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。     

基于未知控制增益的非线性系统自适应迭代反馈控制(英文)

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.     

控制方向未知的二阶时变非线性系统自适应迭代学习控制

对一类二阶严格反馈时变非线性系统的自适应迭代学习控制问题进行了研究.系统中含有非周期时变参数化不确定性且控制方向未知.首先,提出了一种神经网络估计器,实现了对未知非周期时变非线性函数的逼近.随后,用Nussbaum函数对未知控制方向进行了自适应估计,并综合应用baCkstcpping技术和自适应迭代学习控制技术设计了控制器.所设计的控制器能保证系统所有状态量在Lpe-范数意义下有界,且系统的输出量在LT2-范数意义下收敛到期望轨迹.最后的仿真研究证明了控制器设计方法的有效性.     

一类未知非线性系统的智能迭代学习控制

从自适应的角度设计迭代学习控制,将神经网络引入迭代学习控制中。学习控制与自适应控制相结合,使得对网络权值的学习和跟踪控制同时进行,克服 了经典迭代学习控制的一些缺陷。基于Lyapunov直接方法,证明了整个控制系统的稳定并实现了任意精度的跟踪。实例仿真结果说明了算法 的有效性及其所具有的优点。     

高频增益符号未知时的变结构模型参考自适应控制

解决了对象相对阶大于1、高频增益符号未知时的变结构模型参考自适应控制(VS-MRAC)问题．提出了一种基于监控函数的控制信号切换律, 证明只需要对首个辅助误差构造监控函数, 就可决定控制信号的切换时间;进而, 在监控函数的管理下, 控制信号经至多有限次切换后将停止切换, 闭环系统所有信号一致有界, 跟踪误差将收敛到一个残集内, 且该残集可通过减小某些设计参数而变得任意小．特别地, 我们证明, 若系统的某些初始条件为零, 则至多只需要一次切换．     

未知时变时滞非线性参数化系统自适应迭代学习控制

针对含有未知时变参数和时变时滞的非线性参数化系统,提出了一种新的自适应迭代学习控制方法.该方法将参数分离技术与信号置换思想相结合,可以处理含有时变参数和时滞相关不确定性的非线性系统.设计了一种自适应控制策略,使跟踪误差的平方在一个有限区间上的积分渐近收敛于零.通过构造Lyapunov-Krasovskii型复合能量函数,给出了闭环系统收敛的一个充分条件.给出两个仿真例子验证了控制方法的有效性.     

控制增益未知的船舶航向非线性自适应跟踪控制

针对参数不确定的船舶运动非线性控制系统控制方向未知的困难,将逆推算法与Nussbaum增益方法相结合,提出一种新的自适应非线性控制策略,从而实现船舶运动航向跟踪控制.首先,从理论上证明了所设计的自适应控制器保证最终的控制系数符号未知的参数不确定船舶运动非线性系统中所有信号一致有界,船舶的实际航向全局自适应地渐近跟踪期望的参考航向.对两条船舶数学模型的仿真实验结果表明,所设计的自适应非线性跟踪控制器具有良好的适应性及鲁棒性.     

高阶无模型自适应迭代学习控制

针对一类非线性非仿射离散时间系统,提出了高阶无模型自适应迭代学习控制方案.控制器的设计和分析仅依赖于系统的输入/输出(I/O)数据,不需要已知任何其他知识.该方法采用了高阶学习律,可利用更多以前重复过程中的控制信息提高系统收敛性,且学习增益可通过"拟伪偏导数"更新律迭代调节.仿真结果验证了所提出算法的有效性.     

具有未知输入增益的非线性系统的跟踪控制(英文)

针对具有未知输入增益的非线性系统, 提出了一种可实现系统输出跟踪控制的自适应控制方法. 通过在backstepping设计中引入一种新的Nausbaum增益, 按该方法设计的控制器可以在系统输入增益未知的情况下实现系统输出的渐近跟踪.     

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.     

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.     

Repetitive learning control of nonlinear systems over finite intervals

Iterative learning control requires initial repositioning, while the time functions to be learned should be of periodicity in repetitive control. However, there are cases in practice where the time-varying unknowns are not periodic but repetitive, and repetitive learning control is applicable with avoidance of initial repositioning. In this paper, repetitive learning control designs are presented for a broader class of nonlinear systems over finite intervals. The Freeman formula is modified and used for sta...     

Adaptive iterative learning control for a class of non-linearly parameterised systems with input saturations

In this paper, an adaptive iterative learning control scheme is proposed for a class of non-linearly parameterised systems with unknown time-varying parameters and input saturations. By incorporating a saturation function, a new iterative learning control mechanism is presented which includes a feedback term and a parameter updating term. Through the use of parameter separation technique, the non-linear parameters are separated from the non-linear function and then a saturated difference updating law is designed in iteration domain by combining the unknown parametric term of the local Lipschitz continuous function and the unknown time-varying gain into an unknown time-varying function. The analysis of convergence is based on a time-weighted Lyapunov–Krasovskii-like composite energy function which consists of time-weighted input, state and parameter estimation information. The proposed learning control mechanism warrants a L²_{[0, T]} convergence of the tracking error sequence along the iteration axis. Simulation results are provided to illustrate the effectiveness of the adaptive iterative learning control scheme.     

An optimal method for nonlinear iterative learning control systems with constraint and model uncertainty

Clonal selection algorithm is improved and proposed as a method to implement nonlinear optimal iterative learning control algorithm. In the method, more priori information was coded in a clonal selection algorithm to decrease the size of the search space and to deal with constraint on input. Another clonal selection algorithm is used as a model modifying device to cope with uncertainty in the plant model. Finally, simulations show that the convergence speed is satisfactory regardless of the nature of the plant and whether or not the plant model is precise.     

Adaptive fuzzy ILC of nonlinear discrete-time systems with unknown dead zones and control directions

This paper presents an adaptive fuzzy iterative learning control (ILC) design for non-parametrized nonlinear discrete-time systems with unknown input dead zones and control directions. In the proposed adaptive fuzzy ILC algorithm, a fuzzy logic system (FLS) is used to approximate the desired control signal, and an additional adaptive mechanism is designed to compensate for the unknown input dead zone. In dealing with the unknown control direction of the nonlinear discrete-time system, a discrete Nussbaum gain technique is exploited along the iteration axis and applied to the adaptive fuzzy ILC algorithm. As a result, it is proved that the proposed adaptive fuzzy ILC scheme can drive the ILC tracking errors beyond the initial time instants into a tunable residual set as iteration number goes to infinity, and keep all the system signals bounded in the adaptive ILC process. Finally, a simulation example is used to demonstrate the feasibility and effectiveness of the adaptive fuzzy ILC scheme.     

Robustification of iterative learning control and repetitive control by averaging

This paper describes a recently developed averaging technique to robustify iterative learning and repetitive controllers. The robustified controllers are found by minimising cost functions that are averaged over either multiple analytical time-domain models or experimental frequency-domain data. The aim is to produce a technique that is simple and general, and can be applied to any iterative learning control (ILC) or repetitive control (RC) design that involves the minimisation of a cost function. Substantial improvement in convergence to zero tracking error in the presence of model uncertainties has been observed for both ILC and RC by this averaging technique.     

Adaptive consensus control of nonlinear systems with unknown control directions

In this paper, we investigate the adaptive consensus control for a class of nonlinear systems with different unknown control directions where communications among the agents are represented by a directed graph. Based on the backstepping technique, a fully distributed adaptive control approach is proposed without using global information of the topology. Meanwhile, a novel Nussbaum-type function is proposed to address the consensus control with unknown control directions. It is proved that boundedness of all closed-loop signals and asymptotic consensus tracking for all the agents' outputs are ensured. In simulation studies, a numerical example is illustrated to show the effectiveness of the control scheme.     

Almost sure convergence of iterative learning control for stochastic systems

This paper proposes an iterative learning control (ILC) algorithm with the purpose of controling the output of a linear stochastic system presented in state space form to track a desired realizable trajectory. It is proved that the algorithm converges to the optimal one a.s. under the condition that the product input-output coupling matrices are full-column rank in addition to some assumptions on noises. No other knowledge about system matrices and covariance matrices is required.