离散系统单调收敛高阶迭代学习控制

研究了一类离散线性时不变系统高阶迭代学习控制在相应范数意义下的单调收敛条件,给出了对给定目标函数迭代学习控制参数的最优解,并讨论了其收敛速度。常见的离散P型、D型及PD型ILC算法均可看作是所讨论算法的特例。仿真结果表明采用给出的最优设计具有更好的迭代学习单调收敛性能。     

Iterative learning control scheme for manipulators including actuator dynamics

This paper presents the iterative learning control for the industrial robot manipulators including actuator dynamics. Motivated by human learning, the basic idea of iterative learning control is to use information from previous execution of a trial in order to improve performance from trial to trial. This is an advantage, when accurate model of the system is not available as friction and actuator dynamics, though present in the system, are not modeled to reduce the computational complexity. In this paper different aspects of ILC including the design schemes and control algorithms are covered. The learning control scheme comprises two types of control laws: a linear feedback law and a feed-forward control law. In the feedback loop, the fixed gain PD controller provides stability of the system and keeps its state errors within uniform bounds. In the feed-forward path, a learning control rule/strategy is exploited to track the entire span of a reference input over a sequence of iterations. Algorithms are verified through detailed simulation results on a two DOF robot manipulator.     

A novel design strategy for iterative learning and repetitive controllers of systems with a high modal density: Theoretical background

This paper discusses the design and application of iterative learning control (ILC) and repetitive control (RC) for high modal density systems. Typical examples of these systems are structural and acoustical systems considered in active structural acoustic control (ASAC) and active noise control (ANC) applications. The application of traditional ILC and RC design techniques, which are based on a parametric system model, on systems with a high modal density has several important drawbacks: the design procedure is complex, the controllers require much computational power and the robustness of the controllers is low. This paper describes a novel strategy to design noncausal ILC and RC filters, which is especially suited for high modal density systems. Since it does not require a parametric system model, the novel strategy avoids several drawbacks of the traditional techniques: no cumbersome parametric model estimation is required; the ILC and RC controllers are robust to small changes of the poles and zeros of the controlled system; and the complexity of the ILC and RC control filters is restricted. A crucial element in the proposed strategy is the noncausal filtering in the ILC and RC controllers, which requires the availability of a trigger signal to announce a new ILC trial or RC period in advance. A numerical validation on a simulation model proves the potential of the developed strategy.     

重复轨迹运动迭代学习控制的频域分析

应用频域分析方法讨论了一类闭环迭代学习算法的收敛条件和性能,指出其比Arimoto开环迭代学习算法具有明显的优越性,并在讨论迭代收敛条件的基础上给出了闭环迭代学习算法的频域设计方法.     

基于遗传算法的PID型迭代学习控制增益的选取方法

针对PID型迭代学习控制算法,首先讨论了其收敛的充要条件和单调收敛的充分条件,然后给出目前利用单调收敛的充分条件确定PID增益的方法,并指出其不足。在此基础上,提出了基于遗传算法的PID型迭代学习增益选择方法（PID型GA-ILC算法）。利用该算法可以得到不满足PID迭代学习控制系统单调收敛条件但依然能使该系统单调收敛的PID增益,给出了数值仿真实例,证明了PID型GA-ILC算法的有效性。     

基于鲁棒迭代学习控制的永磁同步电机转矩脉动抑制

为了提高永磁同步电机转速伺服系统的性能,抑制转矩脉动对控制系统的影响,提出了滑模控制与迭代学习控制相结合的鲁棒迭代学习控制方法(RILC)。设计了迭代学习控制器抑制周期性转矩脉动,提出了滑模控制器提高系统的抗扰动性能,保证系统强鲁棒性及响应快速性。实验结果显示,电机以900r/min的速度运行时,采用鲁棒迭代学习控制可将速度脉动6次谐波幅值由0.89降低到0.56;加入0.5N·m的负载扰动后,转速波动最大值为22r/min,比PI-迭代学习控制法得到的值减小了1.8%。电机以60r/min运行时,采用鲁棒迭代学习控制可将速度脉动6次谐波幅值由4.87降低到0.45;加入0.5N·m的负载扰动的,转速波动最大值为24r/min,比PI-迭代学习控制法得到的值减小了23%。得到的结果表明,鲁棒迭代学习控制方法可有效抑制转矩脉动,同时可提高永磁同步电机转速伺服系统的鲁棒性和动态响应性能。     

基于迭代学习的粉末成形压机控制策略仿真

为了研究PI型迭代学习控制应用于有周期性动作的粉末成形压机位置控制的性能,首先,介绍了迭代学习控制的基本原理;然后,建立了液压机的主缸控制系统数学模型;最后,基于Matlab/Simulink软件建立了控制系统仿真模型,并对采用PI型迭代学习控制的系统输出位移响应进行了仿真。仿真结果表明,迭代学习控制策略应用于液压机位置控制是可行的。     

基于迭代学习控制的码垛机器人轨迹优化

提出一种学习控制方法用于码垛机器人的速度优化。通过对系统跟随误差学习,实现在误差允许范围内运动速度最大化,从而实现码垛效率的优化。本方法对系统建模要求较低,易于在实际控制器上实现。系统仿真实验结果表明,在带入机械臂系统参数后,在对迭代算法改进后的最优轨迹与未改进的标准轨迹比较后,前者通过改进局部速度,使机器人运转时间缩短了10％～20％。     

Iterative learning-based decentralized adaptive tracker for large-scale systems: a digital redesign approach

In this paper, a digital redesign methodology of the iterative learning-based decentralized adaptive tracker is proposed to improve the dynamic performance of sampled-data linear large-scale control systems consisting of N interconnected multi-input multi-output subsystems, so that the system output will follow any trajectory which may not be presented by the analytic reference model initially. To overcome the interference of each sub-system and simplify the controller design, the proposed model reference decentralized adaptive control scheme constructs a decoupled well-designed reference model first. Then, according to the well-designed model, this paper develops a digital decentralized adaptive tracker based on the optimal analog control and prediction-based digital redesign technique for the sampled-data large-scale coupling system. In order to enhance the tracking performance of the digital tracker at specified sampling instants, we apply the iterative learning control (ILC) to train the control input via continual learning. As a result, the proposed iterative learning-based decentralized adaptive tracker not only has robust closed-loop decoupled property but also possesses good tracking performance at both transient and steady state. Besides, evolutionary programming is applied to search for a good learning gain to speed up the learning process of ILC.     

超低频标准振动台波形复合迭代自学习控制策略研究

将迭代自学习控制与传统ＰＩＤ 控制相结合构成一种新的复合控制策略,应用于超低频标准振动台台面正弦波形的反馈控制上,获得了满意的结果     

B-spline network-based iterative learning control for trajectory tracking of a piezoelectric actuator

This paper presents the trajectory tracking approach of a piezoelectric actuator using an iterative learning control (ILC) scheme based on B-spline network (BSN) filtering. The ILC scheme adopts a state-compensated iterative learning formula, which compensates for the state difference between two consecutive iterations in order that the iterative learning can learn from the tracking errors of the previous iteration effectively. The BSN is used to attenuate the noises and retrieve the signals of the tracking errors for the ILC. The BSN serves as a unique filter which generally does not have zero-phase responses. Design details on the ILC scheme using BSN filtering are discussed in the paper. Extensive experiments of tracking two desired trajectories for a piezoelectric actuator are presented. The experimental results show that the state-compensated ILC scheme using BSN filtering can achieve fast error convergence and keep small steady-state tracking errors close to the system noise level. This research thus relaxes the restriction of the zero-phase criterion commonly applied to the ILC filtering in the literature.     

肌电调制结合迭代学习控制的足下垂 FES 系统

足下垂是指由于神经控制功能障碍导致踝关节无法产生背屈以致足尖上抬不完全或不能的现象。功能性电刺激(FES)作为纠正足下垂步态的治疗方法,通过低频脉冲刺激胫骨前肌引起肌肉收缩,使踝关节产生背屈动作,达到矫正足下垂的目的。本文提出了基于肌电(EMG)调制和迭代学习控制(ILC)的FES输出强度调制方法,利用小腿角速度信号通过动态BP神经网络预测健康步态胫骨前肌肌电信号,以脚尖俯仰角作为反馈信号通过ILC输出参考肌电信号,与神经网络预测的肌电信号加权平均得到修正后的肌电信号,最后利用肌肉激活特性调制FES输出。实验表明开环肌电调制模式下的脚尖俯仰角仅有17°左右,而在闭环调制模式下,脚尖俯仰角最大角度达到了21°左右。本文设计的FES控制系统可以帮助足下垂患者进行康复训练。     

Iterative learning control for the filling of wet clutches

This paper discusses the development of an advanced iterative learning control (ILC) scheme for the filling of wet clutches. In the presented scheme, the appropriate actuator signal for a new clutch engagement is learned automatically based on the quality of previous engagements, such that time-consuming and cumbersome calibrations can be avoided. First, an ILC controller, which uses the position of the piston as control input, is developed and tested on a non-rotating clutch under well controlled conditions. Afterwards, a similar strategy is tested on a rotating set-up, where a pressure sensor is used as the input of the ILC controller. On a higher level, both the position and the pressure controller are extended with a second learning algorithm, that adapts the reference position/pressure to account for environmental changes which cannot be learned by the low-level ILC controller. It is shown that a strong reduction of the transmitted torque level as well as a significant shortening of the engagement time can be achieved with the developed strategy, compared to traditional time-invariant control strategies.     

Tribological Behavior of 1-Methyl-3-Hexadecylimidazolium Tetrafluoroborate Ionic Liquid Crystal as a Neat Lubricant and as an Additive of Liquid Paraffin

Ionic liquid crystal (ILC), 1-methyl-3-hexadecylimidazolium tetrafluroborate, was synthesized. The tribological behavior of ILC was evaluated using a four-ball machine at 80 °C. X-ray photoelectron spectroscopic analysis shows that ILC takes part in tribochemical reactions to generate tribochemical products such as B₂O₃, FeF₂, and/or FeF₃, and amine which form a protective film on sliding steel surface, resulting in reduced friction and wear. Besides, ILC 1-methyl-3-hexadecylimidazolium tetrafluoroborate is completely transformed from solid state to liquid crystalline phase at 80 °C, which facilitates the ordered arrangement of its long alkyl chain on sliding steel surface and helps to improve the tribological properties. When the ILC is used as an additive of liquid paraffin (LP), it contributes to reduce friction and wear and increase the load-carrying capacity of the base stock both at room temperature and 80 °C. The reason might lie in that a small amount of F from ILC takes part in tribochemical reactions to generate tribochemical products that form a protective film on sliding steel surface, and friction-induced heat accelerates the transition of as-synthesized ILC to a mesophase and the ordered arrangement of its long alkyl chain on sliding steel surface, both resulting in improved load-carrying capacity and anti-wear ability of the ILC.     

巨型模锻水压机液压位置保持系统迭代控制研究

巨型模锻水压机动梁同步精度是决定锻件成型精度的关键参数。由于该类型水压机一般具有加工过程重复性、初始加工精度要求高、批次加工数量小的特点,单纯采用PID控制仅能保证初始加工精度,无法确保加工精度的逐步提高,因此本文在分析动梁位置保持系统模型的基础上,采用开环迭代控制和PD控制相结合控制策略,并提出了直接采用PD控制输出作为迭代控制初始控制量的方法.试验结果表明该方法收敛速度快,且能保证初始加工精度,动梁的位置保持精度经过2~3次迭代运行后可提高40%,调整时间可缩短约1.8 s。     

智能自主控制在航迹控制系统中的应用

首先研究了自主式控制系统的关键问题,然后提出一种智能自主式航迹控制系统的结构,采用了模块化设计思想,对直升机对象的航迹控制系统进行设计.还给出了航迹自主控制关键功能模块的算法与具体实现.最后文章给出了系统数字仿真的结果,验证了系统的可行性.     

A novel design strategy for iterative learning and repetitive controllers of systems with a high modal density: Application to active noise control

This paper describes the application of a novel design strategy for iterative learning and repetitive controllers for systems with a high modal density, presented in the companion paper, on two experimental case studies. Both case studies are examples of active structural acoustic control, where the goal is to reduce the radiated noise using structural actuators. In the first case study, ILC is used to control punching noise. An electrodynamic actuator on the frame of the punching machine is driven by the ILC algorithm which takes advantage of the repetitiveness of the consecutive impacts to reduce noise radiation. In the second case study, an RC algorithm is used to control the noise radiated by rotating machinery, which is often mainly periodic. A piezoelectric actuator incorporated in the bearing is driven by the RC algorithm which is capable of reducing harmonics of the rotational frequency of the shaft. Both applications show the practical usefulness of the novel design strategy.     

Large dynamic range nanopositioning using iterative learning control

This paper presents the control system design and tracking performance for a large range single-axis nanopositioning system that is based on a moving magnet actuator and a flexure bearing. While the physical system is designed to be free of friction and backlash, the nonlinearities in the electromagnetic actuator as well as the harmonic distortion in the drive amplifier degrade the tracking performance for dynamic commands. It is shown that linear feedback and feedforward proves to be inadequate to overcome these nonlinearities. This is due to the low open-loop bandwidth of the physical system, which limits the achievable closed-loop bandwidth given actuator saturation concerns. For periodic commands, like those used in scanning applications, the component of the tracking error due to the system nonlinearities exhibits a deterministic pattern and repeats every period. Therefore, a phase lead type iterative learning controller (ILC) is designed and implemented in conjunction with linear feedback and feedforward to reduce this periodic tracking error by more than two orders of magnitude. Experimental results demonstrate the effectiveness of ILC in achieving 10 nm RMS tracking error over 8 mm motion range in response to a 2 Hz band-limited triangular command. This corresponds to a dynamic range of more than 10⁵ for speeds up to 32 mm/s, one of the highest reported in the literature so far, for a cost-effective desktop-sized single-axis motion system.     

非线性电液位置伺服系统的迭代学习PID控制

介绍了一类具有非线性和重复运动特点的电液位置伺服系统的迭代学习PID控制方法。采用PID控制算法,利用系统重复运动的特点和计算机的记忆存储功能,将系统的每次实际输出与理想输出之间的误差应用于下一次控制过程中,即对实际输入不断进行修正,直至实际输出与期望输出间的误差达到满意为止。仿真结果表明,迭代学习PID控制算法具有实现简单、鲁棒性强和重复精度高的特点,能够对电液位置伺服系统实现有效的控制。     

液压同步连续滑移系统的同步控制算法

液压同步连续滑移技术是为解决大型构件移位安装问题而产生的一门新兴施工技术,该技术的核心是同步性控制.分析了一种计算机控制液压同步连续滑移的同步控制算法——双限幅 PID 控制算法,最后通过实际应用验证了该算法的有效性和可操作性.