一类二维变学习因子自学习控制器

设计了一种新的学习控制律,通过沿学习轴递推辨识学习增益矩阵以改善控制效果,分别对连续系统及离散系统设计了学习控制律,给出了相应的收敛性证明结果,同时考虑了系统存在着噪声干扰及初始误差不为零时滓控制器的收敛性条件。仿真结果表明该控制算法能达到很高的控制精度。     

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。     

具有反馈信息的迭代学习控制律在Lebesgue-p范数意义下的收敛性

针对一类线性时不变系统, 提出了具有反馈信息的PD-型(Proportional-derivative-type)迭代学习控制律, 利用卷积的推广的Young不等式, 分析了控制律在Lebesgue-p范数意义下的单调收敛性. 分析表明, 收敛性不但决定于系统的输入输出矩阵和控制律的微分学习增益, 而且依赖于系统的状态矩阵和控制律的比例学习增益; 进一步, 当适当选取反馈增益时, 反馈信息可加快典型的PD-型迭代学习控制律的单调收敛性. 数值仿真验证了理论分析的正确性和控制律的有效性.     

非正则线性系统的闭环P型迭代学习控制

迭代学习控制是改善具有重复运行性质过程的跟踪性能的有效方法。开环迭代学习控制学习周期长，在迭代学习的初期容易出现不稳定和高增益的现象。对非正则系统的迭代学习控制，需要采用高阶微分学习律。该文针对一类非正则线性定常连续系统，讨论了闭环P型迭代学习控制律，给出并证明了闭环P型迭代学习控制律的收敛性条件的两个定理，解决了非正则系统的P型迭代学习控制问题。仿真实例说明闭环迭代学习律的有效性和快速性。     

一类随机系统的输出跟踪

采用直接自适应控制律方法解决多输入单输出随机系统的变目标输出跟踪问题, 同时主要分析了控制律作用下的该系统的稳定性,并给出了输出跟踪的较弱收敛性条件及仿 真实例.     

MIMO非线性系统的直接自适应控制

本文给出模型未知多输入多输出非线性系统的一种动态线性逼近方法，提出了基于该线性化方法的自适应控制律。讨论了在一定假设条件下自适应控制律的收敛性。     

一种稳定的机器人神经网络控制器研究

研究一种稳定的机器人神经网络（ＮＮ）控制器，提出了由神经网络控制器和监督控制器构成的控制方案，给出了控制器的设计方法及ＮＮ学习自适应律，并基于Ｌｙａｐｕｎｏｖ方法证明了控制系统的稳定性和ＮＮ参数收敛性，仿真结果表明该控制方案具有良好的鲁棒性和参数收敛性，从而证明控制器的有效性。     

一种非线性系统自适应控制及其收敛性分析*

本文对基于输入输出随机梯度的非线性系统的控制律进行了收敛性分析，给出了ＳＩＳＯ控制系统收敛的充分条件，并根据该条件给出一种非线性系统自适应控制器的设计方法。     

一类非线性系统时变滑模变结构控制

针对参数不确定性非线系统,分析了在未知扰动下的输出调节问题,提出了基于李亚普诺夫稳定性理论意义下的时变滑模变结构控制律,该控制律能保证系统误差的快速收敛性及对外部扰动和参数不确定性的不敏感性,最后给出的仿真实例实证实了理论分析结果的正确性。     

初态学习下的迭代学习控制

提出一种新的初态学习律，以放宽常规迭代学习控制方法的初始定位条件．它允许一定的定位误差，在迭代中不需要定位在某一具体位置上，使得学习控制系统具有鲁棒收敛性．针对二阶LTI系统，给出了输入学习律及初态学习律的收敛性充分条件．依据收敛性条件，学习增益的选取需系统矩阵的估计值，但在一定建模误差下，仍能保证算法的收敛性．所提出的初态学习律本身及其收敛性条件均与输入矩阵无关．     

Online learning control by association and reinforcement

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.     

Dynamically focused fuzzy learning control

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.     

Iterative learning control — 2D control systems from theory to application

It has long been recognized that iterative learning control is a 2D system, i.e. information propagation occurs in two independent directions. In this paper, the application of so-called norm optimal iterative learning control, which has its origins in the theory of the class of 2D systems known as linear repetitive processes to an experimental testbed in the form of a chain conveyor system is reported. This includes the motivation for applying iterative learning control to such systems, the design and construction of the testbed, and its use to demonstrate that norm optimal iterative learning control gives superior performance over alternatives. As such, it provides an application for 2D systems theory where distinct advantages arise from using such a setting for modelling and control.     

基于强化学习的二级倒立摆控制

在模型未知和没有先验经验的条件下，采用一种改进的强化学习算法实现二级倒立摆系统的平衡控制。该学习算法不需要预测和辨识模型，能通过网络自身的联想和记忆，在线寻求最优策略。该学习算法采用基于神经网络的值函数逼近，并用直接梯度和适合度轨迹修正权值，有效实现对连续状态和行为空间任务的控制。计算机仿真证明了该强化学习算法在较短的时间内即可成功地学会控制直线二级倒立摆系统。     

An iterative learning control theory for a class of nonlinear dynamic systems

An iterative learning control scheme is presented for a class of nonlinear dynamic systems which includes holonomic systems as its subset. The control scheme is composed of two types of control methodology: a linear feedback mechanism and a feedforward learning strategy. At each iteration, the linear feedback provides stability of the system and keeps its state errors within uniform bounds. The iterative learning rule, on the other hand, tracks the entire span of a reference input over a sequence of iterations. The proposed learning control scheme takes into account the dominant system dynamics in its update algorithm in the form of scaled feedback errors. In contrast to many other learning control techniques, the proposed learning algorithm neither uses derivative terms of feedback errors nor assumes external input perturbations as a prerequisite. The convergence proof of the proposed learning scheme is given under minor conditions on the system parameters.     

基于强化学习的大时延过程控制策略研究

具有长时延的过程控制被公认为是较难的系统过程控制。模型预测控制(MPC)是一种适用于大时延过程的新的过程控制方法。相比于PID等传统的控制方法,MPC基于模型对未来状态的预测进行决策,能够兼顾及时反馈与长期规划。但MPC对于过程的预测步数依然是有限的。强化学习作为机器学习的重要部分,原则上能够预测策略在无限长时间内的收益。作者基于强化学习方法改进混凝剂添加过程中的控制算法,利用大量仿真数据训练模型,成功提升了该过程的控制效果。通过对该方法进行仿真模拟,并与传统的MPC方法进行对比,证明了使用强化学习改进过的控制方法在大时延过程控制中的总体表现优于传统MPC方法。     

非线性滞后离散系统的学习控制算法

讨论了滞后非线性离散系统的学习控制问题,由于所给的学习算法及学习控制过程 中,没有涉及和用到相应于理想输出yd的理想输入ud及对应于系统的理想状态xd,故对被 控对象的动力学信息要求得很少,只是一种定性上的Lipschitz条件.所给出的控制算法不仅 收敛,而且也保证了对期望目标在通常意义下的跟踪(而不是像目前有些结果那样,只是跟踪 到期望目标的某一个邻域范围内).而且这些算法还以目前一些通常的算法为特例.     

迭代学习在网络控制中的应用*

针对网络拥塞控制中网络拥塞本身无法建立精确的数学模型的问题,基于迭代学习控制具有结构简单及对系统精确模型不依赖等优点,首次提出了用迭代学习控制算法来解决网络拥塞,其主要目的是提高网络资源的利用率并提供给信源公平的资源分配份额。在提出算法前,首先通过分析网络模型建立了网络拥塞被控系统;然后提出了针对该被控系统的开闭环PID型迭代学习控制算法并证明了其收敛性;最后运用此算法建立了网络拥塞控制模型。通过实验和仿真表明,该算法对解决网络拥塞问题有很好的效果。     

Learning from NN output feedback control of nonlinear systems in Brunovsky canonical form

In this paper, we investigate the learning issue in the adaptive neural network (NN) output feedback control of nonlinear systems in Brunovsky canonical form with unknown affine term. With only output measurements, a high-gain observer (HGO) is employed to estimate the derivatives of the system output which may be associated with the generation of peaking phenomenon. The adverse effect of peaking on learning and its elimination strategies are analyzed. When the gain of HGO is chosen too high, it may cause the failure of learning from the unknown closed-loop system dynamics. Hence, the gain of HGO is not chosen too high to relieve peaking and guarantee the accuracy of the estimated system states. Then, learning from the unknown closed-loop system dynamics can be achieved. When repeating the same or similar control tasks, a neural learning controller is presented which can effectively recall and reuse the learned knowledge to guarantee the output tracking performance. Finally, simulation results demonstrate the effectiveness of the proposed scheme.     

基于FBFN 的鲁棒仿生学习系统设计及在运动平衡控制中的应用

针对两轮直立式机器人的运动平衡控制问题,结合OCPA 仿生学习系统,基于模糊基函数,设计了一 种鲁棒仿生学习控制方案．它不需要动力学系统的先验知识,也不需要离线的学习阶段．鲁棒仿生学习控制器主要 包括仿生学习单元、增益控制单元和鲁棒自适应单元3 部分．仿生学习单元由模糊基函数网络（FBFN）实现,FBFN 不仅执行操作行为产生功能,逼近动力学系统的非线性部分,同时也执行操作行为评价功能,并利用性能测量机制 提供的误差测量信号,产生取向值信息,对操作行为产生网络进行调整．增益控制单元的作用是确保系统的稳定性 和性能,鲁棒自适应单元的作用是消除FBFN 的逼近误差及外部干扰．此外,由于FBFN 的参数是基于李亚普诺夫 稳定性理论在线调整的,因此进一步确保了系统的稳定性和学习的快速性．理论上证明了鲁棒仿生学习控制器的稳 定性,仿真实验结果验证了其可行性和有效性．