超前采样时间迭代学习的下肢康复机器人轨迹跟踪控制

为了实现下肢康复机器人在康复训练过程中高精度的末端轨迹跟踪控制,提出了一种利用超前采样时间的鲁棒自适应迭代学习控制方法。所述超前采样时间迭代算法,是指利用之前运行批次在t+Δ采样时刻的髋膝关节力矩输出,优化调整下一次运行时刻t处的关节力矩给定。仿真结果表明,采用超前采样时间迭代控制,末端轨迹误差具有更快的收敛速度和跟踪精度,并且具有较好的抗干扰性能。     

高频角振动测试转台的迭代学习控制

为了提高角振动转台在陀螺高频特性测试时对周期性期望轨迹的跟踪精度,该文在PID型迭代学习控制的基础上,针对转台控制系统的稳定性和迭代学习的收敛性进行解耦设计,得到一种复合迭代学习控制器,并从频域角度给出了其收敛性条件,最后用MatlabSimulink工具箱对该设计进行了仿真研究.仿真结果表明,与常规比例-积分-微分控制相比较,这种设计改善了系统输出跟踪周期性正弦信号输入的精度,提高了系统带宽,并对系统消除周期性干扰有所裨益.结论表明这种复合迭代控制器可以应用于高频角振动转台的控制.     

时滞对象的自抗扰控制

时滞系统的控制在自动化领域具有重要意义。在剖析时滞系统控制的Smith预估法的本质的基础上,提出利用具有强大噪声抑制能力的"跟踪微分器"来实现"相位超前"、"相位滞后"等功能,从而解决时滞系统控制问题的新办法,其中包括把时滞算子近似成单位1的无视时滞法;把时滞算子近似成一阶惯性环节的提高阶次法;模仿Simith预估法把相位超前的虚拟输出当作被控量来进行无时滞对象控制的输出超前法;及引入虚似控制量的输入超前法等。相应的仿真研究表明,这些新方法都能控制好大时滞系统的控制问题,时滞大小已不再成为"难控"和"易控"的标准。     

非最小相位系统的基函数型自适应迭代学习控制

针对重复运行的未知非最小相位系统的轨迹跟踪问题, 结合时域稳定逆特点, 提出了一种新的基函数型自适应迭代学习控制(Basis function based adaptive iterative learning control, BFAILC)算法. 该算法在迭代控制过程中应用自适应迭代学习辨识算法估计基函数模型, 采用伪逆型学习律逼近系统的稳定逆, 保证了迭代学习控制的收敛性和鲁棒性. 以傅里叶基函数为例, 通过在非最小相位系统上的控制仿真, 验证了算法的有效性.     

卫星导航信号码跟踪精度理论的适用性研究

针对码跟踪精度理论在导航系统信号体制评估中的广泛应用,利用自主研发的卫星导航信号仿真平台对导航跟踪优化的适用条件进行了研究,对不同载噪比环境、前端接收带宽以及超前滞后间距条件下的信号伪距误差进行了测量,将仿真结果与理论结果对比,分析了实际应用中滤波器、鉴别器的非理想特性对信号码跟踪精度所造成的影响.仿真结果表明,在非相干超前滞后(NELP)码跟踪环的条件下,当载噪比较高时,前端接收带宽接近发射带宽,并在超前滞后间距足够小时,计算结果能够较真实的反映信号的性能,并可以为导航信号体制的理论评估以及新型导航信号的设计提供参考.     

间歇生产过程的R调节学习控制

针对间歇生产过程迭代学习控制难以进行跟踪性能分析的难题,本文提出一种变R调节迭代学习控制算法,借鉴经典控制理论定义有界跟踪和零跟踪概念.以此研究能够让系统输出误差达到零跟踪的迭代学习控制策略,并严格地证明了算法的性能,得出可以通过调节权值R使过程产品质量的误差收敛到与模型精度相关联的有界区域的结论,为相关理论结果实施于实际间歇过程提供了理论依据.     

参考轨迹更新的点到点迭代学习控制算法优化及应用

针对受非重复扰动作用的离散线性系统的输出跟踪控制问题,提出一种基于参考轨迹更新的点到点迭代学习控制算法.首先通过构建性能指标函数对控制器进行范数优化,并给出相应的收敛性条件,使得系统输出能够跟踪上更新后参考轨迹处的期望点.其次,当系统输出端受到某批次非重复扰动的影响时,进一步通过引入拉格朗日乘子算法构造多目标性能指标函数,以优化鲁棒迭代学习控制器,达到提高收敛速度和跟踪精度的目的.最后将该算法应用于电机驱动的单机械臂控制系统中,仿真结果验证了算法的合理性和有效性.     

非最小相位系统的扩展Laguerre基函数迭代学习控制

针对非最小相位系统的跟踪问题,提出了一种新的基函数迭代学习控制算法.该算法利用新型的非因果Laguerre扩展基函数逼近系统逆传递函数,设计最优迭代学习律使系统输入收敛到系统的稳定逆,保证了控制性能.算法不依赖于系统的先验模型,仅需以基函数信号作为系统输入进行模型辨识,减少了模型不确定性的影响.通过对单连杆柔性机械臂这样的典型非最小相位系统跟踪问题的仿真,验证了该方法的良好效果.     

输出重定义下的非线性非最小相位系统迭代学习控制

讨论非线性非最小相位系统实现完全跟踪的迭代学习控制方法, 适于在有限作业区间上重复运行的受控系统. 在控制器设计时, 通过输出重定义以使非最小相位系统的零动态变成渐近稳定特性. 分别采用部分限幅和完全限幅两种学习算法设计控制器, 理论分析表明两种算法能够保证学习系统中所有变量的有界性和跟踪误差在整个作业区间上渐近收敛于零. 数值仿真验证了两种迭代学习控制系统的跟踪性能.     

基于未知高频增益的非线性系统自适应迭代学习控制

针对一类单输入单输出不确定非线性重复跟踪系统,提出一种基于完全未知高频反馈增益的自适应迭代学习控制.与普通迭代学习控制需要学习增益稳定性前提条件不同,自适应迭代学习控制通过不断修改Nussbaum形式的高频学习增益达到收敛.经证明当迭代次数i→∞时,重复跟踪误差可一致收敛到任意小界δ.仿真结果表明了该控制方法的有效性.     

On learning transient,auto-tunings of learnable bandwidth and lead step in iterative learning control

The learning transient and tracking accuracy of phase lead compensation iterative learning control are determined by its three parameters: learning gain, system learnable bandwidth and lead step. Because of the model inaccuracy, the learnable bandwidth is often chosen as a conservative value, which often degrades the learning performance. In this article, the learning transient is analysed and the tuning of learnable bandwidth and lead step are developed to achieve good learning transient and tracking accuracy simultaneously. The attractive properties include that the less dependence on system model and that the tracking error during this process keeps at a very low level. Experimental results on an industrial robot are presented to verify the tuning process.     

自抗扰控制器的阶次与参数的选取

本文就选择不同阶次的自抗扰控制器时,对系统的控制参数选取进行了研究.结果表明:线性时不变系统的线性自抗扰控制,可等效为一个复合控制系统,其等效反馈补偿器为一超前校正单元串联一积分器;其等效前置滤波器为一滞后校正单元串联一微分器.观测器带宽和控制器带宽的比值,决定着反馈补偿器的最大相位超前角,而频带则决定着最大相位超前角的发生位置.同时,随着自抗扰控制器阶次的增加,补偿器的最大超前校正角也增加.通过对开环系统的频域分析,本文给出了利用该补偿器的频域特性进行自抗扰控制器参数设计的一般步骤,可大幅度减少工程师的反复试验过程,方便工程师应用.     

Two-mode ILC with pseudo-downsampled learning in high frequency range

In this paper, an iterative learning control scheme that combines the designs in the frequency and time domains is proposed to improve the tracking accuracy. The scheme separates the error signals into low and high frequency band error components and employs different learning mechanisms to deal with these error components, respectively. On the low frequency band, a conventional iterative learning control is used. On the high frequency band, a downsampled learning is implemented to suppress more error components to generate a high tracking accuracy. We will address in detail the issues of signal separation, learning controller design, convergence analysis, and experimental verification of the proposed scheme.     

分数阶迭代学习控制的收敛性分析

本文将传统的迭代学习控制时域和频域分析方法扩展到一类针对分数阶非线性系统的分数阶迭代学习控制时域分析方法.提出了一类新的分数阶迭代学习控制框架并简化了收敛条件,且证明了常增益情况下两类分数阶迭代学习控制收敛条件的等价性问题.该讨论进一步引出了如下两个结果:分数阶不确定系统的分数阶自适应迭代学习控制的可学习区域以及理想带阻型分数阶迭代学习控制的框架.上述结果均得到了仿真验证.     

Enhancing Iterative Learning Control With Fractional Power Update Law

The P-type update law has been the mainstream technique used in iterative learning control (ILC) systems, which resembles linear feedback control with asymptotical convergence. In recent years, finite-time control strategies such as terminal sliding mode control have been shown to be effective in ramping up convergence speed by introducing fractional power with feedback. In this paper, we show that such mechanism can equally ramp up the learning speed in ILC systems. We first propose a fractional power update rule for ILC of single-input-single-output linear systems. A nonlinear error dynamics is constructed along the iteration axis to illustrate the evolutionary converging process. Using the nonlinear mapping approach, fast convergence towards the limit cycles of tracking errors inherently existing in ILC systems is proven. The limit cycles are shown to be tunable to determine the steady states. Numerical simulations are provided to verify the theoretical results.      

An Exploration on Adaptive Iterative Learning Control for a Class of Commensurate High-order Uncertain Nonlinear Fractional Order Systems

This paper explores the adaptive iterative learning control method in the control of fractional order systems for the first time. An adaptive iterative learning control (AILC) scheme is presented for a class of commensurate high-order uncertain nonlinear fractional order systems in the presence of disturbance. To facilitate the controller design, a sliding mode surface of tracking errors is designed by using sufficient conditions of linear fractional order systems. To relax the assumption of the identical initial condition in iterative learning control (ILC), a new boundary layer function is proposed by employing Mittag-Leffler function. The uncertainty in the system is compensated for by utilizing radial basis function neural network. Fractional order differential type updating laws and difference type learning law are designed to estimate unknown constant parameters and time-varying parameter, respectively. The hyperbolic tangent function and a convergent series sequence are used to design robust control term for neural network approximation error and bounded disturbance, simultaneously guaranteeing the learning convergence along iteration. The system output is proved to converge to a small neighborhood of the desired trajectory by constructing Lyapnov-like composite energy function (CEF) containing new integral type Lyapunov function, while keeping all the closed-loop signals bounded. Finally, a simulation example is presented to verify the effectiveness of the proposed approach.      

基于周期信号的迭代学习控制在逆变器中的应用

提出一种基于周期信号的迭代学习控制方法,并将其应用于电力逆变器,分析了跟踪误差沿迭代方向的收敛条件,给出了控制律的时域表达形式以及参数的确定方法.由于该方法理论上可实现跟踪误差收敛到零,因而使逆变器输出电压跟踪精度大幅提高.通过MATLAB/Simulink仿真,该方法的有效性得到验证;此外,与传统PID控制相比,仿真结果显示出该方法具有较好的负载适应能力以及优越的跟踪性能.     

Experience-Based Iterative Learning Controllers for Robotic Systems

An experience based iterative learning controller is proposed for a general class of robotic systems. Experience of the iterative learning controller is stored in the memory in terms of input output data and later used for the prediction of the initial control input for a new desired trajectory. It is proved in this paper that using this approach we can reduce the number of iterations to achieve a certain user defined tracking accuracy. This approach is very general and applicable to all kinds of existing iterative learning control schemes. Numerical illustrations showed the effectiveness of the proposed method.     

基于迭代学习的多智能体系统分布式跟踪控制

文章考虑了具适多智能体系统的分布式跟踪控制问题。通过设计带有初始学习机制的$P$型和$PD^{\alpha}$ 型迭代学习控制策略求解跟踪问题。具适导数具有良好的性质且可以刻画不同步长的实际数据采样情况。初始学习机制放松了初始值条件且提高了算法实现趋同跟踪的性能。在可重复操作环境和有向通信拓扑的假设下,提出了一种分布式迭代学习方案,通过重复同一轨迹的控制尝试和用跟踪误差修正不满意的控制信号来实现有限时间趋同。严格证明了随着迭代次数增加,提出的$P$型和$PD^{\alpha}$ 型迭代学习控制策略使得所有智能体能渐近跟踪上参考轨迹。两个代表性数值仿真验证了算法的有效性。