非线性离散时间系统的最优终端迭代学习控制

仅利用系统的终端输出误差而不是整个输出轨迹,提出了一种最优终端迭代学习控制方法.控制信号可直接通过终点的误差信息进行更新.主要创新点在于控制器的设计和分析只利用系统量测的I/O数据而不需要关于系统模型的任何信息,并可实现沿迭代轴的单调收敛.在此意义上,所提出的控制器是数据驱动的无模型控制方法.严格的数学分析和仿真结果均表明了所提出方法的适用性和有效性.     

非仿射非线性多智能体系统迭代学习一致跟踪

为了解决非仿射非线性多智能体系统在给定时间区间上一致性完全跟踪问题,基于迭代学习控制方法设计一种分布式一致性跟踪控制算法.首先,由引入的虚拟领导者与所有跟随者组成多智能体系统的通信拓扑,其中虚拟领导者的作用是提供期望轨迹.然后,在只有部分跟随者能够获得领导者信息的条件下,利用每个跟随者及其邻居的跟踪误差构造每个跟随者的迭代学习一致性跟踪控制器.同时采用中值定理将非仿射非线性多智能体系统转化仿射形式,并基于压缩映射方法证明所提算法的收敛性,给出算法的收敛条件.理论分析表明,在智能体的非线性函数未知情况下,利用所提算法可以使非仿射非线性多智能体系统在给定时间区间上随迭代次数增加逐次实现一致性完全跟踪.最后,通过仿真算例进一步验证所提算法的有效性.     

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.     

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.     

具有高阶时延的离散非仿射非线性系统的自适应跟踪控制

本文研究了一类具有高阶输入–输出时延的非仿射非线性离散不确定系统的自适应输出跟踪控制问题,提出了一种基于隐函数的自适应输出反馈输出跟踪控制方案.该方案主要解决了两个技术问题:一是构造了基于未知参数估计和未来时刻信号估计的隐函数方程解的自适应控制律,解决了因系统高阶时延导致的控制律因果矛盾问题并实现了闭环稳定和渐近输出跟踪;二是针对非仿射非线性控制律难求解问题,提出了基于迭代解的解析自适应控制律,实现了闭环稳定和实用输出跟踪.最后仿真研究证实了所提出控制方案的有效性.     

基于预设性能的非仿射非线性系统自适应有限时间控制

针对一类具有死区的非仿射非线性系统,将预设性能控制与有限时间控制相结合,提出一种具有预设性能的自适应有限时间跟踪控制方法.基于Backstepping技术、模糊逻辑系统及有限时间Lyapunov稳定理论,给出使系统半全局实际有限时间稳定(semi-globally practically finite-time stable,SGPFS)的充分条件和设计步骤.该控制策略不仅使系统的输出误差在有限时间内收敛到一个预先设定区域,同时保证其收敛速度、最大超调量和稳态误差均满足预先设定的性能要求.最后通过仿真示例验证了所提出设计方法的有效性.     

离散时间非线性系统的数据驱动无模型自适应迭代学习控制

本文基于迭代域的动态线性化方法,提出了一类单入单出离散时间非线性系统的数据驱动无模型自适应迭代学习控制方案.无模型自适应迭代学习控制本质上属于一种数据驱动控制方法,仅利用被控对象的输入输出数据即可实现控制方案的设计.理论分析表明无模型自适应迭代学习控制方案可以保证最大学习误差的单调收敛性.数值仿真和快速路交通控制应用验证了无模型自适应迭代学习控制方案的有效性.     

基于未知高频增益的非线性系统自适应迭代学习控制

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高频反馈增益的自适应迭代学习控制，与普通迭代学习控制需要复习增益稳定性前提条不同，自适应迭代学习控制通过不断修改Nussbaum形式的高频学习增益达到收敛，经证明当迭代次数i→∞时，重复跟踪误差可一致收敛到任意小界δ。仿真结果表明了该控制方法的有效性。     

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。     

一类非仿射非线性离散系统的改进无模型自适应控制

针对一类更广泛的非仿射非线性离散系统,提出一种改进的无模型自适应控制算法。该算法基于非参数动态线性化方法,运用观测器的思想,实现带有扰动系统的实时动态线性化,进而将无模型自适应控制方法的应用推广到更广泛的非仿射非线性离散系统。同时,对推广后的改进无模型自适应控制方法进行理论上的证明,并通过仿真实例验证了所提出的改进无模型自适应控制方法的可行性和有效性。     

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。     

一类非线性相似组合大系统的迭代学习控制

利用状态反馈部分线性化技术研究了一类非线性相似组合大系统的迭代学习控制问题。与现有结果不同的是，它不直接研究系统本身，而是构造一个适当的 反馈，然后对闭环系统给出其迭代学习收敛的充分条件，     

多变量非线性系统的变阶采样迭代学习控制

针对存在初态误差的情形, 提出多变量非线性系统的变阶采样迭代学习控制方法. 相对固定阶迭代学习算法, 变阶算法可有效降低跟踪误差. 对变阶采样迭代学习算法进行了收敛性分析, 推导出收敛充分条件. 给出了变阶学习的两种实现策略-DD (Direct division)和DIP (Division in phases)策略. 数值仿真表明, 基于DIP策略的变阶采样迭代学习算法在获得较高的控制精度的同时, 具有较快的收敛速度.     

非线性系统的PD型迭代学习控制

非线性系统的ＰＤ型迭代学习控制孙明轩黄宝健张学智（西安工业学院电子系西安７１００３２）关键词初始条件问题,迭代学习控制,非线性系统．１）国家自然科学基金资助项目．收稿日期１９９６－０７－２５１引言运用迭代学习控制技术设计控制器时,只需要通过重复操作获...     

Adaptive Robust Control for a Class of Uncertain MIMO Non-affine Nonlinear Systems

In this paper, the adaptive robust tracking control scheme is proposed for a class of multi-input and multioutput (MIMO) non-affine systems with uncertain structure and parameters, unknown control direction and unknown external disturbance based on backstepping technique. The MIMO nonaffine system is first transformed into a time-varying system with strict feedback structure using the mean value theorem, and then the bounded time-varying parameters are estimated by adaptive algorithms with projection. To handle the possible "controller singularity" problem caused by unknown control direction, a Nussbaum function is employed, and the dynamic surface control (DSC) method is applied to solve the problem of "explosion of complexity" in backstepping control. It is proved that the proposed control scheme can guarantee that all signals of the closed-loop system are bounded through Lyapunov stability theorem and decoupled backstepping method. Simulation results are presented to illustrate the effectiveness of the proposed control scheme.      

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.

     

Networked Iterative Learning Control Design for Nonlinear Systems with Stochastic Output Packet Dropouts

This paper develops two proportional‐type (P‐type) networked iterative learning control (NILC) schemes for a class of discrete‐time nonlinear systems whose stochastic output packet dropouts are modeled as 0‐1 Bernoulli stochastic sequences. In constructing the NILC schemes, two kinds of compensation algorithm of the dropped outputs are given. One is to replace the instant‐wise dropped output data with the synchronous desired output data; the other is to substitute the dropped data with the consensus‐instant output data used at the previous iteration. By adopting the lifting technique, it is derived that under certain conditions the expectations of the tracking errors incurred by the proposed NILC schemes converge to zero along the iteration axis. Numerical experiments are carried out for validity and effectiveness.