一类MIMO非线性系统的自适应模糊输出反馈控制

针对一类MIMO非线性状态不可测系统,提出了一种稳定的基于观测器的自适应模糊控制方法.该方法不需要系统状态可测的条件,而是通过设计模糊观测器来估计系统的状态.证明了所提出的控制方法可保证闭环系统的稳定性和跟踪误差的收敛性.仿真结果进一步验证了该控制方法的实用性和有效性.     

非线性系统死区输入的自适应动态面控制

为实现对具有未知死区输入的非线性系统的跟踪控制,提出一种基于扩张状态观测器的自适应动态面控制算法。采用扩张状态观测器代替函数逼近器在线估计反演法每一步中的不确定函数,基于反演法技术构造自适应动态面输出反馈控制器,通过跟踪微分器来消除传统反演控制算法计算负荷大的问题,并给出稳定性证明。对所提出的控制方法进行仿真验证,结果表明该算法跟踪效果较好,鲁棒性和抗干扰能力较强。     

一类非线性系统的单尺度小波框架 网络自适应观测器

本文提出了一类MIMO非线性系统的小波网络自适应观测器设计方法,可对一类非线性未知或具有不确定性的非仿射系统进行状态估计.文中给出了小波网络参数的在线自适应调节律,从理论上分析了观测器增益矩阵的选择对状态估计误差的影响.根据Lyapunov理论证明了整个自适应观测器的稳定性,仿真试验表明了该方法的有效性.     

扩张状态观测器下的自适应输出反馈控制

针对一类不确定非线性系统,研究了一种结合反步法和自抗扰控制的新的自适应输出反馈控制方法.通过引入扩张状态观测器(ESO)对被控系统的未知状态进行实时估计,同时利用扩张状态观测器实现对系统中的不确定项在线逼近及补偿.通过非线性滤波器对反步法设计过程中的虚拟控制信号进行求导,避免了传统反步法设计控制中复杂性爆炸的问题,并由此设计了自适应输出反馈控制器.通过李雅普诺夫函数证明了这种控制方法的稳定性,验证了闭环系统中所有信号均是有界的.数值仿真算例进一步验证了该方法的有效性.     

单隐含层模糊递归小波神经网络的观测器设计

根据模糊神经网络在非线性函数逼近方面的特性和小波变换具有良好的时频两维信号的分析能力,建立了结合两者优点的单隐含层模糊递归小波神经网络(Single hidden Layer Fuzzy Recurrent Wavelet Neural Network,SLFRWNN),并分析了SLFRWNN的结构、激活函数形式及激活函数对网络性能的影响.在此基础上,提出了一种基于SLFRWNN的自适应观测器设计方法,并通过引入Lyapunov函数,证明了这种观测器设计方法的稳定性,进而给出该网络观测器的初始化和最佳训练算法;仿真结果表明SLFRWNN观测器能很好地观测系统的状态.     

考虑约束的卫星抗扰反步姿态控制

针对具有不确定性、外干扰及饱和约束的卫星非线性姿态跟踪问题, 将约束反步法与状态观测器相结合, 提出分块自适应抗扰反步控制器。卫星模型由修正罗德里格参数进行描述。利用带参数投影的非线性扩张状态观测器对时变的“总干扰”项进行在线估计补偿, 以提高反步控制器的鲁棒性。在设计反步控制器时, 引入指令滤波器和修正跟踪误差信号以施加系统状态和执行器的饱和限制, 同时较容易获得虚拟控制导数, 并且放宽了干扰估计律投影算子的投影集范围。Lyapunov理论证明了闭环系统在非线性阻尼的作用下输入-状态稳定。对比仿真表明, 与传统自适应反步法相比, 所提出的控制器具有更高的姿态跟踪性能和干扰估计精度。     

一种新型的扩张状态观测器

提出一种新型的扩张状态观测器。将作用于实际运行环境中的非线性系统的内外扰归结为“未知扰动”，利用输入输出数据对系统的状态变量及扩张的状态变量进行实时估计并补偿。仿真结果表明，这种新型的扩张状态观测器可以很好地消除系统的内外扰动影响，具有一定的实际应用价值。     

扩张状态观测器在非线性系统故障诊断中的应用

介绍了扩张状态观测器理论,提出了一种应用于非线性系统的基于扩张状态观测器的故障诊断新方法,给出了多阶系统的故障观测器的设计方法,最后通过仿真实验证明所提供的方法相对于传统方法具有以下优点:一是可以得到故障的近似函数,便于识别故障。二是具有优良的即时性和反应能力。     

具有外部扰动的非线性多智能体系统的自适应控制分析

阐述在未知扰动下含有未知量的非线性多智能体系统控制问题。提出了一种分布式设计,可实现在加权有向图拓扑下的多智能体系统一致性跟踪控制。每个智能体由有未知量的严格反馈非线性系统建模,并包含外部干扰。通过backstepping技术和神经网络的方法,在只需要自己和相邻智能体之间的相对状态信息的情况下,为每个从智能体构造自适应分布式控制器。设计的控制器和自适应控制率可保证领航者与所有跟随器之间的跟踪误差收敛到原点的一个小邻域。运用Radial Basis Function(RBF)神经网络用于逼近未知的非线性函数,并设计了一个非线性扰动观测器用于估计未知的外部扰动。采用Nussbaum函数来处理模型中未知符号的参数,仿真结果验证了所提方法的有效性。     

基于自适应状态观测器的混沌同步仿真研究

针对一类特定结构的含有未知参数的混沌系统,给出了一种基于状态观测器的自适应同步控制方法。通过一个带有控制器的非线性状态观测器实现混沌系统的渐进同步并对未知参数进行估计。该控制方法简单易行,且能通过自适应律自动调整以跟随参数的变化,具有较强的鲁棒性。理论分析和仿真实验都证明了该方法的有效性。     

基于IMM-SCKF-STF的机动目标跟踪算法

在处理非线性机动目标跟踪问题时,传统的非线性滤波估计算法跟踪误差大且容易引起滤波发散.针对上述问题,研究将强跟踪平方根容积卡尔曼滤波(SCKF-STF)和交互多模型(IMM)算法相结合,提出一种新型的交互多模型强跟踪平方根容积卡尔曼滤波(IMM-SCKF-STF)跟踪算法.该算法在SCKF基础上引入强跟踪渐消因子,使其不仅拥有应对机动目标状态突变的强跟踪能力,同时还具备交互多模型算法的优良机动目标跟踪性能.因此,新算法在机动目标跟踪方面将获得更高的非线性滤波估计精度,且算法的稳定性和应对状态突变的跟踪鲁棒性能获得显著提高.最后,通过两个仿真例子验证了此算法的有效性与优越性.     

Adaptive Fuzzy Output-Feedback Tracking Control for a Class of Switched Stochastic Nonlinear Time-Delay Systems

In this paper, a design scheme for an adaptive fuzzy tracking controller is proposed for a class of switched stochastic nonlinear time-delay systems via dynamic output-feedback. First, a reduced-order observer is introduced to estimate the unmeasurable states of the switched system. During the adaptive controller design procedure, an appropriate stochastic Lyapunov–Krasovskii functional deals with the time-delay terms, and fuzzy logic systems are employed to approximate the unknown nonlinearities. Based on the designed controller, the semi-globally uniform ultimate boundedness of all the closed-loop signals is guaranteed and the tracking error converges to a small neighborhood of the origin. Finally, a simulation example is given to illustrate the validity of the proposed approach.     

An Iterative LMI-Based Reduced-Order Observer Design for Fractional-Order Chaos Synchronization

In this paper, attempts are made to design a reduced-order observer for a nonlinear Lipschitz class of fractional-order systems. It is assumed that nonlinear terms not only depend on measurable states but depend on unknown states and inputs as well. The sufficient conditions for stability of the observer based on the Lyapunov technique are derived and converted into linear matrix inequalities (LMIs). To overcome the main drawback of previous research studies which assumed that the sum of terms in infinite series coming from fractional derivative of a Lyapunov function is bounded and its upper bound is predefined, we used an iterative LMI-based algorithm to find out this bound. A four-wing chaotic system is implemented in both PSpice and MATLAB software as a case study. Simulation results are reported to show the effectiveness of the proposed iterative LMI-based reduced-order observer in tracking the unmeasurable state variables of the chaotic fractional system in different initial conditions.     

非线性系统的异步多速率数据 融合估计算法研究

 研究了一类非线性时变动态系统的状态估计问题,在不同传感器以不同采样率异步对同一目标进行观测时,提出了一种有效的数据融合估计算法.通过建立多尺度模型,将异步多速率系统形式转化为同步多速率系统;在每一步分别进行状态的预测和更新.在状态和观测预测时,采用强跟踪滤波(STF)算法;在状态更新时,采用有反馈分布式结构,顺序的利用每一个传感器的观测信息去更新状态的估计;从而基于给定的非线性系统模型,得到融合所有异步、多速率传感器观测信息的状态估计结果.该方法不需要对状态或观测进行扩维,计算量适当,从而保证了算法的实时性.仿真结果验证了算法的有效性.     

Precise friction control for the nonlinear friction system using the friction state observer and sliding mode control with recurrent fuzzy neural networks

This paper deals with a tracking control problem of a mechanical servo system with nonlinear dynamic friction which contains a directly immeasurable friction state variable and an uncertainty caused by incomplete parameter modeling and its variations. In order to provide an efficient solution to these control problems, we propose a composite control scheme, which consists of a friction state observer, a RFNN approximator and an approximation error compensator with sliding mode control. In first, a sliding mode controller and friction state observer are designed to estimate the unknown internal state of the LuGre friction model. Next, a RFNN is developed to approximate an unknown lumped friction uncertainty. Finally, an adaptive error compensator is designed to compensate an approximation error of RFNN. Some simulations and experiments on the mechanical servo system composed of ball-screw and DC servo motor are executed. Their results give a satisfactory performance of the proposed control scheme.     

Neural Network Based Adaptive Tracking of Nonlinear Multi-Agent System

In this paper, the problems of robust consensus tracking control for the second-order multi-agent system with uncertain model parameters and nonlinear disturbances are considered. An adaptive control strategy is proposed to smooth the agent’s trajectory, and the neural network is constructed to estimate the system’s unknown components. The consensus conditions are demonstrated for tracking a leader with nonlinear dynamics under an adaptive control algorithm in the absence of model uncertainties. Then, the results are extended to the system with unknown time-varying disturbances by applying the neural network estimation to compensating for the uncertain parts of the agents’ models. Update laws are designed based on the Lyapunov function terms to ensure the effectiveness of robust control. Finally, the theoretical results are verified by numerical simulations, and a comparative experiment is conducted, showing that the trajectories generated by the proposed method exhibit less oscillation and converge faster.     

多站测角的机动目标最小二乘自适应跟踪算法

为了避免被动跟踪中非线性带来的计算复杂化及精度的下降问题,该文首先采用最小二乘法对目标的状态进行粗估计,然后采用当前机动目标模型和自适应跟踪算法进行线性的卡尔曼滤波,以实现对目标较高精度的定位和跟踪。实验结果表明:该方法对于匀速和匀加速运动的目标都可以达到良好的跟踪效果,其误差远小于经典的singer方法;对于强机动目标,singer方法将失效,而本文方法仍能实时辨识出目标的速度和加速度,并且估计效果良好。     

基于UKF算法的惟方位单站无源跟踪

单站无源跟踪问题本质上是非线性估计问题,使用传统的EKF算法进行跟踪滤波,得到的结果误差较大,容易产生发散现象。本文在惟方位跟踪中应用UKF算法,仿真结果表明,与EKF相比,采用UKF算法跟踪精度较明显的提高,同时增强了滤波器的稳定性,有效地改善了跟踪性能。     

Model based reduced-order observers for a class of mechatronic systems with mitigation of disturbances effects using the Attractive Ellipsoid Method

State-estimation of mechatronic systems is essential because, in addition to position and velocity, there are electric and magnetic signals that must be measured, and can lead to sophisticated and expensive instrumentation systems. This paper addresses the problem of state-estimation for underactuated mechanical systems whose active joints are driven by permanent magnet dc-motors. Luenberger-type structures are designed, where the measured state variables and the dynamic model are used to construct the unavailable ones, in presence of uncertainties and disturbances as inputs. To avoid the peaking phenomenon is one of the main challenges on state observation for nonlinear systems. Underactuated mechatronic systems are not immune to this problem, which is exacerbated when external disturbances are taken into account. The major purpose here is to use a robust reduced observer to estimate the unmeasured state variables. The Attractive Ellipsoid Method provides reliable estimation of unmeasured state signals because it reduces the influence of external disturbances on the estimate error signals. This ensures that the estimated signals converge to an invariant set of minimal size around the real ones. The first design is based on the quasi-linear representation that meets the observability condition. The second considers the entire nonlinear model, which necessitates the availability of the entire position vector. The major findings are demonstrated experimentally on a non-traditional mechatronic system: the linear double pendulum system whose last joint is constrained by two linear springs. This shows a significant improvement of the estimation response, concerning robust techniques based on the linear approximation, towards its application in designing an adaptive observer to provide state and parameters estimation simultaneously, which is essential in robust and adaptive control systems.