Adaptive neural network tracking control for a class of unknown nonlinear time-delay systems

1 .INTRODUCTIONNeural network (NN) control has made great pro-gress in past decades[1 ~4]. In Ref .[1] , adaptivebounding design technique was applied to adaptiveneural control for a class of strict-feedback nonlin-ear systems . The requirement of a known boundon the network reconstruction error was removed.By introducing an integral Lyapunov function,anadaptive NN control approach was proposed forunknown strict-feedback nonlinear systems[2],where the controller singularity problem was …     

一类非线性时滞系统的可靠控制设计

针对一类非线性时滞系统 ,首先不考虑故障情况 ,设计了一种控制器 ,使得系统在控制器的作用下 ,对于任意的初始状态都能控制到原点。然后针对可能发生的故障 ,利用神经网络的在线建模能力 ,以其输出作为原有控制律的附加控制信号来补偿故障对系统的影响 ,从而构成了一种可靠控制器 ,实现了该类系统的可靠控制。     

Adaptive NN stabilization for stochastic systems with discrete and distributed time-varying delays

A new adaptive neural network(NN) output-feedback stabilization controller is investigated for a class of uncertain stochastic nonlinear strict-feedback systems with discrete and distributed time-varying delays and unknown nonlinear functions in both drift and diffusion terms.First,an extensional stability notion and the related criterion are introduced.Then,a nonlinear observer to estimate the unmeasurable states is designed,and a systematic backstepping procedure to design an adaptive NN output-feedback controller is proposed such that the closed-loop system is stable in probability.The effectiveness of the proposed control scheme is demonstrated via a numerical example.     

Output-feedback adaptive stochastic nonlinear stabilization using neural networks

For the first time, an adaptive backstepping neural network control approach is extended to a class of stochastic nonlinear output-feedback systems. Different from the existing results, the nonlinear terms are assumed to be completely unknown and only a neural network is employed to compensate for all unknown nonlinear functions so that the controller design is more simplified. Based on stochastic LaSalle theorem, the resulted closed-loop system is proved to be globally asymptotically stable in probability....     

Simultaneous quadratic performance stabilization for linear time-delay systems

1.INTRODUCTION The simultaneous stabilization problem is concerned with the design of a single feedback controller P stabilizing every member of a collection of systems that is M 1 , M 2, … , Mr. In this case, the controller P is called the simultaneous stabilization controller. The motivation behind the simultaneous stabilization problem stems from the stability requirement of a system operating in different modes. For example, an industrial plant may be subject to different modes owi…     

超混沌时滞神经网络的同步及其仿真

时滞神经网络是解空间为无穷维的非线性动力系统,这样的系统可产生具有多个正的Lyapunov指数的超混沌行为。复杂的时间序列使得这类系统特别适用于保密通信中。针对一类时滞超混沌神经网络系统,利用单向耦合同步概念,设计了一个非线性控制器,并通过Lyapunov-Krasovskii方法和Halanay不等式引理分别给出了系统渐进同步和指数同步的与时滞无关的充分条件。通过引入控制矩阵正定的条件,避免了繁复的LMI计算,简化了控制过程。数值仿真验证了该方法的有效性,数值计算给出了指数同步率和控制参数的关系。     

基于不确定时滞模型的鲁棒AQM控制器设计

针对网络环境的不确定性和网络参数的时变性,采用不确定时滞系统模型对网络拥塞控制问题进行了研究。给出了利用线性矩阵不等式方法判定主动队列管理(active queue management,AQM)系统稳定的充分条件,以及基于求解非线性矩阵不等式的AQM控制器参数设计方法,设计了基于线性矩阵不等式的含时滞静态输出反馈控制器。仿真结果表明,利用该方法所得到的控制器是有效的,在变化的网络环境下,其动态响应更快,稳态波动更小。     

网络环境下的不确定非线性系统远程智能控制

针对一类网络环境下的不确定非线性动态系统,提出了一种基于神经网络非线性补偿和线性反馈控制相结合的智能远程控制方法。该方法通过构造神经网络在线估计器学习局部子系统的非线性特性,从而将局部子系统线性化,神经网络权重的在线学习没有持续激励的要求,且可保证闭环误差系统一致最终有界稳定。最后对线性化的闭环时延控制系统,远程控制器采用基于李亚普诺夫(Lyapunov)稳定性理论的线性状态反馈控制,从而保证了整个闭环网络控制系统的稳定性。     

Adaptive integral dynamic surface control based on fully tuned radial basis function neural network

An adaptive integral dynamic surface control approach based on fully tuned radial basis function neural network (FTRBFNN) is presented for a general class of strict-feedback nonlinear systems, which may possess a wide class of uncertainties that are not linearly parameterized and do not have any prior knowledge of the bounding functions. FTRBFNN is employed to approximate the uncertainty online, and a systematic framework for adaptive controller design is given by dynamic surface control. The control algorithm has two outstanding features, namely, the neural network regulates the weights, width and center of Gaussian function simultaneously, which ensures the control system has perfect ability of restraining different unknown uncertainties and the integral term of tracking error introduced in the control law can eliminate the static error of the closed loop system effectively. As a result, high control precision can be achieved. All signals in the closed loop system can be guaranteed bounded by Lyapunov approach. Finally, simulation results demonstrate the validity of the control approach.     

电加热锅炉系统神经网络PID解耦控制器

以多输入多输出系统电加热锅炉为被控对象 ,基于神经网络 PID控制 ,提出了一种可用于带有耦合时延的多输入多输出系统的比例、积分和微分参数自整定的多输入多输出神经网络 PID解耦控制器 ,可以实现多变量系统的解耦 ,定值跟踪控制 ,并使系统具有很好的动态及稳态性能.     

Neural network based adaptive sliding mode control of uncertain nonlinear systems

The purpose of this paper is the design of neural network-based adaptive sliding mode controller for uncertain unknown nonlinear systems. A special architecture adaptive neural network, with hyperbolic tangent activation functions, is used to emulate the equivalent and switching control terms of the classic sliding mode control (SMC). Lyapunov stability theory is used to guarantee a uniform ultimate boundedness property for the tracking error, as well as of all other signals in the closed loop. In addition to keeping the stability and robustness properties of the SMC, the neural network-based adaptive sliding mode controller exhibits perfect rejection of faults arising during the system operating. Simulation studies are used to illustrate and clarify the theoretical results.     

离散模糊时滞系统的鲁棒LQ/H∞非脆弱控制

针对一类由T-S模糊模型描述的不确定离散非线性时滞系统,讨论其在控制器存在可加性摄动情形下的鲁棒LQ/H_∞非脆弱控制问题。通过构造适当的Lyapunov函数,给出以线性矩阵不等式形式表示的系统时滞依赖稳定的充分条件以及相应的鲁棒LQ/H_∞非脆弱控制器设计方法。数值仿真表明,所构造的控制器不仅能够保证闭环模糊时滞系统的鲁棒渐近稳定性,还能使系统达到一定的H_∞干扰抑制水平。     

非线性时滞系统自适应输出反馈跟踪控制

针对一类参数化非线性时滞输出反馈系统,提出了一种自适应输出跟踪控制器的设计方案。引入时滞滤波器估计未知系统状态,采用backstepping技术设计控制律和参数自适应律,运用占优方法处理非线性时滞项,从而放宽了对非线性时滞函数的要求,实现了对给定目标轨线的全局渐近跟踪,同时保证闭环系统所有信号一致有界。基于Lyapunov-Krasoviskii泛函方法证明了整个闭环系统的稳定性。实例仿真说明了该方案的可行性。     

基于RCMAC干扰观测器的高超声速飞行控制

利用自回归小脑模型神经网络(recurrent cerebella model neural network, RCMAC)良好的非线性逼近能力和自学习能力,结合反馈线性化和反演控制方法,提出了一种自适应非线性控制策略,用于高速再入飞行器控制系统的设计。该方案将RCMAC干扰观测器(recurrent cerebella disturbance observer, RCDO)用于估计系统模型的不确定项,同时采用反演控制方式设计伪线性控制项,并利用符号函数逼近误差的上界,根据Lyapunov稳定性理论设计了权值更新规则,保证闭环系统信号有界。高速再入飞行器的六自由度仿真结果验证了方法的有效性和鲁棒性。     

基于级联卷积神经网络的港口多方向舰船检测与分类

港口舰船目标自动检测的定位和类型分类是一个重要而具有挑战性的问题。针对高分辨率光学遥感影像中多方向性排列密集的近岸舰船目标定位和识别困难的问题,提出基于级联区域卷积神经网络和手工提取特征相结合的近岸舰船检测识别框架。首先,使用级联的区域卷积神经网络对舰船位置进行粗定位并对类别进行估计，得到一系列粗定位的垂直预测框。然后,设计一个可以准确定位舰船的斜框旋转回归器,其将第一阶段所得粗定位垂直矩形框转变为带方向的斜矩形框。最后,使用非极大值抑制的方法去除冗余的预测框。实验采用谷歌地球上采集的数据集进行训练和预测,实验结果表明所提算法在精准率和召回率上均具有较大优势。     

一类严格反馈时滞系统的自适应输出反馈控制

针对一类单输入单输出的严格反馈时滞系统研究跟踪控制问题。该控制系统包含不确定项、输出约束、未知死区特性和未知时间延迟。首先,设计一个状态观测器来估计无法测量的系统状态;其次,利用径向基函数(radial basis function,RBF)神经网络去逼近未知的系统内部动态;同时,利用障碍李雅普诺夫(Lyapunov)函数确保输出约束及Lyapunov-Krasovskii方法消除时滞项对系统的影响;最后,基于Lyapunov稳定性理论,构造一个鲁棒自适应神经网络输出反馈控制器,并且克服了过参数问题。结果显示,设计的神经网络输出反馈控制器可以保证闭环系统中的所有信号都是半全局最终一致有界的,跟踪误差能收敛到零值小的领域内。文中通过两个例子进一步验证了提出方法的有效性。     

离散模糊时滞系统的鲁棒LQ/H_∞非脆弱控制

针对一类由T-S模糊模型描述的不确定离散非线性时滞系统,讨论其在控制器存在可加性摄动情形下的鲁棒LQ/H_∞非脆弱控制问题.通过构造适当的Lyapunov函数,给出以线性矩阵不等式形式表示的系统时滞依赖稳定的充分条件以及相应的鲁棒LQ/H_∞非脆弱控制器设计方法.数值仿真表明,所构造的控制器不仅能够保证闭环模糊时滞系统的鲁棒渐近稳定性,还能使系统达到一定的H_∞干扰抑制水平.     

基于级联卷积神经网络的港口多方向舰船检测与分类

港口舰船目标自动检测的定位和类型分类是一个重要而具有挑战性的问题。针对高分辨率光学遥感影像中多方向性排列密集的近岸舰船目标定位和识别困难的问题,提出基于级联区域卷积神经网络和手工提取特征相结合的近岸舰船检测识别框架。首先,使用级联的区域卷积神经网络对舰船位置进行粗定位并对类别进行估计，得到一系列粗定位的垂直预测框。然后,设计一个可以准确定位舰船的斜框旋转回归器,其将第一阶段所得粗定位垂直矩形框转变为带方向的斜矩形框。最后,使用非极大值抑制的方法去除冗余的预测框。实验采用谷歌地球上采集的数据集进行训练和预测,实验结果表明所提算法在精准率和召回率上均具有较大优势。     

不确定离散广义时滞系统的静态输出反馈控制

针对一类不确定离散时滞广义系统,给出了静态输出反馈控制器的设计方法.首先基于标称离散广义时滞系统的稳定条件,以受限线性矩阵不等式形式,得到闭环离散广义时滞系统正则、因果且渐近稳定的充分条件,同时利用受限矩阵不等式的可行解给出静态输出反馈控制器的设计方法;然后采用矩阵的正交补,把求受限线性矩阵不等式的可行解问题转化为求严格线性矩阵不等式的可行解;最后的数值实例说明了所给方法的有效性和正确性.