带有跟踪微分器的无模型控制器的控制仿真研究

本文介绍了带有跟踪微分器的无模型控制器的基本理论和基本设计方法,比较了该控制方法与非线性跟踪微分器的控制效果.仿真结果表明:带有跟踪微分器的无模型控制器具有的独特的优点,适合于处理带有强干扰以及大时滞系统的控制问题.     

基于神经网络的跟踪-微分器设计

针对自抗扰控制的跟踪-微分器模块中存在着待定参数的问题,借助人工神经网络便于建立非线性映射的思想,提出了基于神经网络的跟踪-微分器的设计方法.运用该方法可以对任意阶的跟踪-微分器进行参数设计,设计出的跟踪-微分器具有良好的鲁棒性.仿真实验表明,由该设计方法设计出的跟踪-微分器能够提供精确的微分信号,有一定的工程应用参考价值.     

高精度快速非线性离散跟踪微分器

对二阶连续系统最速控制设计中采用的综合函数作了分析, 得到了一种新的高精度、快速离散非线性跟踪微分器, 并对其滤波特性、微分提取特性进行了初步分析. 分析表明, 这种跟踪微分器对输入信号具有很好的跟踪能力, 能够滤除噪声和提取微分信号. 相位延迟小, 幅值衰减小, 因而具有跟踪的快速性、准确性. 最后, 给出了数值仿真例子来验证该算法; 并给出了一个实验结果, 证实了算法的可行性.     

基于改进sigmoid函数的非线性跟踪微分器

本文受神经网络中常用的sigmoid激励函数特性的启发,提出了一种形式简单、调参相对容易的非线性跟踪微分器(STD).首先,在sigmoid函数的基础上引入指数和幅度因子,利用改进后的sigmoid函数构造加速度函数,接着,借助Lyapunov直接法证明了所设计的跟踪微分器的非摄动形式具有全局渐近稳定性,随后利用系统等价性给出了跟踪微分器的具体形式并通过扫频测试分析了其频域特性;最后,与线性微分器(LD)、全程快速跟踪微分器(HSTD)以及改进的跟踪微分器(ITD)、反正切形式的跟踪微分器(ATD)分别进行对比仿真分析.结果表明,基于sigmoid函数设计的跟踪微分器可以兼顾响应的快速性以及平稳性、全程无抖振,对信号的广义导数具有良好的逼近能力和滤波效果.     

非线性跟踪-微分器的性能分析及其改进

对非线性跟踪-微分器的性质进行分析,用适当的指数函数形式取代开关函数,定性地讨论时间尺度的取值问题,使系统在扰动存在的情况下也能满足跟踪要求,同时对跟踪-微分器的稳定性进行了分析.     

基于反双曲正弦函数的跟踪微分器

反双曲正弦函数是光滑连续函数,具有快速并消除速度高频震颤的作用.利用反双曲正弦函数构造加速度函数,设计二阶跟踪微分器,证明了跟踪微分器的收敛性,并分析了跟踪微分器频域特性.仿真实验表明,该跟踪微分器能对输入信号进行低通滤波,且跟踪精度高,响应速度快;同时,它较好地抑制了微分信号的噪声放大效应,可以得到输入函数理想的微分信号.     

非线性PID控制器

本文利用非线性跟踪-微分器和非线性组合方式来改进经典PID调节器以提高其适应性 和鲁棒性.     

一种改进的模糊PD 控制器

充分利用非线性跟踪微分器获得高质量微分信号的特性，将跟踪微分器与传统的简单模糊PD控制器相结合，提出一种简单的高性能的改进的模糊PD控制器．该改进模糊控制器的最显著特点是对测量噪声的强鲁棒性和工程易实现性．数值仿真证明了其有效性和高效性．     

基于非线性参数的电液伺服系统滑模控制

针对在电液伺服系统跟踪控制中存在非线性不确定参数和外界扰动的问题,提出了一种基于积分微分器的滑模Lyapunov函数的控制方法。首先,在只有位移信号测量输出的情况下,采用高阶积分链式微分器对其速度和加速度信息进行预估。系统存在非线性不确定参数,利用微分器对状态和不确定项的实时估计,设计出积分滑模控制器,实现自适应规律以及对电液伺服系统中不确定扰动的抑制。搭建电液伺服系统AMESim模型并与Matlab构成联合仿真平台,对控制器进行仿真。仿真表明,该控制器具有良好的对非线性不确定参数变化的补偿能力和跟踪性能。     

基于改进跟踪微分器的进气压力控制技术研究

高空台进气压力控制系统是高空模拟的关键部分,直接决定了模拟环境的可靠性。然而,其被控对象存在非线性与时滞特性,易导致反馈信号出现相位延迟和受噪声干扰。针对传统方法不能很好解决相位与滤波之间的矛盾,引入Fal函数滤波器,设计了一种改进的跟踪微分器实现对信号的相位补偿和滤波。数值仿真分析表明,改进跟踪微分器比传统相位补偿器具有更强的滤波和相位补偿能力。通过仿真试验证明了改进跟踪微分器在进气压力控制系统中的有效性。该方法可以提升进气压力控制系统的抗干扰能力。     

On a novel tracking differentiator design based on iterative learning in a moving window

Differential signals are key in control engineering as they anticipate future behavior of process variables and therefore are critical in formulating control laws such as proportional-integral-derivative (PID). The practical challenge, however, is to extract such signals from noisy measurements and this difficulty is addressed first by J. Han in the form of linear and nonlinear tracking differentiator (TD). While improvements were made, TD did not completely resolve the conflict between the noise sensitivity and the accuracy and timeliness of the differentiation. The two approaches proposed in this paper start with the basic linear TD, but apply iterative learning mechanism to the historical data in a moving window (MW), to form two new iterative learning tracking differentiators (IL-TD): one is a parallel IL-TD using an iterative ladder network structure which is implementable in analog circuits; the other a serial IL-TD which is implementable digitally on any computer platform. Both algorithms are validated in simulations which show that the proposed two IL-TDs have better tracking differentiation and de-noise performance compared to the existing linear TD.     

Differentiator-based time delay control for uncertain robot manipulators

High-quality acceleration signal plays a significant role in fast and precise trajectory tracking of robot manipulators via time delay control (TDC). This paper proposes a fast transient tracking differentiator (FTD) for obtaining the noise-less time derivative from a noisy measurement within the framework of tracking differentiator (TD) design methodology. Global asymptotic convergence of the proposed FTD is proven by Lyapunov's direct method and TD theory. The proposed FTD is cascaded to construct an acceleration estimation and is integrated with the commonly used TDC for an improved trajectory tracking of robot manipulators in the presences of parametric uncertainties and bounded disturbances. Numerical simulations and real-time experimental validation comparisons demonstrate that the proposed approach provides an easy-going model-free improved design for fast and accurate trajectory tracking of robot manipulators with position measurement only.     

大射电望远镜馈源指向系统轨迹跟踪自抗扰控制

采用独立控制策略实现新一代大射电望远镜馈源指向跟踪系统的高精度轨迹跟踪 .将两组成子系统之间的动力学耦合及其他扰动视为对精调Stewart平台子系统的未知扰动 ,设计了强鲁棒自抗扰控制器实现扰动补偿 .大射电望远镜馈源指向系统 5 0m缩比模型实验 ,验证了独立控制策略和自抗扰控制器的工程有效性和可行性 .为将来建造新一代大射电望远镜工程奠定了坚实的基础     

反正切形式跟踪微分器设计及相平面分析

跟踪微分器可以从不连续或含有噪声的测量信号中提取连续滤波信号和微分信号. 本文从二阶跟踪微分器的加速度函数式入手, 提出一种反正切形式的跟踪微分器结构, 利用李雅普诺夫第二方法证明其具有全局一致渐进稳定性, 利用相平面分析方法研究, 说明原点是稳定结点或稳定焦点, 并给出跟踪微分器参数调节的定性规律和约束条件. 最后在含有噪声的方波输入下进行仿真, 结果表明反正切形式跟踪微分器可以兼顾跟踪快速性与过渡过 程平稳性, 并且具有很好的滤波效果, 可以给出连续平滑的跟踪信号和微分信号, 而且待调节的参数较少, 具有工程实用价值.     

An adaptive visual attentive tracker for human communicational behaviors using HMM-based TD learning with new State distinction capability

To develop a nonverbal communication channel between an operator and a system, we built a tracking system called the Adaptive Visual Attentive Tracker (AVAT) to track and zoom in to the operator's behavioral sequence which represents his/her intention. In our system, hidden Markov models (HMMs) first roughly model the gesture pattern. Then, the state transition probabilities in HMMs are used to assign as the rewards in temporal difference (TD) learning. Later, the TD learning method is utilized to adjust the action model of the tracker for its situated behaviors in the tracking task. Identification of the hand sign gesture context through wavelet analysis autonomously provides a reward value for optimizing AVAT's action patterns. Experimental results of tracking the operator's hand sign action sequences during her natural walking motion with higher accuracy are shown which demonstrate the effectiveness of the proposed HMM-based TD learning algorithm of AVAT. During TD learning experiments, the exploring randomly chosen actions sometimes exceed the predefined state area, and thus involuntarily enlarge the domain of states. We describe a method utilizing HMMs with continuous observation distribution to detect whether the state would be split to make a new state. The generation of new states brings the ability of enlarging the predefined area of states.     

基于跟踪微分器的基波测量方法研究

电网基波参数特别是基波频率在谐波分析与治理中具有重要的地位,也是电能质量、电能计量以及电力系统控制等领域的重要技术指标。在非同步采样情况下,针对基于频域法测量基波频率存在频谱泄露现象和测算精度不高的问题,提出了基于跟踪微分器(Tracking Differentiator,TD)的基波参数时域测量方法。该方法的主要思想是将检测到的基波信号通过TD来获取基波跟踪信号及其微分跟踪信号,再通过这两个信号的最大幅值即可获取基波的幅值、频率和初相位参数。仿真结果表明,本文方法只需要60%额定工频周期窗口的样本数据即可获得较高精度的基波参数测算值,是一种有效的基波参数测量方法。     

Precise control of linear systems subject to actuator saturation using tracking differentiator and reduced order composite nonlinear feedback control

Aiming at the actuator saturation problem and the system performance requirement, this article proposes a new control scheme, i.e. a newly developed tracking differentiator-composite nonlinear feedback (TDCNF) control law, which is the combination of a tracking differentiator (TD) and a reduced order composite nonlinear feedback (CNF) control law. The TD, used here, mainly helps to provide a smooth reference signal and largely avoid actuator saturation. The reduced order CNF control law, on the one hand, estimates those unmeasurable state variables for measurement feedback control and, on the other hand, ensures satisfactory system performance. The stability of the newly developed TDCNF is proved in detail. Finally, to verify the effectiveness of the newly developed TDCNF control law, two illustrative examples are demonstrated and therein a novel design of the proposed control law is given. Simulation results show that the proposed control law can achieve good tracking performance and effectively avoid the actuator saturation.     

高动态离心机系统的自抗扰控制设计

针对传统PID在控制高速精密离心机系统时难以满足其高动态过程的要求,对系统目标过渡过程进行安排并设计了自抗扰控制器.所提出的自抗扰控制器包括3个部分:跟踪微分器、扩张状态观测器和误差反馈控制器.由于离心机在启动和制动阶段,系统状态会经历一个快速变化的过程,所以在离心机系统动态变化阶段采用跟踪微分器对目标函数进行过渡过程安排,防止系统出现过大超调;并且设计了扩张状态观测器对系统未知干扰进行估计和补偿;补偿后采用误差反馈控制器实现离心机系统高动态过程的跟踪控制.最后通过对自抗扰控制进行参数整定,使得系统满足所提出的各项性能指标要求.仿真结果验证了相比于传统PID控制,所提出的自抗扰控制器在超调量,调节时间以及稳态控制精度等性能指标上具有优越性.