Fourier-Neural-Network-Based Learning Control for a Class of Nonlinear Systems With Flexible Components

This paper considers an output feedback learning control for a class of uncertain nonlinear systems with flexible components. The distinct time delay caused by system flexibility leads to the phase lag phenomenon and low system bandwidth. Therefore, the tracking problem of such systems is very difficult and challenging. To improve the tracking performance of such systems, an iterative learning control scheme using the Fourier neural network (FNN) is presented in this paper. This scheme uses only local output information for feedback. FNN employs orthogonal complex Fourier exponentials as its activation functions and the physical meaning of its hidden-layer neurons is clear. The FNN-based learning controller introduced here relies on the frequency-domain method, which converts the tracking problem in the time domain into a number of regulation problems in the frequency domain. A novel phase compensation method is introduced to deal with the phase lag phenomenon, so that the bandwidth of the closed-loop system is increased. Experiments on a belt-driven positioning table are conducted to show the effectiveness of the proposed controller.     

基于遗传算法的直流伺服系统参数辨识及摩擦补偿控制

针对静态摩擦力对数控机床直流伺服系统的干扰问题,提出了一种先利用遗传算法对静态摩擦模型中的参数进行辨识,然后采用基于摩擦模型补偿的伺服控制方法。该方法首先根据直流伺服系统的摩擦特性建立摩擦模型,再将摩擦补偿引入到直流伺服系统的反馈控制结构中,获取伺服电机的位置误差。采用遗传算法对摩擦补偿模型进行参数辨识,使摩擦补偿量在数值上不断逼近实际的摩擦干扰,并利用摩擦补偿量来抵消摩擦给伺服系统带来的影响。为了验证参数辨识的效果,将普通PD控制与基于摩擦补偿的PD控制进行了仿真比较,实验结果表明,后者能够消除由于静摩擦的存在而造成的位置跟踪中出现的平顶现象,能够达到理想的跟踪效果。因此,本文所提出的方法具有较强的摩擦干扰补偿能力,能够实现对直流伺服系统的精确控制。     

自适应与迭代学习复合控制的电液位置系统的研究

电液伺服系统具有高阶性、非线性、时变性的特点,而且运行过程中还将受到温度变化等因素的干扰,所以常规的PID控制方法难以取得令人满意的效果。为了使电液伺服系统拥有较好的控制效果,提出一种将自适应与迭代学习控制相结合的控制策略,应用于电液伺服系统的位置控制中。应用位置作为反馈,采用开闭环PI的控制策略,以及自校正的控制原理设计控制器,最后运用MATLAB来对其进行仿真。仿真结果表明,该控制策略能够提高系统的控制精度,提升系统的响应速度,提高其收敛与鲁棒性能。     

基于重复补偿控制的交流伺服系统的PID控制

针对精密机床采用的交流伺服系统中,输出快速无超调地跟踪输入指令,控制精度高的要求,利用重复补偿控制的优点,将其与PID控制相结合形成一种基于重复补偿的PID控制结构,用于精密机床中交流伺服系统的位置控制,并在此基础上着重讨论了滤波器对控制效果的影响.从仿真结果可以看出,该控制方法提高了交流伺服系统对周期信号的跟踪精度....     

Further results on dissimilarity spaces for hyperspectral images RF-CBIR

Content-Based Image Retrieval (CBIR) systems are powerful search tools in image databases that have been little applied to hyperspectral images. Relevance feedback (RF) is an iterative process that uses machine learning techniques and user’s feedback to improve the CBIR systems performance. We pursued to expand previous research in hyperspectral CBIR systems built on dissimilarity functions defined either on spectral and spatial features extracted by spectral unmixing techniques, or on dictionaries extracted by dictionary-based compressors. These dissimilarity functions were not suitable for direct application in common machine learning techniques. We propose to use a RF general approach based on dissimilarity spaces which is more appropriate for the application of machine learning algorithms to the hyperspectral RF-CBIR. We validate the proposed RF method for hyperspectral CBIR systems over a real hyperspectral dataset.     

Observer‐based compensation control of servo systems with backlash

For compensating backlash phenomenon in servo systems, the authors propose an observer method in this paper to estimate both system states and vibration torque before controller design. First, a systematic scheme is given to obtain plant parameters, which is very important in observing system states. This is a parameter estimation principle that gives a crude estimation and computes the differences between the crude and true values. As a result, the precise value of the parameters is obtained by adding together the crude value and the difference. Then, based on the precise estimated parameters, an extended state observer (ESO) is designed to obtain feedback and feedforward signals. Consequently, robust compensation control is achieved by designing an output feedback controller, consisting of a feedback term and a feedforward term. Finally, in order to validate the proposed approach, extensive experiments are performed on a practical servo system with backlash nonlinearity.     

Adaptive fuzzy learning control for a class of nonlinear dynamic systems

This paper presents an adaptive iterative learning control scheme that is applicable to a class of nonlinear systems. The control scheme guarantees system stability and boundedness by using the feedback controller coupled with the fuzzy compensator and achieves precise tracking by using the iterative learning rules. In the feedback plus fuzzy compensator unit, the feedback control part stabilizes the overall closed‐loop system and keeps its error bounded, and the fuzzy compensator estimates and compensates for the nonlinear part of the system, thereby keeping the feedback gains reasonably low in the feedback controller. The fuzzy compensator is designed by applying the fuzzy approximation technique to the uncertain nonlinear term to be compensated. In the iterative learning controller, a simple learning control rule is used to achieve precise tracking of the reference signal and a parameter learning algorithm is used to update the parameters in the fuzzy compensator so as to identify the uncertain nonlinearity as much as possible. © 2000 John Wiley & Sons, Inc.     

神经网络在线学习补偿自适应控制及其应用

针对电液伺服系统的复杂非线性和不确定性特性,基于反馈误差学习法、小波分析理论并结合面向控制的辨识思想,提出了神经网络在线自学习自适应控制与"参征器"补偿控制相结合的控制方法.该方法将"过程辨识"和"参征器"引入反馈误差学习法的神经网络学习和控制中,控制参数的调整基于被控过程的小波变换结果信息,利用反馈误差学习法实现;"参征器"起监督和补偿控制作用,避免控制器的输出产生振荡或进入饱和状态.应用研究结果证明:该方法避免了采用直接反馈误差法可能造成的饱和和过调整问题;有效地提高了系统的稳定性、鲁棒性、控制精度和     

An iterative learning control theory for a class of nonlinear dynamic systems

An iterative learning control scheme is presented for a class of nonlinear dynamic systems which includes holonomic systems as its subset. The control scheme is composed of two types of control methodology: a linear feedback mechanism and a feedforward learning strategy. At each iteration, the linear feedback provides stability of the system and keeps its state errors within uniform bounds. The iterative learning rule, on the other hand, tracks the entire span of a reference input over a sequence of iterations. The proposed learning control scheme takes into account the dominant system dynamics in its update algorithm in the form of scaled feedback errors. In contrast to many other learning control techniques, the proposed learning algorithm neither uses derivative terms of feedback errors nor assumes external input perturbations as a prerequisite. The convergence proof of the proposed learning scheme is given under minor conditions on the system parameters.     

液压驱动型四足机器人电液伺服控制系统仿真建模与实验分析

复杂的作业环境和艰巨的作业任务使液压驱动型四足机器人对其伺服系统的精度、速度和力量均比一般机器人在普通情况下有更高的要求。为掌握液压驱动型四足机器人在多种路况下行走时各液压缸的受力情况以及液压系统内流量、压力的变化情况,需要对其虚拟样机进行机械动力系统和液压伺服系统的联合仿真,定性分析电液伺服系统位置、速度等被控对象的特性,并分析PID控制器在四足机器人伺服控制方面的特性与不足。针对传统控制算法在四足机器人控制存在的短板问题,设计了一种非对称前馈补偿模糊自适应PID算法,并利用物理样机进行了实际验证。实验结果为四足机器人电液伺服控制系统硬件、软件和控制算法的设计与优化指明了方向,还为研究四足机器人平稳步态控制策略提供了决策依据和数据支持。     

基于未知控制增益的非线性系统自适应迭代反馈控制

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控制增益的自适应迭代反馈控制. 与普通迭代学习控制需要学习增益稳定性前提条件不同, 所提自适应迭代反馈控制律通过不断修改Nuss baum形式的反馈增益达到收敛. 证明当迭代次数i→δ时, 重复跟踪误差可一致收敛到任意小界δ. 仿真显示了所提控制方法的有效性.     

Real-Time Iterative Compensation Framework for Precision Mechatronic Motion Control Systems

With regard to precision/ultra-precision motion systems, it is important to achieve excellent tracking performance for various trajectory tracking tasks even under uncertain external disturbances. In this paper, to overcome the limitation of robustness to trajectory variations and external disturbances in offline feedforward compensation strategies such as iterative learning control (ILC), a novel real-time iterative compensation (RIC) control framework is proposed for precision motion systems without changing the inner closed-loop controller. Specifically, the RIC method can be divided into two parts, i.e., accurate model prediction and real-time iterative compensation. An accurate prediction model considering lumped disturbances is firstly established to predict tracking errors at future sampling times. In light of predicted errors, a feedforward compensation term is developed to modify the following reference trajectory by real-time iterative calculation. Both the prediction and compensation processes are finished in a real-time motion control sampling period. The stability and convergence of the entire control system after real-time iterative compensation is analyzed for different conditions. Various simulation results consistently demonstrate that the proposed RIC framework possesses satisfactory dynamic regulation capability, which contributes to high tracking accuracy comparable to ILC or even better and strong robustness.      

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.     

滑模观测器在大惯量扫描镜系统中抗谐振的应用

针对扫描镜伺服控制系统中柔性连接所导致的机械谐振问题, 本文提出一种结合电机加速度反馈以及负 载速度反馈的滑模观测器控制方法. 首先建立伺服系统柔性连接的数学模型并分析谐振对系统性能的影响; 然后 给出滑模观测器的设计过程; 随后将观测出的电机加速度以及负载速度反馈到伺服系统中进行补偿; 最后分析了 位置传感器对伺服系统控制性能的影响, 系统的鲁棒性, 并与相似被控条件下的其他控制方法进行了比较. 仿真结 果表明: 与未采用观测器时相比, 本文所提出的方法有效地抑制了基于柔性连接的伺服系统中的机械谐振问题, 提 高了系统的控制性能, 且具有较高的鲁棒性.     

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.     

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.     

存在未知时滞非线性系统的迭代变区间预测迭代学习控制

本文针对机理模型未知的非线性非仿射多入多出(multiple-input and multiple-output,MIMO)离散时间系统, 研究了系统同时存在未知时滞和迭代变化运行时间区间的预测迭代学习控制(predictive iterative learning control,PILC)问题. 首先利用未知时滞的上下界信息建立了一种新型的动态线性化(dynamic linearization,DL)模型, 理论分析表明该模型能够等价描述本文所考虑的存在未知时滞的未知非线性系统. 同时, 设计一种新的数据补偿机制用以处理由于系统运行时间区间迭代变化而引起的数据丢失问题. 基于所建立的DL模型和数据补偿机制, 设计了能够同时处理未知时滞和迭代变化运行时间区间的预测迭代学习控制方法. 通过严格的理论分析同时给出了建模误差和跟踪控制误差的收敛性质. 最后, 通过仿真进一步验证了所提方法的有效性.     

基于TRIO运动控制器的瓦楞纸板横切机控制系统设计

本论文在分析瓦楞纸板横切机生产工艺的基础上,提出了以TRIO运动控制器＋欧姆龙伺服驱动器＋触摸屏的交流伺服控制方案。根据切刀的运动规律,研究其轨迹跟踪控制算法,利用TRIO运动控制器的电子凸轮功能,实现了横切机速度同步跟踪和定长剪切功能。实验表明,此控制系统能有效提高横切机的响应速度、剪切精度,并可适用于更高速的瓦楞纸板生产线。     

基于反馈控制的迭代学习控制器设计

针对具有不确定项或干扰项的重复非线性时变系统,提出了基于反馈控制的迭代学习控制器,其中迭代学习控制器设计为高阶PD型,它以前馈的形式作用于对象,在满足一定的收敛性条件下,证明了该控制器的跟踪误差界是系统初始状态误差界和系统输出干扰项界的线性函数,同时改变反馈增益可以调整系统的最终跟踪误差界,仿真与实验均表明了该方法的有效性。     

电动伺服舵机系统中的迭代学习控制

电动伺服舵机控制系统采用全数字三环控制策略,分别为位置环、速度环和电流环;作为内环的电流环,应具有良好的稳态和动态特性,其输出电流要求快速准确地跟踪给定电流,以保证舵机控制系统高性能位置伺服的要求;在传统的增量式积分分离PI控制算法的基础上,引入-D型迭代学习控制前馈环节,提高了电流跟踪的快速性和跟踪精度,建立了系统的数学模型并在MATLAB上进行了系统仿真;仿真结果表明,引入D型迭代学习控制后,电流环的稳态和动态特性良好,保证了输出电流跟踪的快速性、精确性.