Production Control of a Manufacturing System with Random Machine Breakdowns

ＰｒｏｄｕｃｔｉｏｎＣｏｎｔｒｏｌｏｆａＭａｎｕｆａｃｔｕｒｉｎｇＳｙｓｔｅｍｗｉｔｈＲａｎｄｏｍＭａｃｈｉｎｅＢｒｅａｋｄｏｗｎｓＣＨＡＯＸｉｕｌｉ（ＤｅｐａｒｔｍｅｎｔｏｆＩｎｄｕｓｔｒｉａｌａｎｄＭａｎｕｆａｃｔｕｒｉｎｇＥｎｇｉｎｅｅｒｉｎｇ...     

A Continuous Minimization Method for Solving Optimal Control Problem

ＡＣｏｎｔｉｎｕｏｕｓＭｉｎｉｍｉｚａｔｉｏｎＭｅｔｈｏｄｆｏｒＳｏｌｖｉｎｇＯｐｔｉｍａｌＣｏｎｔｒｏｌＰｒｏｂｌｅｍＡＣｏｎｔｉｎｕｏｕｓＭｉｎｉｍｉｚａｔｉｏｎＭｅｔｈｏｄｆｏｒＳｏｌｖｉｎｇＯｐｔｉｍａｌＣｏｎｔｒｏｌＰｒｏｂｌｅｍ￥ＧＵＲｏ...     

A Novel Approach to Combination of Forecasts

ＡＮｏｖｅｌＡｐｐｒｏａｃｈｔｏＣｏｍｂｉｎａｔｉｏｎｏｆＦｏｒｅｃａｓｔｓＧＡＯＲｅｎｘｉａｎｇＩｎｓｔｉｔｕｔｅｏｆＡｐｐｌｉｅｄＭａｔｈｅｍａｔｉｃｓ，ＡｃａｄｅｍｉａＳｉｎｉｃａ，Ｂｅｉｊｉｎｇ．１Ｏ００８０，ＣｈｉｎａＺＨＡＮＧＳｈｉｙｉｎ...     

ON CONVERGENCE AND RATE OF CONVERGENCE OF ALGORITHMS IN THE MOVER－STAYER MODEL

ＯＮＣＯＮＶＥＲＧＥＮＣＥＡＮＤＲＡＴＥＯＦＣＯＮＶＥＲＧＥＮＣＥＯＦＡＬＧＯＲＩＴＨＭＳＩＮＴＨＥＭＯＶＥＲ－ＳＴＡＹＥＲＭＯＤＥＬＷＡＮＧＤｏｎｇｑｉａｎ；Ｌａｗｏｋｏ，Ｃ．Ｒ．Ｏ．（ＤｅｐａｒｔｍｅｎｔｏｆＳｔａｔｉｓｔｉｃｓ，ＭａｓｓｅｙＵｎ...     

IMPROVEMENT FOR FERRO''''S MINIMAX THEOREM

ＩＭＰＲＯＶＥＭＥＮＴＦＯＲＦＥＲＲＯ＇ＳＭＩＮＩＭＡＸＴＨＥＯＲＥＭ￥ＣＨＥＮＧｕａｎｇｙａ（ＩｎｓｔｉｔｕｔｅｏｆＳｙｓｔｅｍｓＳｃｉｅｎｃｅ，ＡｃａｄｅｍｉａＳｉｎｉｃａ，Ｂｅｉｊｉｎｇ１０００８０，Ｃｈｉｎａ）Ａｂｓｔｒａｃｔ：Ｔｈｉｓｎｏｔ...     

ON THE ISOMORPHISMS OF SMOOTH ALGEBRAIC CURVES (Ⅰ)

ＯＮＴＨＥＩＳＯＭＯＲＰＨＩＳＭＳＯＦＳＭＯＯＴＨＡＬＧＥＢＲＡＩＣＣＵＲＶＥＳ（Ⅰ）ＤＵＨｏｎｇ（ＩｎｓｔｉｔｕｔｅｏｆＳｙｓｔｅｍｓＳｃｉｅｎｃｅ，ＡｃａｄｅｍｉａＳｉｎｉｃａ，Ｂｅｉｊｉｎｇ１０００８０，Ｃｈｉｎａ）Ａｂｓｔｒａｃｔ：Ｉｎｔｈｉ...     

A Genetic Algorithm for Optimal Design of Model Output Following Control

ＡＧｅｎｅｔｉｃＡｌｇｏｒｉｔｈｍｆｏｒＯｐｔｉｍａｌＤｅｓｉｇｎｏｆＭｏｄｅｌＯｕｔｐｕｔＦｏｌｌｏｗｉｎｇＣｏｎｔｒｏｌ￥ＺＨＡＮＧＸｉａｏｊｕｎ；ＹＡＭＡＮＥＹｕｚｏ（ＡｓｈｉｋａｇａＩｎｓｔｉｔｕｔｅｏｆＴｅｃｈｎｏｌｏｇｙ，Ａｓｈｉｋａｇａ...     

A METHOD FOR MECHANICAL GEOMETRY THEOREM PROVING

ＡＭＥＴＨＯＤＦＯＲＭＥＣＨＡＮＩＣＡＬＧＥＯＭＥＴＲＹＴＨＥＯＲＥＭＰＲＯＶＩＮＧ￥ＷＵＪｉｎｚｈａｏ（ＩｎｓｔｉｔｕｔｅｏｆＳｙｓｔｅｍｓＳｃｉｅｎｃｅ，ＡｃａｄｅｍｉａＳｉｎｉｃａ，Ｂｅｉｊｉｎｇ１０００８０，Ｃｈｉｎａ）ＴＡＮＨｏｎｇｙａｎ（...     

CHAOS IN TOPOLOGICAL MARKOV CHAINS

ＣＨＡＯＳＩＮＴＯＰＯＬＯＧＩＣＡＬＭＡＲＫＯＶＣＨＡＩＮＳ￥ＣＡＯＬｉａｎｇｙｕｅ；ＷＵＳｈｉｊｉｎｇ；ＺＨＡＯＨａｎｚｈａｎｇ（ＩｎｓｔｉｔｕｔｅｏｆＳｙｓｔｅｍｓＳｃｉｅｎｃｅ，ＡｃａｄｅｍｉａＳｉｎｉｃａ，Ｂｅｉｊｉｎｇ１０００８０，Ｃｈｉｎ...     

ON THE ISOMORPHISMS OF COMPLETE INTERSECTION VARIETIES

ＯＮ　ＴＨＥ　ＩＳＯＭＯＲＰＨＩＳＭＳ　ＯＦ　ＣＯＭＰＬＥＴＥＩＮＴＥＲＳＥＣＴＩＯＮ　ＶＡＲＩＥＴＩＥＳＤＵＨｏｎｇ（ＩｎｓｔｉｔｕｔｅｏｆＳｙｓｔｅｍｓＳｃｉｅｎｃｅ，ＡｃａｄｅｍｉａＳｉｎｉｃａ，Ｂｅｉｊｉｎｇ１０００８０，Ｃｈｉｎａ）Ａｂｓｔ...     

一类非线性系统的迭代学习控制

在一类非线性系统的闭环迭代学习控制中，对系统输入进行闭环D型迭代学习，对输入的初值采用P型迭代学习控制律，给出了它的收敛性证明，从收敛性的证明过程可以看出，这种迭代学习控制受到较小的收敛条件的限制最后用仿真结果对收敛性和收敛条件进行了验证。     

未知非线性系统的神经网络跟踪控制与仿真研究

应用输入／输出反馈线性化方法和李亚普诺夫方法，研究了一类具有未知非线性函数的非线性动态系统的自适应鲁棒输出跟踪控制问题。首先通过坐标变换和输入变换，将非线性系统变换为部分线性可控系统。接着采用多层前向神经网络来逼近未知非线性函数，网络的权值根据李亚普诺夫原则来在线修正，这样就克服了多神经网络控制系统中存在的稳定性问题。同时，为了减少权值学习时间，应用遗传算法预先离线训练网络权值。最后提出了一个基于神经网络建模的自适应鲁棒控制律，给出了李亚普诺夫意义下的稳定性证明。所提出的控制律可确保相应闭环系统的状态及跟踪误差一致最终有界。所给的Van der pol系统的例子说明了所提控制方案的有效性与鲁棒性。     

A Novel Evolutionary-Fuzzy Control Algorithm for Complex Systems

A Novel Evolutionary-Fuzzy Control Algorithm for Complex Systems~~~~     

Networked Learning Predictive Control of Nonlinear Cyber-Physical Systems

Cyber-physical systems integrate computing, network and physical environments to make the systems more efficient and cooperative, and have important and extensive application prospects, such as the Internet of things. This paper studies the control problem of nonlinear cyber-physical systems with unknown dynamics and communication delays. A networked learning predictive control scheme is proposed for unknown nonlinear cyber-physical systems. This scheme recursively learns unknown system dynamics, actively compensates for communication delays and accurately tracks a desired reference. Learning multi-step predictors are presented to predict various step ahead outputs of the unknown nonlinear cyber-physical systems. The optimal design of controllers minimises a performance cost function which measures the tracking error predictions and control input increment predictions. The system analysis leads to the stability criteria of closed-loop nonlinear cyber-physical systems employing the networked learning predictive control scheme. An example illustrates the outcomes of the proposed scheme.

     

一类高阶非线性系统的混合自适应重复学习控制

针对含有时变和时不变未知参数的高阶非线性系统,利用分段积分机制,提出了一种新的自适应重复学习控制方法,该方法结合了反馈线性化,可以处理参数在一个未知紧集内周期性快时变的非线性系统,通过引进微分-差分参数自适应律,设计了一种自适应控制策略,使广义跟踪误差在误差平方范数意义下渐近收敛于零,通过构造Lyapunov函数,给出了闭环系统收敛的一个充分条件.实例仿真结果说明了该方法的有效性和可行性.     

不确定非线性系统变结构模糊自适应鲁棒控制

考虑一类不确定非线性系统的鲁棒控制器设计问题，在假设系统不确定性函数结构未知的情况下，利用模糊系统对不确定性函数进行逼近，将获得的模糊系统函数作为系统不确定性界函数。在此基础上，提出了一种变结构模糊自适应鲁棒控制算法，该算法在确保系统的滑动模态超平面为渐近可达的前提下，在线对系统模型的控制增益参数和不确定性函数的界参数进行估计。最后以船舶减摇鳍非线性控制系统为例，进行了变结构鲁棒自适应模糊控制器的设计。仿真研究表明，所提出的算法是有效的。     

Adaptive adjustment of iterative learning control gain matrix in harsh noise environment

For the robustness problem of open-loop P-type iterative learning control under the influence of measurement noise which is inevitable in actual systems, an adaptive adjustment algorithm of iterative learning nonlinear gain matrix based on error amplitude is proposed and two nonlinear gain functions are given. Then with the help of Bellman-Gronwall lemma, the robustness proof is derived. At last, an example is simulated and analyzed. The results show that when there exists measurement noise, the proposed learning law adjusts the learning gain matrix on line based on error amplitude, thus can make a compromise between learning convergence rate and convergence accuracy to some extent: the fast convergence rate is achieved with high gain in initial learning stage, the strong robustness and high convergence accuracy are achieved at the same time with small gain in the end learning stage, thus better learning results are obtained.     

基于鲁棒控制Lyapunov函数的非线性自适应跟踪

研究了一类带有未知参数以及扰动的多输入非线性系统的自适应跟踪问题，利用控制Lyapunov方法设计出一种自适应控制器。该控制器对系统的参数和状态的不确定性具有鲁棒性，并且能保证系统闭环全局稳定。仿真结果表明，该控制方案具有良好的跟踪性能、鲁棒性及参数辨识能力。     

推广匹配非线性系统变结构鲁棒自适应控制

对于一类不确定非线性系统 ,利用推广匹配条件对系统进行反馈线性化 ,将系统变换成一个具有n-2个积分链和两个含有未知参数和不确定项的方程的非线性系统。在利用极点配置法给出鲁棒自适应滑动模态超平面设计的基础上 ,提出了变结构鲁棒自适应控制律的设计方法 ,并将所提出的设计方法应用于下摆加驱动电机系统的控制律。经仿真研究表明 ,控制效果令人满意。     

OUTPUT FEEDBACK CONTROL FOR MIMO NONLINEAR SYSTEMS WITH EXOGENOUS SIGNALS

The paper addresses the global output tracking of a class of multi-input multi-output （MIMO） nonlinear systems affected by disturbances, which are generated by a known exosystem. An adaptive controller is designed based on the proposed observer and the backstepping approach to asymptotically track arbitrary reference signal and to guarantee the boundedness of all the signals in the closed loop system. Finally, the numerical simulation results illustrate the effectiveness of the ProPosed scheme.