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非线性离散系统的迭代学习控制方法及其应用* 总被引:5,自引:1,他引:5
本文根据误差收敛准则,提出了非线性离散系统的迭代学习算法,给出了PID型学习控制的收敛条件,并证明了PID型学习控制对预定轨线的逼近特性,在交流变频电机起动过程控制中应用表明,使用本文方法可以得到实用的结果。 相似文献
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提出了一种基于规则和学习算法设计的电力系统智能PID控制器的设计方法。通过对固定参数电力系统PID控制器性能的研究,验证并获得了一些关于电力系统电压和稳定性控制协调与鲁棒性的结论。在此基础上,研制出一种智能PID控制器,它由基于规则的开关控制和基于学习控制的算法组成。在单机无穷大电力系统中应用的非线性仿真表明,这种智能PID控制器满足电力系统电压和稳定性协调控制的要求,且具有较强的鲁棒性。 相似文献
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针对基于固定增益迭代学习的交通子区边界控制方法收敛速度慢、迭代次数过多及控制精度差的问题。提出了一种迭代学习结合改进狼群算法的交通子区边界控制方案。该方案首先根据宏观基本图理论建立交通子区路网的车辆平衡方程,设计出系统的迭代学习控制律。其次分析了迭代学习控制对宏观基本图的影响,引入自适应步长的狼群算法,该算法以上一批次的宏观基本图为模型,离线对迭代学习控制器的比例和微分增益系数进行寻优,再将最优结果代入下一控制周期迭代学习控制中,进而改善收敛速度与精度。最后,对该方案的收敛性提供了数学证明,而仿真实验结果也表明该算法相较于具有固定增益的迭代学习控制器,收敛速度得到提升,对系统期望轨迹也具有较好的跟踪精度,具有较强的可行性与有效性。 相似文献
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针对城市交通信号控制中如何有效利用相关信息优化交通控制并保证控制算法的适应性和鲁棒性的问题,提出一种基于深度强化学习的交通信号控制算法,利用深度学习网络构造一个智能体来控制整个区域交通。首先通过连续感知交通环境的状态来选择当前状态下可能的最优控制策略,环境的状态由位置矩阵和速度矩阵抽象表示,矩阵表示法有效地抽象出环境中的主要信息并减少了冗余信息;然后智能体以在有限时间内最大化车辆通行全局速度为目标,根据所选策略对交通环境的影响,利用强化学习算法不断修正其内部参数;最后,通过多次迭代,智能体学会如何有效地控制交通。在微观交通仿真软件Vissim中进行的实验表明,对比其他基于深度强化学习的算法,所提算法在全局平均速度、平均等待队长以及算法稳定性方面展现出更好的结果。其中,与基线相比,平均速度提高9%,平均等待队长降低约13.4%。实验结果证明该方法能够适应动态变化的复杂的交通环境。 相似文献
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迭代学习控制作为智能控制的一个分支,近年来得到了很大的发展,在各个领域都有广泛的运用。为提高迭代学习速度,本文给出了指数变增益加速算法。机器人系统的仿真结果表明,该方法能大大提高学习速度,具有良好的控制性能。 相似文献
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本文针对以往单纯采用迭代学习控制引起的系统初始转矩冲击和收敛时间过长的问题,提出了一种新的基于经验数据的学习控制算法。指出影响系统输出的关键因素是舵机的转速。然后根据这个特点,应用迭代学习控制的经验,设计了一种简单的算法来确定迭代学习控制的初始输入量,再利用神经网络学习算法修正偏差。仿真结果表明基于经验数据的学习控制很好的解决了单纯采用迭代学习控制而引起的初始转矩冲击和收敛时间过长的问题。 相似文献
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本文针对具有迭代初始误差的高相对度线性多变量离散系统,提出了一种P型的迭代学习控制算法.通过将迭代学习控制系统的二维运动过程描述为一维的线性离散系统,证明了该迭代学习控制算法的收敛性及其收敛的充要条件.该迭代学习控制算法通过对系统前次重复运动过程中的输入和跟踪误差信号进行学习,来不断地调整输入量,使得系统在经过一定次数的学习以后,在初始时间点以外的实际输出趋于期望输出.数值仿真结果表明了所提出算法的有效性. 相似文献
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Learning control is an iterative approach to the problem of improving transient behavior for processes that are repetitive in nature. In this article, we present some results on iterative learning control. A complete review of the literature is given first. Then, a general formulation of the problem is given. Next, we present a complete analysis of the learning control problem for the case of linear, time-invariant plants and controllers. This analysis offers: (1) insight into the nature of the solution of the learning control problem by deriving sufficient convergence conditions; (2) an approach to learning control for linear systems based on parameter estimation; and (3) an analysis that shows that for finite-horizon problems it is possible to design a learning control algorithm that converges, with memory, in one step. Finally, a time-varying learning controller is given for controlling the trajectory of a nonlinear robot manipulator. A brief simulation example is presented to illustrate the effectiveness of this scheme. 相似文献
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针对注射过程具有重复运行和非线性的特性,在对预测控制与迭代学习控制进行综合应用并加以改进的基础上,给出一种模型预测迭代学习复合控制新算法,研究了控制器的设计方案.同时,将迭代学习思想引入到预测步长的在线调整,提出了预测步长的迭代学习方法.仿真结果表明,该方法是有效的,其控制性能优于PID迭代学习控制系统. 相似文献
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Yu-Ping Tian Author Vitae 《Automatica》2003,39(11):1957-1966
This paper addresses the robust learning control problem for a class of nonlinear systems with structured periodic and unstructured aperiodic uncertainties. A recursive technique is proposed which extends the backstepping idea to the robust repetitive learning control systems. A learning evaluation function instead of a Lyapunov function is formulated as a guideline for derivation of the control strategy which guarantees the asymptotic stability of the tracking system. A design example is given. 相似文献
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对于非线性迭代学习控制问题,提出基于延拓法和修正Newton法的具有全局收敛性的迭代学习控制新方法.由于一般的Newton型迭代学习控制律都是局部收敛的,在实际应用中有很大局限性.为拓宽收敛范围,该方法将延拓法引入迭代学习控制问题,提出基于同伦延拓的新的Newton型迭代学习控制律,使得初始控制可以较为任意的选择.新的迭代学习控制算法将求解过程分成N个子问题,每个子问题由换列修正Newton法利用简单的递推公式解出.本文给出算法收敛的充分条件,证明了算法的全局收敛性.该算法对于非线性系统迭代学习控制具有全局收敛和计算简单的优点. 相似文献
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Satoshi Shiba Masafumi Uchida Akio Nozawa Hirotoshi Asano Hitoshi Onogaki Tota Mizuno Hideto Ide Syuichi Yokoyama 《Artificial Life and Robotics》2009,14(2):213-218
A modular robot can be built with a shape and function that matches the working environment. We developed a four-arm modular
robot system which can be configured in a planar structure. A learning mechanism is incorporated in each module constituting
the robot. We aim to control the overall shape of the robot by an accumulation of the autonomous actions resulting from the
individual learning functions. Considering that the overall shape of a modular robot depends on the learning conditions in
each module, this control method can be treated as a dispersion control learning method. The learning object is cooperative
motion between adjacent modules. The learning process proceeds based on Q-learning by trial and error. We confirmed the effectiveness
of the proposed technique by computer simulation. 相似文献
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Dynamically focused fuzzy learning control 总被引:1,自引:0,他引:1
Kwong W.A. Passino K.M. 《IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics》1996,26(1):53-74
A "learning system" possesses the capability to improve its performance over time by interacting with its environment. A learning control system is designed so that its "learning controller" has the ability to improve the performance of the closed-loop system by generating command inputs to the plant and utilizing feedback information from the plant. Learning controllers are often designed to mimic the manner in which a human in the control loop would learn how to control a system while it operates. Some characteristics of this human learning process may include: (i) a natural tendency for the human to focus their learning by paying particular attention to the current operating conditions of the system since these may be most relevant to determining how to enhance performance; (ii) after learning how to control the plant for some operating condition, if the operating conditions change, then the best way to control the system may have to be re-learned; and (iii) a human with a significant amount of experience at controlling the system in one operating region should not forget this experience if the operating condition changes. To mimic these types of human learning behavior, we introduce three strategies that can be used to dynamically focus a learning controller onto the current operating region of the system. We show how the subsequent "dynamically focused learning" (DFL) can be used to enhance the performance of the "fuzzy model reference learning controller" (FMRLC) and furthermore we perform comparative analysis with a conventional adaptive control technique. A magnetic ball suspension system is used throughout the paper to perform the comparative analyses, and to illustrate the concept of dynamically focused fuzzy learning control. 相似文献
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A composite energy function-based learning control approach for nonlinear systems with time-varying parametric uncertainties 总被引:4,自引:0,他引:4
Jian-Xin Xu Ying Tan 《Automatic Control, IEEE Transactions on》2002,47(11):1940-1945
A new learning control approach is developed in this note to address a class of nonlinear systems with time-varying parametric uncertainties. The concept of composite energy function (CEF), which provides the system information along both time and learning repetition horizons, is introduced in the analysis of learning control. CEF consists of two parts. The first part is a standard Lyapunov function,. which is used to access system behavior along time horizon during each learning cycle. The second part is an L/sup 2/ norm of parametric learning errors which reflects the variation of the system status when the control system is updated on the basis of learning cycles. The proposed learning control algorithm achieves asymptotical convergence along a learning repetition horizon. At the same time, the boundedness and pointwise convergence of the tracking error along time horizon is guaranteed. The proposed learning control strategy is applicable to quite general classes of nonlinear systems without requiring the global Lipschitz continuity condition and zero relative degree condition. 相似文献
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Online learning control by association and reinforcement 总被引:4,自引:0,他引:4
This paper focuses on a systematic treatment for developing a generic online learning control system based on the fundamental principle of reinforcement learning or more specifically neural dynamic programming. This online learning system improves its performance over time in two aspects: 1) it learns from its own mistakes through the reinforcement signal from the external environment and tries to reinforce its action to improve future performance; and 2) system states associated with the positive reinforcement is memorized through a network learning process where in the future, similar states will be more positively associated with a control action leading to a positive reinforcement. A successful candidate of online learning control design is introduced. Real-time learning algorithms is derived for individual components in the learning system. Some analytical insight is provided to give guidelines on the learning process took place in each module of the online learning control system. 相似文献