轮廓位置误差补偿方法研究

轮廓加工中存在的电气、机械延迟、系统参数不确定性及两轴驱动系统参数不匹配等因素的影响,提出了增益误差辅助补偿零相差前馈跟踪控制器(K-ZPETC)与交叉耦合控制器(CCC)相结合的控制策略对两轴的运动进行协调控制来减小轮廓误差。并采用专家PID控制增益K使系统实现准确跟踪,减小了跟踪误差;CCC作用于两轴之间,用以增加两轴间的匹配程度,以减小轮廓误差。仿真和实验结果表明所提出的控制方案具有较好的跟踪性和鲁棒性,进而大大提高了轮廓精度。     

立轴磨床多轴加工进刀轨迹优化控制模型

对立轴磨床多轴加工进刀轨迹优化控制模型的设计,提高模型的加工精度,为评价数控立轴磨床多轴联动的误差补偿提供依据。传统的进刀轨迹优化控制模型采用稳态预测误差控制的控制算法,导致磨床进刀的交叉耦合轨迹预补偿误差较大。提出一种基于直线轨迹的轮廓误差补偿的立轴磨床多轴加工进刀轨迹优化控制模型。以CAD为预处理软件工具,对加工模具进行空间曲线生成实体模型,构建三维造型,进行立轴磨床的多轴加工进刀轨迹控制模型设计,正确地选择加工刀具,合理地设置切削参数,构建刀具进刀的时间控制轴。构建复杂的零件加工控制系统解耦得到两个独立的控制子系统,实现进刀轨迹三轴联动控制,构建立轴磨床多轴加工进刀轨迹控制系统的轮廓误差向量最小化目标函数,实现控制模型改进。仿真结果表明,该模型能有效提高加工精度,减小误差。     

基于BP神经网络的多电机交叉耦合控制方法研究

高精度的多轴同步控制已成为现代制造业的关键问题之一,针对多轴同步控制系统的非线性、时变、易受扰动影响等特性,现有的多轴同步控制策略难以确定合理的耦合控制规律和在线计算量较大等问题。分析了目前多轴同步控制策略的发展现状,设计了基于相邻交叉耦合误差的多轴同步控制算法,根据BP神经网络控制方法对交叉耦合控制参数进行整定。该方法不仅可以减小跟踪误差,同时可消除相邻轴之间的同步误差,结构简单,同步性能好。最后,论述了该方法在三轴同步控制系统中应用的有效性。     

神经网络离散建模算法在迭代学习控制中的应用

利用神经网络离散建模算法，给出了非线性离散系统的一种新颖的迭代学习控制方法．该迭代学习控制方法允许控制初始状态误差的存在且保证仅经过几次迭代就可使系统达到很高的控制精度．     

线性时变系统变初始条件变期望轨迹的迭代学习控制

对于非严格重复线性时变连续系统,初始迭代条件和参考轨迹在一定带宽范围内都是迭代变化的.提出一种非严格的迭代学习方法来控制跟踪整流.通过该方法所获得的控制器,能保证闭环系统的所有信号是全局有界的,能够使超出初始时间间隔的输出跟踪误差收敛到一个小的残差集内,该残差集大小取决于输入矩阵的估测误差.尤其是当输入矩阵已知的情况下,能够让超出的初始时间间隔输出跟踪误差趋近于零.     

试验机中多参数融合的双电机同步控制研究

为了防止旋转试验机在工作过程中的液体管路和各模拟信号线路等多参数因缠绕在一起而折断,以达到旋转试验机稳定工作的目的。基于试验机结构上的双电机同步控制理论分析,首先建立伺服电机的数学模型,并提出了在Simulink界面仿真下的常规双电机交叉耦合控制和模糊PID双电机交叉耦合控制的方法。Simulink仿真曲线结果表明,模糊PID交叉耦合控制系统在抗干扰性和稳定性方面比常规PID系统更好的结论。     

双线性型迭代学习控制算法

基于迭代学习控制中的开闭环配合方案，本文提出了一类双线性型学习控制算法．针对刚性关节机器人动力学特性，文中给出了保证算法迭代收敛性的充分条件．仿真结果表明，适当选取双线性学习控制成分的增益矩阵可加速收敛过程．     

迭代学习控制系统的鲁棒性分析

讨论了在偏离，状态输出扰动和非线性扰动同时存在的干扰环境中运行的迭代学习控制系统的鲁棒性问题。通过更精确的误差渐近界估计，结合迭代学习控制算法中的开环和闭环方案，给出了算法的鲁棒性条件，以及算法收敛性所要求的渐近干扰条件。     

一种五轴联动数控刀具半径补偿方法

针对五轴数控机床的刀具半径补偿不易实现等问题,基于后置处理提出了一种五轴刀具半径补偿方法。建立了刀具半径补偿的数学模型,得到了刀具补偿方向矢量和补偿后刀位点坐标,同时给出了后置处理的实现方法。以摆头转台类五轴数控机床为例,阐述了刀具半径补偿的实现方法,分别建立了前置刀位数据、补偿后刀位数据与机床各平动轴运动数据之间的关系。最后,基于VC++设计了一种具有刀具半径补偿功能的后置处理软件,并进行了仿真研究,仿真结果验证了所述刀具半径补偿方法的有效性。同时,以叶轮加工为例,进行了实验验证,在加工过程中不会出现干涉现象,轮廓误差可以控制在误差允许的范围内,加工效果与标准刀具十分接近,进一步验证了所述算法的可行性和实用性。     

基于迭代学习控制理论的励磁控制器仿真研究

将基于迭代控制理论所提出的一种线性迭代学习律,应用于同步发电机的励磁控制中,采用Matlab对单机--无穷大系统进行仿真,结果表明该方法的有效性和一般性,改善了控制性能、具有很强的维持机端电压的能力,有利于提高电力系统稳定性。     

Iterative learning feedback control for linear parabolic distributed parameter systems with multiple collocated piecewise observation

This paper presents a novel iterative learning feedback control method for linear parabolic distributed parameter systems with multiple collocated piecewise observation. Multiple actuators and sensors distributed at the same position of the spatial domain are utilized to perform collocated piecewise control and measurement operations. The advantage of the proposed method is that it combines the iterative learning algorithm and feedback technique. Not only can it use the iterative learning algorithm to track the desired output trajectory, but also the feedback control approach can be utilized to achieve real-time online update. By utilizing integration by parts, triangle inequality, mean value theorem for integrals and Gronwall lemma, two sufficient conditions based on the inequality constraints for the convergence analysis of the tracking error system are presented. Some simulation experiments are provided to prove the effectiveness of the proposed method.     

Adaptive iterative extended state observer-based data-driven iterative learning model predictive control for semiconductor silicon single crystal batch process

Aiming at the problems of unstable batch control of key crystal quality parameters and susceptibility to batch-to-batch non-repetitive disturbances during repeated operation of single crystal furnaces, this paper proposes a data-driven iterative learning model predictive control method based on an adaptive iterative extended state observer (IESO) for designing melt temperature and crystal diameter learning controllers with disturbance suppression. By applying dynamic linearization techniques and model predictive control strategies along the iterative axis, an ILMPC scheme with disturbance compensation terms using only input and output data of the system is designed. Among them, adaptive IESO is used to estimate the disturbance compensation terms. Then, the theoretical analysis shows that the tracking error of the ILMPC scheme can converge to a bounded range as the number of iterations increases. The experimental results verify the effectiveness of the proposed control method, which not only ensures that the control system has learning ability, but also achieves stable and accurate control of crystal quality parameters.     

基于逆系统的变轨迹迭代学习控制

针对一类未知非线性时变系统,本文提出一种不同次迭代运行过程中期望轨迹可变的迭代学习控制算法。该算法利用高斯径向基网络逼近系统逆的未知参数,并采用迭代学习的方式修正网络逼近的系数,然后结合变结构技术设计控制律。收敛性分析表明,随着迭代次数的增加,逼近系数与最佳系数的差异逐渐减小。最后,在机械臂上的仿真验证了算法的有效性。     

Consensus control via iterative learning for distributed parameter models multi-agent systems with time-delay

Most of the available results of iterative learning control (ILC) are that solve the consensus problem of lumped parameter models multi-agent systems. This paper considers the consensus control problem of distributed parameter models multi-agent systems with time-delay. By using the knowledge between neighboring agents, considering time-delay problem in the multi-agent systems, a distributed P-type iterative learning control protocol is proposed. The consensus error between any two agents in the sense of L² norm can converge to zero after enough iterations based on proposed ILC law. And then we extend these conclusions to Lipschitz nonlinear case. Finally, the simulation result shows the effectiveness of the control method.     

Distributed point-to-point iterative learning control for multi-agent systems with quantization

For multi-agent system (MAS), most of existing iterative learning control (ILC) algorithms consider about the tracking of reference defined over the whole trial interval, while the point-to-point (P2P) task, where the emphasis is placed on the tracking of intermediate time points, has not been explored. Thus, a distributed ILC method is proposed, in which each agent updates the feedforward control input by learning from the experience of itself and its neighbors in previous repeated tasks to achieve the goal of improving performance. In addition, for the sake of reducing the burden of data transmission in MAS, effective data quantization is essential. In this case, the quantitative measurement of the error of the tracking time points is further used in the ILC updating law. In order to accommodate this requirement, a distributed point-to-point iterative learning control (P2PILC) with tracking error quantization for MAS is first proposed in this paper. A necessary and sufficient condition is presented for the asymptotical stability of the proposed algorithm, and simulation results show the effectiveness of it finally.     

Event-triggered learning consensus of networked heterogeneous nonlinear agents with switching topologies

In this work, a lifted event-triggered iterative learning control (lifted ETILC) is proposed aiming for addressing all the key issues of heterogeneous dynamics, switching topologies, limited resources, and model-dependence in the consensus of nonlinear multi-agent systems (MASs). First, we establish a linear data model for describing the I/O relationships of the heterogeneous nonlinear agents as a linear parametric form to make the non-affine structural MAS affine with respect to the control input. Both the heterogeneous dynamics and uncertainties of the agents are included in the parameters of the linear data model, which are then estimated through an iterative projection algorithm. On this basis, a lifted event-triggered learning consensus is proposed with an event-triggering condition derived through a Lyapunov function. In this work, no threshold condition but the event-triggering condition is used which plays a key role in guaranteeing both the stability and the iterative convergence of the proposed lifted ETILC. The proposed method can reduce the number of control actions significantly in batches while guaranteeing the iterative convergence of tracking error. Both rigorous analysis and simulations are provided and confirm the validity of the lifted ETILC.     

Observer based switching ILC for consensus of nonlinear nonaffine multi-agent systems

This study considers the main challenges of presenting an iterative observer under a data-driven framework for nonlinear nonaffine multi-agent systems (MASs) that can estimate nonrepetitive uncertainties of initial states and disturbances by using the information from previous iterations. Consequently, an observer-based iterative learning control is proposed for the accurate consensus tracking. First, the dynamic effect of nonrepetitive initial states is transformed as a total disturbance of the linear data model which is developed to describe I/O iteration-dynamic relationship of nonlinear nonaffine MASs. Second, the measurement noises are considered as the main uncertainty of system output. Then, we present an iterative disturbance observer to estimate the total uncertainty caused by the nonrepetitive initial shifts and measurement noises together. Next, we further propose an observer-based switching iterative learning control (OBSILC) using the iterative disturbance observer to compensate the total uncertainty and an iterative parameter estimator to estimate unknown gradient parameters. The proposed OBSILC consists of two learning control algorithms and the only difference between the two is that an iteration-decrement factor is introduced in one of them to further reduce the effect of the total uncertainty. These two algorithms are switched to each other according to a preset error threshold. Theoretical results are demonstrated by the simulation study. The proposed OBSILC can reduce the influence of nonrepetitive initial values and measurement noises in the iterative learning control for MASs by only using I/O data.     

Terminal iterative learning control for discrete-time nonlinear systems based on neural networks

The terminal iterative learning control is designed for nonlinear systems based on neural networks. A terminal output tracking error model is obtained by using a system input and output algebraic function as well as the differential mean value theorem. The radial basis function neural network is utilized to construct the input for the system. The weights are updated by optimizing an objective function and an auxiliary error is introduced to compensate the approximation error from the neural network. Both time-invariant input case and time-varying input case are discussed in the note. Strict convergence analysis of proposed algorithm is proved by the Lyapunov like method. Simulations based on train station control problem and batch reactor are provided to demonstrate the effectiveness of the proposed algorithms.