加速抑制随机初态误差影响的迭代学习控制

针对一类具有不确定性的多输入多输出非线性系统,提出一种迭代学习控制算法.该算法具有的特点是:针对任意初态情形,结合开环 D型迭代学习控制器的优点,在时间轴上设计了一个随迭代次数增加而缩短的时间段.在该时间段上,控制算法对状态偏差进行修正,以使系统输出在此段时间后跟踪期望输出,且系统跟踪误差收敛到一个界内.这个界仅由系统自身不确定性和不确定的外界干扰决定,与初态误差无关.当外界扰动为0,以及迭代次数趋于无穷时,经过上述时间段后,系统输出精确跟踪期望输出.理论证明和仿真结果都说明了该算法的有效性.     

基于神经网络的注塑机注射速度的迭代学习控制

对具有不确定性和干扰项的重复非线性注塑机控制系统,尤其是注射速度的控制,提出基于神经网络的迭代学习控制器,其中迭代学习控制器设计为神经网络控制器,它以前馈方式作用于对象。PD反馈控制器用于使系统达到稳定,同时和前馈的神经网络学习控制器一起使系统达到理想的控制效果。仿真结果表明,该控制器可以随着迭代次数的增加有效减小跟踪误差。     

保证瞬态性能的迭代学习控制

讨论一类不确定非线性系统的可保证瞬态性能的迭代学习控制问题.引入限定跟踪误差瞬态特性的界函数,通过误差转换方法,定义一个转换误差变量,将跟踪误差的保证瞬态特性问题转化为该误差变量的有界性问题.采用Lyapunov方法,设计迭代学习控制器处理系统中参数和非参数不确定性.并且,采用完全限幅学习机制,保证转换误差变量的有界性和一致收敛性.从而既能得出系统输出在整个作业区间的完全跟踪性能,同时又能够保证跟踪误差在每次迭代的过程中具有保证的瞬态特性.仿真结果验证了所提控制方法的有效性.     

姿态变化一致有界的姿态稳定控制器设计

针对航天器姿态稳定控制问题, 设计一种迭代学习姿态控制器. 将连续非周期运动的姿态跟踪过程分解为队列重复运动, 采用前一周期的姿态跟踪误差修正后一周期的控制输入, 分别对未知参数和干扰构建有界迭代学习律, 给出航天器姿态稳定控制器, 并从理论上分析了闭环系统的渐近稳定性和姿态跟踪误差的一致有界性. 通过在轨捕获非合作目标过程中航天器姿态跟踪控制问题的数值仿真, 验证了迭代学习控制器的鲁棒性和强抗干扰性.

     

用奇异模型描述的受限机器人的学习控制

本文了一类受限机器人的迭代学习控制方案，控制器的设计是基于机器人模型的降阶形式。在存在有界未知干扰的情况下，对末端操纵器受线性、无摩擦约束面的受限机器人。本文给出的控制方案保证了机器人系统的完全运动跟踪，同时保证了力跟踪误差有界的，且界的大小是可调节的。     

具有未知死区输入非线性系统的迭代学习控制

针对一类具有死区输入非线性系统,提出一种实现有限作业区间轨迹跟踪控制的神经网络迭代学习算法.基于Lyapunov-like方法设计学习控制器,回避了常规迭代学习控制中受控系统非线性特性需满足全局Lipschitz连续条件的要求.为处理输入死区,利用神经网络逼近这种强非线性特性;同时,通过对神经网络逼近误差界的估计并在控制器中设置补偿作用以消除其影响,从而提高系统的跟踪性能.     

同步发电机励磁控制器设计

针对基于开环迭代学习控制的同步发电机励磁控制器存在稳定性差、易受干扰影响、不能完全跟踪期望轨迹的问题,设计了一种基于开闭环PID迭代学习控制与电力系统稳定器结合的同步发电机励磁控制器;在开环迭代学习控制的基础上,引入闭环反馈控制,将上一次的输入和输出误差及当前的输出误差作为当前的控制输入,从而构成开闭环PID型迭代学习控制;同时,利用电力系统稳定器提供的附加正阻尼信号来改善同步发电机励磁控制系统的电压稳定性和功角稳定性。仿真结果表明,该励磁控制器具有较强的鲁棒性,提高了系统稳定性。     

基于迭代滑模增量反馈的欠驱动AUV地形跟踪控制

为实现欠驱动自治水下机器人(AUV)在未知海流干扰作用下的地形跟踪控制,提出一种基于非线性迭代滑模增量反馈的航迹跟踪控制器.基于虚拟向导的方法,建立AUV垂直面航迹跟踪误差方程.采用迭代方法,设计滑模增量反馈控制器,无需对AUV模型参数不确定部分和海流干扰进行估计,这样避免了AUV俯仰舵的抖振现象,并且减小了输出反馈控制的稳态误差与超调问题.仿真实验表明,所设计的控制器对AUV系统的模型参数摄动及海流干扰变化不敏感,所设计的参数易于调节.     

基于非线性迭代滑模的欠驱动UUV三维航迹跟踪控制

为实现欠驱动无人水下航行器(Unmanned underwater vehicle, UUV)在未知海流干扰作用下的三维航迹跟踪控制, 提出一种基于工程解耦思想设计的非线性迭代滑模航迹跟踪控制器. 基于虚拟向导的方法,建立UUV空间航迹跟踪误差方程;采用迭代方法设计非线性滑模控制器, 无需对UUV模型参数不确定部分和海流干扰进行估计,避免了舵的抖振现象以及减小了稳态误差与超调问题. 仿真实验表明,设计的控制器对欠驱动UUV系统的模型参数摄动及海流干扰变化不敏感、 且设计参数易于调节,可以实现三维航迹的精确跟踪.     

具有未知死区输入非线性系统的迭代学习控制

针对一类具有死区输入非线性系统,提出一种实现有限作业区间轨迹跟踪控制的神经网络迭代学习算法.基于Lyapunov-like方法设计学习控制器,回避了常规迭代学习控制中受控系统非线性特性需满足全局Lipschitz连续条件的要求.为处理输入死区,利用神经网络逼近这种强非线性特性;同时,通过对神经网络逼近误差界的估计并在控制器中设置补偿作用以消除其影响,从而提高系统的跟踪性能.

     

Adaptive iterative learning control for switched nonlinear continuous-time systems

In this paper, an adaptive iterative learning control (ILC) method is proposed for switched nonlinear continuous-time systems with time-varying parametric uncertainties. First, an iterative learning controller is constructed with a state feedback term in the time domain and an adaptive learning term in the iteration domain. Then a switched nonlinear continuous-discrete two-dimensional (2D) system is built to describe the adaptive ILC system. Multiple 2D Lyapunov functions-based analysis ensures that the 2D system is exponentially stable, and the tracking error will converge to zero in the iteration domain. The design method of the iterative learning controller is obtained by solving a linear matrix inequality. Finally, the efficacy of the proposed controller is demonstrated by the simulation results.     

融合迭代学习与干扰观测器的压电微动平台精密运动控制

针对运动系统中常见的重复参考轨迹,尽管迭代学习控制（iterative learning control,ILC）可以通过迭代有效消除重复误差,但其对于非重复性干扰十分敏感．为实现在非重复干扰环境下压电微动平台的精密运动,提出了融合ILC与干扰观测器（disturbance observer,DOB）的控制策略．为避免复杂的迟滞建模,将迟滞非线性视为迭代过程中的重复性输入干扰．为保证控制策略的稳定性,推导其收敛条件并分析对非重复性干扰的抑制作用从而降低收敛误差．最后在压电微动平台进行了对比实验,结果表明：所提控制策略可以在无迟滞模型的前提下有效补偿迟滞非线性．针对理想环境下的5Hz、10Hz、20Hz三角波跟踪,其跟踪误差的均方根在行程的0.4%以内;而在非重复干扰环境下,跟踪误差的均方根为10.24nm,与内置的控制器、单独的反馈控制器、ILC相比,分别降低了98.73%、98.67%与88.24%．而且在干扰环境下,所提控制策略加快了ILC的收敛速度．实验结果充分验证了所提控制策略的有效性,实现了压电微动平台的精密运动．     

Robust design of feedback feed-forward iterative learning control based on 2D system theory for linear uncertain systems

For a class of linear discrete-time uncertain systems, a feedback feed-forward iterative learning control (ILC) scheme is proposed, which is comprised of an iterative learning controller and two current iteration feedback controllers. The iterative learning controller is used to improve the performance along the iteration direction and the feedback controllers are used to improve the performance along the time direction. First of all, the uncertain feedback feed-forward ILC system is presented by an uncertain two-dimensional Roesser model system. Then, two robust control schemes are proposed. One can ensure that the feedback feed-forward ILC system is bounded-input bounded-output stable along time direction, and the other can ensure that the feedback feed-forward ILC system is asymptotically stable along time direction. Both schemes can guarantee the system is robust monotonically convergent along the iteration direction. Third, the robust convergent sufficient conditions are given, which contains a linear matrix inequality (LMI). Moreover, the LMI can be used to determine the gain matrix of the feedback feed-forward iterative learning controller. Finally, the simulation results are presented to demonstrate the effectiveness of the proposed schemes.     

A Learning Control for a Class of Linear Time Varying Systems Using Double Differential of Error

In this paper, a new iterative learning control based on the double differential of the error is proposed for the linear time varying system having relative degree greater than one. The convergence criterion of the proposed method is proved. Furthermore, it is shown by simulations that convergence of error can be increased considerably by using our proposed controller as compared to the iterative learning controller using error or single differential of the error for the modification of the control input without increasing the learning gain.     

高相对阶连续时间系统的间接迭代学习控制

本文提出了一类高相对阶线性连续时间系统的间接迭代学习控制算法,该算法相对独立于系统局部控制器,因此可以应用于已有局部反馈控制器的系统.采用具有极点配置的H∞鲁棒控制器作为系统的内环控制,而在外环通过迭代学习控制调整内环系统的指令信号.通过引入拉氏变化,构建了迭代学习系统的2-D Roesser模型,推导了系统渐近收敛条件,并研究了存在有界初始条件偏移和迭代变化外部干扰时算法的鲁棒性能.最后,利用空中加油对接控制的算例进一步验证了算法的有效性.     

严格反馈互联系统分散式backstepping自适应迭代学习控制

基于Lyapunov分析方法,针对具有严格反馈形式的非线性互联系统,本文设计了一种分散式backstepping自适应迭代学习控制器.子系统之间的互联项为所有子系统输出项线性有界,为每个子系统设计的控制器仅采用该子系统的信息,不需要子系统之间相互传递信息.在控制器中,引入在时间轴和迭代轴上同时更新的自适应参数,以补偿子系统之间的互联项影响.通过采用本文给出的控制器,可使得每个子系统的输出跟踪相应的参考模型输出,仿真结果验证了本文算法的有效性.     

基于迭代学习观测器的航天器姿态主动容错控制

针对含有外部扰动和执行器故障的一类航天器姿态控制系统,本文提出基于迭代学习观测器的主动容错控制方案.首先,建立了含有外部扰动和执行器故障的航天器姿态控制系统的运动学和动力学模型.其次,为了提高观测器的故障估计精度,在传统迭代学习观测器设计基础上引入上一时刻状态估计误差信息,文章提出一种改进型学习估计算法.进一步,基于滑模控制和指定时间稳定理论,利用学习观测器的故障估计信息设计指定时间主动容错控制器.与现有的航天器主动容错控制方案相比,本文所提出的算法的优势在于可以使故障系统的姿态能在指定时间跟踪上指令信号.基于Lyapunov方法,本文从理论上证明了改进型学习观测器和姿态容错控制系统的稳定性.最后,通过数值仿真,说明了所提容错控制方案的有效性和可行性.     

Iterative learning of model reference adaptive controller for uncertain nonlinear systems with only output measurement

In this paper, a model reference adaptive control strategy is used to design an iterative learning controller for a class of repeatable nonlinear systems with uncertain parameters, high relative degree, initial output resetting error, input disturbance and output noise. The class of nonlinear systems should satisfy some differential geometric conditions such that the plant can be transformed via a state transformation into an output feedback canonical form. A suitable error model is derived based on signals filtered from plant input and output. The learning controller compensates for the unknown parameters, uncertainties and nonlinearity via projection type adaptation laws which update control parameters along the iteration domain. It is shown that the internal signals remain bounded for all iterations. The output tracking error will converge to a profile which can be tuned by design parameters and the learning speed is improved if the learning gain is large.     

A reinforced learning control using iterative error compensation for uncertain dynamical systems

This paper investigates a learning control using iterative error compensation for uncertain systems to enhance the precision of a high speed, computer-controlled machining process. It is specially useful in mass-produced parts produced by a high-speed machine tool system. This method uses an iterative learning technique which adopts machine commands and cutting errors experienced from previous manoeuvres as references for compensation actions in the current manoeuvre. Non-repetitive disturbances and nonlinear dynamics of the cutting processes and servo systems of the machine which greatly affect the convergence of the learning control systems were studied in this research. State feedback and output feedback methods were used for controller design. Stability and performance of learning control systems designed via the proposed method were verified by simulations on a single degree of freedom servo positioning system.