法跟踪性能好,能够有效降低滑模控制器的抖振,满足车辆横向控制要求。     

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...     

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...     

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.     

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...     

基于径向基神经网络的分数阶混沌系统控制

针对分数阶混沌系统的控制问题,提出了一种基于径向基函数（RBF）神经网络的控制方法．利用RBF 神经网络对混沌系统的非线性进行补偿,并且神经网络的权值可以通过调整律在线调整．在有参数干扰和外部扰动 的情况下,所设计的控制器仍能使得控制误差渐近收敛到零．以分数阶Liu 混沌系统为例施加控制,仿真结果验证 了该方法的有效性和鲁棒性．     

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...