共查询到17条相似文献,搜索用时 262 毫秒
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针对带未知时变参数的非线性多智能体系统的编队问题,提出一种分布式自适应迭代学习控制策略。首先,通过傅里叶级数对系统的不确定参数进行展开,采用一个收敛级数序列处理傅里叶级数展开产生的截断误差,结合多智能体运行过程中的编队误差推导自适应迭代学习控制律和参数更新律;其次,针对领导者动态对大部分智能体都是未知的情况,设计新的辅助控制来补偿未知动态和避免未知有界干扰;然后,基于李亚普诺夫能量函数证明了在所设计控制律作用下多智能体系统编队误差随着迭代次数的增加在有限时间内趋于0;最后,将该控制策略运用到多无人机编队系统中,并通过搭建半物理实验平台,验证了控制方法的有效性。实验结果表明该控制方法可以确保多智能体快速形成所需编队,并且每个智能体在有限时间内可以精确跟踪期望轨迹。所提方法充分考虑了多智能体系统的参数不确定性以及抗干扰的能力,为实际应用中复杂多智能体系统的精确控制提供了有效的方法。 相似文献
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针对机械臂这类非线性的不确定性系统,基于迭代学习控制与滑模控制策略,提出了一种有效的迭代滑模控制方法。该控制方法通过将滑模控制律引入到迭代学习控制中,并运用Lyapunov理论对控制律进行证明,从而确保系统的稳定性。基于拉格朗日力学法建立动力学模型,得到相对简化的n关节机械臂模型。以一个二关节机械臂为例,通过MATLAB仿真验证所提控制策略可有效提高关节的跟踪速度与跟踪精度,并且在一定程度上可减缓传统滑模控制的抖振现象,与传统迭代学习控制相比,系统具有鲁棒性。物理试验验证了所提控制策略的有效性。 相似文献
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提出一种有限长迭代学习控制策略,用来减小扫描光刻系统的预扫描时间.首先给出扫描光刻系统的结构和扫描轨迹,并提取系统在扫描方向的运动控制模型;其次通过闭环整定得到系统的脉冲响应系数,并基于名义系统的优化指标进行离线运算,得到非因果迭代学习控制律的学习系数.为减小迭代学习控制律对扫描周期的依赖性,以学习矩阵的主对角线为中心,提取截短的有效学习系数,构成有限长脉冲响应学习控制律.此外还给出了有限长学习控制律的收敛性证明,并对学习控制律的鲁棒性进行了讨论.最后将上述方法应用于扫描光刻实验平台,实验结果表明:有限长迭代学习控制律可以有效地减小扫描光刻系统的预扫描时间. 相似文献
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对于可控励磁磁悬浮直线同步电机(CEMLLSM),常规迭代学习控制(ILC)精度低、抖振大,且抗外部扰动能力差。为提高跟踪精度,设计了一种基于扩张状态观测器(ESO)的变增益自适应ILC算法。首先,研究CEMLLSM的工作原理及数学模型。其次,设计基于ESO的变增益自适应迭代学习控制器,为控制器中固定增益部分引入指数可变增益,增加自适应迭代项对控制律中的未知参数进行迭代学习,从而减小系统抖振与误差并加快系统收敛速度。通过引入ESO观测系统的外部干扰,对控制量进行补偿,进而提高系统的抗扰动能力。最后,用MATLAB对控制系统进行仿真分析,仿真结果表明该算法能够有效减小跟踪误差,并对扰动有良好的抑制作用。 相似文献
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无速度传感器控制技术以其便捷可靠的优势,广泛应用于电力牵引交流调速系统中,为系统的控制带来了种种便利。在详细分析了几种不同控制策略对系统的影响基础上,针对传统滑模观测器存在抖振的现象,提出基于改进型指数趋近律的模糊滑模控制策略。该方法将转速的误差变量引入电流估算模型中,并对转速反馈信息进行模糊逻辑控制,结合Lyapunov函数设计出转速的自适应律,最后在Simulink仿真平台上验证方案的可行性。结果表明,该方案不但保留了传统滑模优势,而且有效地降低了滑模抖振的影响,满足交流调速系统的要求。 相似文献
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Xingyu Zhou Haoping Wang Yang Tian Gang Zheng 《International Journal of Adaptive Control and Signal Processing》2020,34(9):1220-1241
In this article, an observer-based adaptive boundary iterative learning control law is developed for a class of two-link rigid-flexible manipulator with input backlash, the unknown external disturbance, and the endpoint constraint. To tackle the backlash nonlinearities and ensure the vibration suppression, the disturbance observers based upon the iterative learning conception are considered in the adaptive boundary control design. A barrier Lyapunov function is incorporated with boundary control law to restrict the endpoint state. Based on the defined barrier composite energy function, the tracking angle error convergence of the rigid part is guaranteed, and the vibrations of the flexible part are suppressed through the rigorous analysis. Finally, a numerical simulation is provided to illustrate the effectiveness of the proposed control. 相似文献
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Zhijie Liu Jinkun Liu Wei He 《International Journal of Adaptive Control and Signal Processing》2017,31(6):903-916
In this study, we consider the boundary control problem of a flexible manipulator in the presence of system parametric uncertainty and external disturbances. The dynamic behavior of the flexible manipulator is represented by partial differential equations (PDEs). Based on the Lyapunov method, we propose an adaptive iterative learning control scheme for trajectory tracking and vibration suppressing of a flexible manipulator. The proposed control scheme is designed using both a proportional‐derivative feedback structure and an iterative term. The learning convergence of iterative learning control is achieved through rigorous analysis without any simplification or discretization of the PDE dynamics. Finally, the results are illustrated using numerical simulations for control performance verification. Copyright © 2016 John Wiley & Sons, Ltd. 相似文献
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混合有源电力滤波器可以动态抑制电网谐波电流和补偿容性无功功率,改善电网电能质量。针对传统PI型迭代学习控制算法在并联有源电力滤波器应用中的不足,算法收敛性严重依赖于学习控制的初始输入,迭代学习控制器的参数是定常值,会影响有源滤波系统的控制性能。本文提出一种新型PI迭代学习控制算法,将其应用于混合有源电力滤波器系统的电流反馈控制中,得到了应用迭代算法的收敛性条件,并采用一种改进的Ziegler-Nichols方法对控制器参数进行了优化,以提高系统的控制精度。为了提高系统的动态响应性能,提出一种谐波电流误差的反馈-前馈控制策略,其中电流误差信号的D型前馈控制环节用于实现滤波器系统的电流快速补偿,同时利用一个三层BP神经网络对前馈控制增益进行优化。仿真和实验结果证明了该迭代算法及控制策略的可行性与有效性。 相似文献
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Yan Zhou Heng Liu Jinde Cao Shenggang Li 《International Journal of Adaptive Control and Signal Processing》2019,33(12):1739-1758
This paper presents a composite learning fuzzy control to synchronize two different uncertain incommensurate fractional‐order time‐varying delayed chaotic systems with unknown external disturbances and mismatched parametric uncertainties via the Takagi‐Sugeno fuzzy method. An adaptive controller together with fractional‐order composite learning laws is designed based on both a parallel distributed compensation technology and a fractional Lyapunov criterion. The boundedness of all variables in the closed‐loop system and the Mittag‐Leffler stability of tracking error can be guaranteed. T‐S fuzzy systems are provided to tackle unknown nonlinear functions. The distinctive features of the proposed approach consist in the following: (1) a supervisory control law is designed to compensate the lumped disturbances; (2) both the prediction error and the tracking error are used to estimate the unknown fuzzy system parameters; (3) parameter convergence can be ensured by an interval excitation condition. Finally, the feasibility of the proposed control strategy is demonstrated throughout an illustrative example. 相似文献
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以电子凸轮为研究对象,提出一种轨迹跟踪控制方法。阐述了电子凸轮运动规律,在此基础上基于3次非均匀B样条曲线给出了一种凸轮曲线设计方法。为提高凸轮轨迹跟踪精度,设计了模糊滑模迭代控制器。迭代学习控制算法可用于实现目标轨迹的跟踪;模糊控制和滑模控制则可以提高电子凸轮的收敛速度与鲁棒性。滑模控制器处理轨迹偏差及其变化率;模糊控制器对滑模输出进行模糊化和解模糊化处理;通过实时控制调节迭代学习控制器的增量得到理想的轨迹跟踪效果。针对基于伺服电机的电子凸轮,控制系统给出了具体硬件架构。通过实验验证表明,模糊滑模迭代控制算法能够满足电子凸轮对轨迹跟踪精度与鲁棒性的要求,电子凸轮能够有效取代传统的机械凸轮机构。 相似文献
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Mouhacine Benosman Amir‐Massoud Farahmand Meng Xia 《International Journal of Adaptive Control and Signal Processing》2019,33(2):335-355
In this paper, we study the problem of adaptive trajectory tracking control for a class of nonlinear systems with structured parametric uncertainties. We propose to use an iterative modular approach: we first design a robust nonlinear state feedback that renders the closed‐loop input‐to‐state stable (ISS). Here, the input is considered to be the estimation error of the uncertain parameters, and the state is considered to be the closed‐loop output tracking error. Next, we propose an iterative adaptive algorithm, where we augment this robust ISS controller with an iterative data‐driven learning algorithm to estimate online the parametric uncertainties of the model. We implement this method with two different learning approaches. The first one is a data‐driven multiparametric extremum seeking method, which guarantees local convergence results, and the second is a Bayesian optimization‐based method called Gaussian Process Upper Confidence Bound, which guarantees global results in a compact search set. The combination of the ISS feedback and the data‐driven learning algorithms gives a learning‐based modular indirect adaptive controller. We show the efficiency of this approach on a two‐link robot manipulator numerical example. 相似文献