Chen系统的自适应追踪控制

利用非线性单输入控制器实现混沌Chen系统的自适应追踪控制.根据Chen系统的结构特点选取合适的反馈方式，设计单输入自适应控制器，使Chen系统自适应追踪参数未知的Rossler系统的某一变量，并由Lyapunov直接方法证明误差信号渐近稳定于零.数值仿真结果表明该控制方法可行，且可以实现未知参数的辨识.     

不确定非线性光电伺服系统滑模自适应控制

针对同时存在参数不确定性和结构不确定性的非线性光电伺服系统,运用自适应原理对系统的参数进行在线估计,同时提出一种改进的指数趋近律,并结合滑模控制（变结构控制）策略设计系统的滑模自适应位置控制器（APR）。以等效正弦1.2sin(0.93t)作为仿真输入,在0.45s后跟踪误差小于60μrad,表明该控制方法对此类不确定非线性系统的控制效果良好。     

基于LaSalle不变集定理自适应控制永磁同步电动机的混沌运动

基于LaSalle不变集定理,设计自适应混沌控制器对永磁同步电动机中的混沌进行控制.由于该控制器能自动跟踪平衡点,无需预先知道系统平衡点位置,不需要改变受控系统原有的结构, 因此设计直接简便,控制代价小,易于工程实现.仿真结果表明了该方法的有效性. 关键词： LaSalle不变集定理 自适应律 混沌控制 永磁同步电动机     

数据中心空调系统节能降耗研究进展

介绍了数据中心空调系统负荷特性和能耗现状,分析了包括风冷直接膨胀式、水冷直接膨胀式、冷冻水型机房空调等常规机房空调机组的适用情况,论述了目前重点的研究方向即气流组织优化、局部热点消除与液体冷却以及自然冷量利用三方面的研究进展。进一步,综述了热管式机房空调、分离式热管型空调系统(SHP)、分离式热管结合分体式空调系统的研究进展和最新的研究成果。     

一类含参数不确定性混沌系统的自适应控制

基于Lyaponov函数,对一类含参数不确定性混沌系统,当系统的不确定性参数具有未知界时,给出了自适应控制器的设计方法和构造控制器的解析式.构造混沌控制器直接代入控制器的参数化公式即可.方法简单,无需拼凑,能够使系统消除混沌并渐近稳定到任何期望的光滑轨道上.而整个设计过程无需预先知道未知参数的估计值和精确值.仿真验证了其有效性 关键词： 混沌系统 自适应混沌控制 Lyaponov函数 参数不确定性     

混沌系统自适应追踪控制新方法

提出一种混沌系统自适应追踪控制任意参考信号的新方法.该方法是通过预先设计出补偿控制器将混沌系统状态变量对参考信号的追踪控制问题转化为同结构混沌系统状态变量的自适应同步问题,再通过设计出自适应控制器,使同结构混沌系统全局渐近达到同步,追踪控制器为补偿控制器和自适应控制器的代数和.基于Lyapunov稳定性原理,理论上严格证明了利用本方法所设计追踪控制器的正确性.最后,以超混沌Chen系统为控制对象,利用本方法设计出追踪控制器完成了对不动点,正、余弦信号,同结构混沌系统状态变量,异结构混沌系统状态变量的追踪控 关键词： 自适应追踪控制 补偿控制器 自适应控制器 追踪控制器     

RCLSJ约瑟夫森结混沌系统的鲁棒自适应滑模控制

讨论了具有不确定参数和外界扰动的非线性电阻-电容-电流分路结模型(RCLSJ)混沌系统的广义控制问题。基于Lyapunov稳定性理论和滑模变结构控制方法,设计了鲁棒自适应滑模控制器,使得RCLSJ混沌系统能以任意比例因子追踪期望的参考信号。该方法不需要预先知道不确定参数和外界扰动的上下界,在控制器中引入一类简单的自适应律,用以在线估计参数和界值。该控制方法对参数不匹配和外界扰动具有较强的鲁棒性。数值仿真表明了该方法的有效性。     

双机系统的模糊PID自适应励磁调节控制

针对多机并联系统的稳定性控制问题,在构建同步发电机双机并联运行系统暂态仿真模型的基础上,将基于模糊理论的PID自适应控制方法用于双机并联运行系统的励磁调节自适应控制器的设计;该方法以电压的偏差和变化率作为控制器的输入,然后通过模糊处理,对比例、积分、微分系数进行自适应实时调整;在不同运行模式下对双机并联运行系统进行了仿真与动态性能分析,结果表明所设计的模糊PID自适应控制算法较传统的PID励磁控制具有更好的鲁棒性和抗干扰能力。     

空间相机CCD像面拼接重叠像元数分析与计算

针对一类具有分段仿射形式的混杂系统模型的控制方法问题,提出了一种高效显式模型预测控制算法。该算法通过将最优控制问题转化为多参数规划问题,离线求得具有分段仿射形式的显式控制器;在线过程,应用一种新的搜索算法,它能够快速准确的对系统状态点进行定位,确定其所属的控制器分区,再根据该分区所对应的子控制率,进行简单的线性运算,即可得到系统的输入。该控制方法避免了反复的在线优化计算,大大减少了计算量,并且,在线计算的速度更快,控制的实时性更好。将该算法应用到具有典型混杂特性的两容水箱系统中,仿真结果表明：水箱的液位从初始液位能够快速平稳的达到期望的液位,且与其它的控制算法相比较,该算法更加高效。     

受扰统一混沌系统基于RBF网络的主动滑模控制

针对受外扰影响的统一混沌系统,提出一种基于径向基函数(RBF)神经网络的主动滑模自适应控制方法.将被控系统分解为受控子系统和自由子系统,利用主动控制思想,建立受控子系统在目标点处的状态误差的可控标准型,设计出一个结构简单的基于滑模趋近率在线参数整定的RBF函数神经网络控制器,并且基于Lyapunov稳定性理论分析了系统的稳定性.仿真结果表明该控制器对系统参数突变和外部干扰具有鲁棒性,同时抑制了抖振. 关键词： 统一混沌系统 主动控制 滑模控制 RBF网络     

A knowledge-based adaptive control environment for an industrial laser cutting system

A hierarchically structured environment that integrates a knowledge- based expert system, adaptive process control and pattern recognition techniques for controlling a laser cutting process is described. Knowledge of the laser cutting process for different materials is organised and encoded into a rule-based system. An adaptive control algorithm based on on-line recursive parameter estimation and on-line control law synthesis was adopted for the highly non-linear cutting process control. Cutting speed was selected as the major control variable. Irradiance emitted from the cut front is used for the feedback signal to this adaptive controller. The irradiance signal feeds the recursive parameter estimator for system identification. Techniques of pattern recognition, which have been well developed in coherent optics, were applied to assess cut quality by characterising the exit spark cone images of the gas assisted laser cutting process. Images from the cutting processes were grabbed, edge enhanced and correlated with a synthetic discriminant function filter which was synthesised from reference images to give good cut quality. Results from digital simulations based on these pattern recognition algorithms are also presented.     

ADAPTIVE CONTROL AND IDENTIFICATION OF CHAOTIC SYSTEMS

A novel adaptive control and identification on-line method is proposed for a class of chaotic system with uncertain parameters. We prove that, using the presented method, a controller and identifier is developed which can remove chaos in nonlinear systems and make the system asymptotically stabilizing to an arbitrarily desired smooth orbit. And at the same time, estimates to uncertain parameters converge to their true values. The advantage of our method over the existing result is that the controller and identifier is directly constructed by analytic formula without knowing unknown bounds about uncertain parameters in advance. A computer simulation example is given to validate the proposed approach.     

Cooperative adaptive bidirectional control of a train platoon for efficient utility and string stability

This paper proposes cooperative adaptive control schemes for a train platoon to improve efficient utility and guarantee string stability. The control schemes are developed based on a bidirectional strategy, i.e., the information of proximal(preceding and following) trains is used in the controller design. Based on available proximal information(prox-info) of location, speed, and acceleration, a direct adaptive control is designed to maintain the tracking interval at the minimum safe distance. Based on available prox-info of location, an observer-based adaptive control is designed to achieve the same target, which alleviates the requirements of equipped sensors to measure prox-info of speed and acceleration. The developed schemes are capable of on-line estimating of the unknown system parameters and stabilizing the closed-loop system, the string stability of train platoon is guaranteed on the basis of Lyapunov stability theorem. Numerical simulation results are presented to verify the effectiveness of the proposed control laws.     

基于径向基函数神经网络的Lorenz混沌系统滑模控制

针对受参数不确定和外扰影响的混沌Lorenz系统，提出一种基于径向基函数（RBF）神经网 络的滑模控制方法.基于被控系统在不稳定平衡点处状态误差的可控规范形，设计滑模切换 面并将其作为神经网络的唯一输入.单入单出形式的RBF控制器隐层只需7个径向基函数，网 络的权值则依滑模趋近条件在线确定.仿真表明该控制器对系统参数突变和外部干扰具有鲁棒性，同时抑制了抖振. 关键词： 混沌控制 滑模 径向基函数神经网络 Lorenz系统     

不确定混沌系统的直接自适应模糊神经网络控制

提出利用直接自适应模糊神经网络控制一类不确定非线性混沌系统新方法.采用Takagi-Sug eno模糊逻辑系统估计混沌对象中未知函数，然后再对模糊神经网络控制律参数进行在线调 整，在系统所有信号一致有界情形下，解决混沌状态跟踪给定参考轨道控制问题.仿真结果 表明所得结论是正确的. 关键词： 模糊神经网络 自适应控制 混沌 参数不确定性     

基于扩张状态观测器的永磁同步电机混沌系统自适应滑模控制

针对部分状态不可测的永磁同步电机混沌系统, 结合自适应滑模控制和扩张状态观测器理论, 提出一种基于扩张状态观测器的永磁同步电机自适应混沌控制方法, 取消了系统所有状态完全可测的限制. 通过坐标变换, 将永磁同步电机混沌模型变为更适宜控制器设计的Brunovsky标准形式. 在系统部分状态和非线性不确定项上界均未知的情况下, 基于扩张状态观测器估计系统未知状态及不确定项, 并设计自适应滑模控制器, 保证系统状态快速稳定收敛至零点. 仿真结果表明, 该控制器能够改善滑模控制的抖振问题以及提高系统鲁棒性. 关键词： 永磁同步电机 混沌控制 扩张状态观测器 自适应滑模     

考虑模型不确定性和时延的静止无功补偿器自适应滑膜控制器设计

静止无功补偿器(static var compensator,SVC)不仅可以为电力系统提供无功支撑、稳定电压,其附加控制还可以有效提高系统暂态稳定性,但SVC模型参数的不确定性以及广域测量信号时延等外部干扰给附加控制器的设计带来很大的难度.提出了一种基于自适应滑模变结构理论的SVC鲁棒控制器设计方法,所设计控制器能有效提高系统暂态稳定性,并且其对于模型不确定性以及时延有较好的鲁棒性.首先根据区域惯量中心的运动方程建立了包含SVC的电力系统模型;然后将滑模变结构理论应用于电力系统模型中,求得SVC附加控制律,并通过自适应律优化控制器参数;最后通过四机两区域系统以及IEEE9节点系统对SVC控制器效果进行了仿真验证.结果表明,SVC自适应滑模控制器可以有效提升系统暂态稳定性,并且其性能优于传统的线性控制方法.     

Robust adaptive fuzzy neural tracking control for a class of unknown chaotic systems

In this paper, an adaptive fuzzy neural controller (AFNC) for a class of unknown chaotic systems is proposed. The proposed AFNC is comprised of a fuzzy neural controller and a robust controller. The fuzzy neural controller including a fuzzy neural network identifier (FNNI) is the principal controller. The FNNI is used for online estimation of the controlled system dynamics by tuning the parameters of fuzzy neural network (FNN). The Gaussian function, a specific example of radial basis function, is adopted here as a membership function. So, the tuning parameters include the weighting factors in the consequent part and the means and variances of the Gaussian membership functions in the antecedent part of fuzzy implications. To tune the parameters online, the back-propagation (BP) algorithm is developed. The robust controller is used to guarantee the stability and to control the performance of the closed-loop adaptive system, which is achieved always. Finally, simulation results show that the AFNC can achieve favourable tracking performances.     

Compensation control study based on impairment-aware for Wavelength Switched Optical Networks (WSON)

In an optical network, the optical signal transmitted along the lightpath may need to travel through a number of cross connect switches (OXCs), optical amplifiers, and fiber segments. While the signal propagates toward its destination, the optical components would continuously degrade the signal quality by inducing impairments. When the signal degradation is so severe that the received bit-error rate (BER) becomes unacceptably high, the lightpath would not be able to provide good service quality to a connection request. Such a lightpath, which has poor signal quality due to transmission impairments in the physical layer, should not be used for connection provisioning in the network layer. This paper presents an adaptive PID controller based on the power compensation of BP neural network to restrict the influence of the impairment power for a networked control system (NCS) with the presence of controller time-delay and power compensation at amplifiers' node firstly. Control algorithms continuously adjust their channel powers in response to dynamic information from the network links. And the controller could achieve the on-line adaptive power compensation without changing the parameters of PID controller. The results of simulation show that the proposed controller could adjust better channel power at the transmitter sites and achieve channel optical signal-to-noise ratio (OSNR) optimization with controller's time-delay.