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.     

一类新型自学习控制器及其在气动伺服系统中的应用

设计了一种新的学习控制律，它通过沿学 习轴递推辨识学习增益矩阵以改善控制效果，并分别针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噪声干扰及初始误差不为零时学 习控制器收敛性条件，最后把它用于气动伺服系统位置控制，给出了相应仿真结果，结果表 明本文提出的控制算法能达到很高控制精度．     

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。     

不确定性系统的非因果鲁棒学习控制

针对不确定性系统提出一种非因果鲁棒学习控制方法。该学习控制律的非因果学习部分通过标称系统的优化指标得到,鲁棒部分通过设计鲁棒加权来实现。首先,不考虑鲁棒部分的具体形式,推导出标称系统描述的学习控制律的鲁棒收敛性条件;然后,设计与系统不确定性相关的鲁棒加权,由鲁棒收敛性条件得到鲁棒加权的设计原则;最后,通过仿真实验验证了所提出方法的有效性,并分析了不同形式不确定性系统鲁棒设计的保守性。     

可变学习增益的迭代学习控制律

基于迭代学习控制理论提出了一种可变学习增益的迭代学习律,在非线性系统中对期望轨迹进行跟踪,与学习增益不变的迭代学习控制相比较,收敛速度得到很大的提高;通过对其收敛性进行严格的数学证明,得到了迭代学习律收敛的充分条件;在单机无穷大系统中,将该控制律应用于同步发电机的励磁控制,仿真结果表明该控制律的有效性,改善了控制的动态特性,有利于提高电力系统稳定性.     

线性广义系统P型迭代学习控制离散频域收敛性

针对一类线性广义系统,研究其P型迭代学习控制在离散频域中的收敛性态。在离散频域中,对广义系统进行奇异值分解后,利用傅里叶级数系数的性质和离散的Parseval能量等式,推演了一阶P型迭代学习控制律跟踪误差的离散能量频谱的递归关系和特性,获得了学习控制律收敛的充分条件;讨论了二阶P型迭代学习控制律的收敛条件。仿真实验验证了理论的正确性和学习律的有效性。     

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。     

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.     

线性时不变系统PID–型迭代学习控制律的单调收敛形态

传统的迭代学习控制机理中, 积分补偿是典型的策略之一, 但其跟踪效用并不明确. 本文针对连续线性时 不变系统, 对传统的PD–型迭代学习控制律嵌入积分补偿, 利用分部积分法和推广的卷积Young不等式, 在Lebesgue- p范数意义下, 理论分析一阶和二阶PID–型迭代学习控制律的收敛性态. 结果表明, 当比例、积分和导数学习增益满 足适当条件时, 一阶PID–型迭代学习控制律是单调收敛的, 二阶PID–型迭代学习控制律是双迭代单调收敛的. 数值 仿真验证了积分补偿可有效地提高系统的跟踪性能.     

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．     

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.     

现代远程教学系统的设计和实现

介绍了网络化教学及智能化远程学习系统。分析了网络化教学流程及其主要部分的模型。通过典型实例，探索了对具体系统的开发设计，证明了智能化远程学习模型是一个实用的远程学习智能化方案，模型具有可实现性。     

宽度学习系统研究进展

当数据规模庞大时,深度学习模型会遇到权重调整耗时,容易陷入局部最优解的问题.为了解决这些问题,宽度学习系统应运而生,宽度学习系统不仅结构简单、训练速度快、准确率高,而且还具有增量学习的优势.介绍了宽度学习系统的产生背景和发展历程,阐述了宽度学习系统的基础理论与实现方法,对比了它与深度网络的异同;介绍了宽度学习系统在图像分类、数值回归、脑电信号处理等应用中的改进算法,分析了这些算法的优势和不足.最后总结了现有宽度学习算法存在的缺陷,并对未来研究方向进行了展望.     

An online learning algorithm for adaptable topologies of neural networks

Many real scenarios in machine learning are of dynamic nature. Learning in these types of environments represents an important challenge for learning systems. In this context, the model used for learning should work in real time and have the ability to act and react by itself, adjusting its controlling parameters, even its structures, depending on the requirements of the process. In a previous work, the authors presented an online learning algorithm for two-layer feedforward neural networks that includes a factor that weights the errors committed in each of the samples. This method is effective in dynamic environments as well as in stationary contexts. As regards this method’s incremental feature, we raise the possibility that the network topology is adapted according to the learning needs. In this paper, we demonstrate and justify the suitability of the online learning algorithm to work with adaptive structures without significantly degrading its performance. The theoretical basis for the method is given and its performance is illustrated by means of its application to different system identification problems. The results confirm that the proposed method is able to incorporate units to its hidden layer, during the learning process, without high performance degradation.     

基于协同最小二乘支持向量机的Q学习

针对强化学习系统收敛速度慢的问题, 提出一种适用于连续状态、离散动作空间的基于协同最小二乘支持向量机的Q学习. 该Q学习系统由一个最小二乘支持向量回归机(Least squares support vector regression machine, LS-SVRM)和一个最小二乘支持向量分类机(Least squares support vector classification machine, LS-SVCM)构成. LS-SVRM用于逼近状态--动作对到值函数的映射, LS-SVCM则用于逼近连续状态空间到离散动作空间的映射, 并为LS-SVRM提供实时、动态的知识或建议(建议动作值)以促进值函数的学习. 小车爬山最短时间控制仿真结果表明, 与基于单一LS-SVRM的Q学习系统相比, 该方法加快了系统的学习收敛速度, 具有较好的学习性能.     

Mobile Robot Learning by Self-Observation

The aim of the project described in this paper was to investigate robot learning at a most fundamental level. The project focused on the transition between organisms with innate behaviors and organisms that have the most rudimentary capability of learning through their personal interaction with their environment. It was assumed that the innate behaviors gave basic survival competence but no learning ability. By observing the interaction between their innate behaviors and the organism's environment it was reasoned that the organism should be able to learn how to modify its actions in a way that improves its performance. If a learning system is given more information than it requires then, when it is successful, it is difficult to say which pieces of information contribute to the success. For this reason the information available to the learning system was kept to an absolute minimum. In order to provide a practical test of the learning scheme developed in this project, the robot environment EDEN was constructed. Within EDEN a robot's actions influence its internal energy reserves. The environment incorporates sources of energy, and it also involves situations that use additional energy or reduce energy consumption. A successful learning scheme was developed purely based on the recorded history of the robot's interactions with its environment and the knowledge that the robot's innate behavior was reactive. This learning scheme allowed the robot to improve its energy management by exhibiting classical conditioning and a restricted form of operant conditioning.     

Parallel Reinforcement Learning: A Framework and Case Study

In this paper, a new machine learning framework is developed for complex system control, called parallel reinforcement learning. To overcome data deficiency of current data-driven algorithms, a parallel system is built to improve complex learning system by self-guidance. Based on the Markov chain (MC) theory, we combine the transfer learning, predictive learning, deep learning and reinforcement learning to tackle the data and action processes and to express the knowledge. Parallel reinforcement learning framework is formulated and several case studies for real-world problems are finally introduced.      

On learning transient,auto-tunings of learnable bandwidth and lead step in iterative learning control

The learning transient and tracking accuracy of phase lead compensation iterative learning control are determined by its three parameters: learning gain, system learnable bandwidth and lead step. Because of the model inaccuracy, the learnable bandwidth is often chosen as a conservative value, which often degrades the learning performance. In this article, the learning transient is analysed and the tuning of learnable bandwidth and lead step are developed to achieve good learning transient and tracking accuracy simultaneously. The attractive properties include that the less dependence on system model and that the tracking error during this process keeps at a very low level. Experimental results on an industrial robot are presented to verify the tuning process.