非线性系统迭代跟踪控制的批次遗忘学习算法

针对P型迭代学习算法对初始偏差和输出误差扰动敏感,以及PD型迭代学习算法容易放大系统噪声,降低系统鲁棒性的问题,研究了具有任意有界扰动及期望输出的重复运行非线性时变系统的PD型迭代学习跟踪控制算法.利用迭代学习过程记忆的期望轨迹、期望控制以及跟踪误差,给出基于变批次遗忘因子的学习控制器设计,并借助λ范数理论和Bellman-Gronwall不等式,讨论保证闭环跟踪系统批次误差有界的学习增益存在的充分必要条件,及分析控制算法的一致收敛性.本算法改善了系统的鲁棒性和动态特性,单关节机械臂的跟踪控制仿真验证了方法的有效性.     

带遗忘因子的高阶闭环迭代学习控制器设计

为了解决迭代学习控制对系统存在的不确定性和非重复性干扰的鲁棒性问题，提出了一种带有遗忘因子的高阶闭环迭代学习控制器。该控制器中控制量包括反馈和前馈部分；其中，反馈控制采用简单的HD控制，迭代学习控制器设计为高阶HD型，它以前馈控制的形式作用于对象。通过引入遗忘因子对迭代学习控制器沿迭代方向进行滤波以，削弱系统模型的不确定部分及非重复干扰对系统收敛性的影响。仿真实验证明了该学习控制器的有效性和实用性。     

非线性时变系统开闭环P型迭代学习控制的收敛性

对于非线性时变系统,给出了其开闭环P型迭代学习控制收敛的充要条件.这些收敛条件与被控系统状态方程的具体形式无关.对比表明,该文的结论改进了现有结果.     

非线性系统高阶迭代学习算法

结合迭代学习控制算法中的开环和闭环方案，本文针对更一般的非线性系统，讨论高阶算法的广泛适用性。理论和仿真结果表明了高阶算法在输出跟踪和干扰抑制方面的有效性。     

非线性系统迭代学习算法

对于一个未知的非线性连续系统或离散系统,从任给的一个初始控制出发,尝试实现一条给定的输出目标轨线.在满足一定条件下,利用跟踪误差来修正控制函数,经过反复的迭代学习可以取得满意的效果.本文改进了Arimoto、Togai和Bien等的开环迭代学习的收敛条件,并提出闭环迭代学习算法.理论与仿真结果证明了闭环算法在收敛条件、速度和抗干扰能力上都优于开环算法.     

一类非线性时变系统的迭代学习控制

针对一类具有任意初态的不确定非线性时变系统,应用校正期望轨迹方法把任意初态问题转换为零初始误差的变期望轨迹的迭代学习控制问题,提出了求解校正期望轨迹的过渡轨迹的计算方法.然后,针对变期望轨迹问题提出了一种新的迭代学习控制算法,在算法中引入了期望轨迹的高阶导数来克服期望轨迹的变化,并通过设计稳定的跟踪误差滑动面来处理系统中非线性时变不确定性.论文给出了相关定理,并应用类Lyapunov方法给出了详细证明.仿真结果表明所提出的算法是有效的,该算法不需要系统的模型结构信息,比自适应迭代学习控制算法具有更宽的适用范围.     

非线性系统在任意初值下的PID型迭代学习控制

针对一类在有限时间区间上重复运行的非线性系统,给出了一种可以解决迭代学习控制中任意初值问题的PID型迭代学习算法及其收敛条件。采用算子理论证明了该算法的收敛性,结果表明该算法不仅有效解决了迭代学习控制的初值问题,而且放宽了收敛条件。仿真分析及与PD型迭代学习控制算法的仿真结果的对比证明,非线性系统在任意初值条件下经过PID型迭代学习后跟踪精度显著提高,输出误差曲线更快速趋于零,表明了该算法的有效性。     

连续非线性系统的迭代学习控制方法*

本文根据误差收敛准则，提出了连续非线性系统的迭代学习控制算法，给出了ＰＩＤ型学习控制算法的收效条件，实际应用表明，该方法可以逼近预定的任意轨线。     

基于模糊辨识的非线性连续系统的迭代学习控制

基于非线性连续动态的模型辨识算法，给出了非线性连续系统的一种非常有效的迭代学习控制方案。该控制方案不要求非线性连续系统中具体的非线性关系，并且容许系统初始误差的存在。     

非线性系统闭环P型迭代学习控制的收敛性

本文得到并证明了当被控系统的状态方程为一类非线性方程时，采用闭环Ｐ型学习律迭代学习控制的收敛的充分条件和必要条件，最后，我们给出了典型的仿真结果。     

高速列车隧道压力波模拟迭代遗忘因子控制

高速列车在隧道运行、隧道交会时车内、外会产生交替变化的气压差,对车体气密性及材料耐疲劳强度提出更高要求.基于Matlab和AMESim构建联合仿真平台,设计高速列车隧道压力波模拟系统.使用实测隧道压力波数据作为期望输出,研究车体气密疲劳性能.考虑到模拟系统非线性、大时滞及多扰动特点,采用一种带有时变遗忘因子的开环迭代PID控制算法.仿真结果表明,其系统更稳定、收敛速度更快.     

离散非线性系统开闭环P型迭代学习控制律及其收敛性

本文在讨论了一般开环与闭环迭代学习控制的不足后，针对一类离散非线性系统，提出了新的开闭环ＰＧ型迭代学习控制律，给出了它的收敛性证明，仿真结果表明：开闭环Ｐ型迭代律优于单纯的开环或产才环Ｐ型迭代 律。     

非线性离散时间系统迭代学习控制的稳定性分析

讨论了初始偏移对于非线性离散时间系统迭代学习控制性能的影响.提出描述选择学习控制算法的学习律,并给出保证系统稳定性的充分条件.     

Robust Optimization-Based Iterative Learning Control for Nonlinear Systems With Nonrepetitive Uncertainties

This paper aims to solve the robust iterative learning control(ILC)problems for nonlinear time-varying systems in the presence of nonrepetitive uncertainties.A new optimization-based method is proposed to design and analyze adaptive ILC,for which robust convergence analysis via a contraction mapping approach is realized by leveraging properties of substochastic matrices.It is shown that robust tracking tasks can be realized for optimization-based adaptive ILC,where the boundedness of system trajectories and estimated parameters can be ensured,regardless of unknown time-varying nonlinearities and nonrepetitive uncertainties.Two simulation tests,especially implemented for an injection molding process,demonstrate the effectiveness of our robust optimization-based ILC results.     

Prediction-based Iterative Learning Control (PILC) for Uncertain Dynamic Nonlinear Systems Using System Identification Technique

Prediction-based Iterative Learning Control (PILC) is proposed in this paper for a class of time varying nonlinear uncertain systems. Convergence of PILC is analyzed and the uniform boundedness of tracking error is obtained in the presence of uncertainty and disturbances. It is shown that the learning algorithm not only guarantees the robustness, but also improves the learning rate despite the presence of disturbances and slowly varying desired trajectories in succeeding iterations. The effectiveness of the proposed PILC is presented by simulations.     

Noise-Tolerant ZNN-Based Data-Driven Iterative Learning Control for Discrete Nonaffine Nonlinear MIMO Repetitive Systems

Aiming at the tracking problem of a class of discrete nonaffine nonlinear multi-input multi-output (MIMO) repetitive systems subjected to separable and nonseparable disturbances, a novel data-driven iterative learning control (ILC) scheme based on the zeroing neural networks (ZNNs) is proposed. First, the equivalent dynamic linearization data model is obtained by means of dynamic linearization technology, which exists theoretically in the iteration domain. Then, the iterative extended state observer (IESO) is developed to estimate the disturbance and the coupling between systems, and the decoupled dynamic linearization model is obtained for the purpose of controller synthesis. To solve the zero-seeking tracking problem with inherent tolerance of noise, an ILC based on noise-tolerant modified ZNN is proposed. The strict assumptions imposed on the initialization conditions of each iteration in the existing ILC methods can be absolutely removed with our method. In addition, theoretical analysis indicates that the modified ZNN can converge to the exact solution of the zero-seeking tracking problem. Finally, a generalized example and an application-oriented example are presented to verify the effectiveness and superiority of the proposed process.

     

非线性非仿射离散时间系统的两阶段最优迭代学习控制

On the basis of a new dynamic linearization technology along the iteration axis,a dual-stage optimal iterative learning control is presented for nonlinear and non-affine discrete-time systems.Dual-stage indicates that two optimal learning stages are designed respectively to improve control input sequence and the learning gain iteratively.The main feature is that the controller design and convergence analysis only depend on the I/O data of the dynamical system.In other words,we can easily select the control parameters without knowing any other knowledge of the system.Simulation study illustrates the geometrical convergence of the presented method along the iteration axis,in which an example of freeway traffic iterative learning control is noteworthy for its intrinsic engineering importance.