Robust ℋ︁∞ PID control for multivariable networked control systems with disturbance/noise attenuation

In this paper, we study the design problem of PID controllers for networked control systems (NCSs) with polyhedral uncertainties. The load disturbance and measurement noise are both taken into account in the modeling to better reflect the practical scenario. By using a novel technique, the design problem of PID controllers is converted into a design problem of output feedback controllers. Our goal of this paper is two‐fold: (1) To design the robust PID tracking controllers for practical models; (2) To develop the robust ??_∞ PID control such that load and reference disturbances can be attenuated with a prescribed level. Sufficient conditions are derived by employing advanced techniques for achieving delay dependence. The proposed controller can be readily designed based on iterative suboptimal algorithms. Finally, four examples are presented to show the effectiveness of the proposed methods. Copyright © 2011 John Wiley & Sons, Ltd.     

Short-term load forecasting using bayesian neural networks learned by Hybrid Monte Carlo algorithm

This paper presents a short term load forecasting model based on Bayesian neural network (shorted as BNN) learned by the Hybrid Monte Carlo (shorted as HMC) algorithm. The weight vector parameter of the Bayesian neural network is a multi-dimensional random variable. In learning process, the Bayesian neural network is considered as a special Hamiltonian dynamical system, and the weights vector as the system position variable. The HMC algorithm is used to learn the weight vector parameter with respect to Normal prior distribution and Cauchy prior distribution, respectively. The Bayesian neural networks learned by Laplace algorithm and HMC algorithm and the artificial neural network (ANN) learned by the BP algorithm were used to forecast the hourly load of 25 days of April (Spring), August (Summer), October (Autumn) and January (Winter), respectively. The roots mean squared error (RMSE) and the mean absolute percent errors (MAPE) were used to measured the forecasting performance. The experimental result shows that the BNNs learned by HMC algorithm have far better performance than the BNN learned by Laplace algorithm and the neural network learned BP algorithm and the BNN learned by HMC has powerful generalizing capability, it can welly solve the overfitting problem.     

基于GPRS的中央空调远程监控系统设计

远程监控系统对于中央空调系统的节能、集中管理、维修以及远程诊断有重要的意义。设计了一种基于中国移动GPRS的通信架构,实现中央空调制造商从中国大陆任意通过中国电信接入因特网的PC上监控位于中国大陆的中国移动GSM覆盖网络下的任意中央空调机组。与其他中央空调监控系统相比,具有应用范围广、成本低、数据实时性好的特点。     

基于小波的实时烟雾检测*

传统的离子式、吸气式、光电式等烟雾检测器在大空间中检测烟雾时,会受到发射信号与接收信号之间的距离、平面角度、精确对准等限制,无法对整个空间的烟雾状况进行描述.新的方法通过监测区域的摄像机拍摄的视频图像序列,进行小波变换,分析图像帧在时域和空域的频率特性,来确定被监测区域是否有火灾烟雾的发生.实验证明该方法不受空间高度、热障、易爆、有毒等环境的限制,并且有灵敏度高、抗干扰力强、适用范围广等特点.     

基于FPGA的无线接入点硬件平台的设计与实现*

基于认证、安全及QoS等方面的研究需要,提出了基于FPGA的HostAP设计方案,并搭建了无线接入点的硬件平台,在此平台上进行了硬件的裁减与集成;设计和实现了无线接入点的硬件电路,完成了无线接入点的高速PCB布局布线.在PCB布局布线的过程中,重点解决了高速印制电路板设计中的传输线效应问题,最后进行了实际的系统测试.     

含多端口能量路由器的多能互补系统故障保护功能测试研究

针对某区域用电负荷存在严重的季节性过载现象,以及可再生能源存在功率波动大、稳定性较差的问题,研制了多能(含水电、光伏、储能)、多端口的能量路由器装置及其能量管理系统.综合考虑当地多场景运行,解决了季节性过载问题,实现了能量的合理配置与利用.首先,对比分析选定能量路由器各端口变换器的拓扑结构;然后,对多场景下的工作模式进行介绍;最后,对含能量路由器的多能互补系统进行故障保护功能测试.测试结果表明,该系统具有较好的故障保护功能,有效实现了不同场景下的工作模式切换,直流母线电压稳定以及上层功率指令跟踪等功能,解决了当地用电负荷季节性过载和可再生能源功率波动较大的问题.     

预分离色谱法直接测定微量组份无限稀释活度系数

预分离色谱法可直接测定混合物中微量组份的无限稀释活度系数(γ~∞),而不必把此组份从混合物中分离出来。主要由四步组成:被测组份的预浓缩,被测组份的定性,选择预分离柱和γ~∞的测定。预浓缩样品经过预分离柱分出被测组份,然后进入测量柱测定γ~∞。此方法简捷、可靠与常规以纯溶质进样的色谱测y~∞的方法具有相同的测定精度,可广泛用于缺少纯样的场合。用此法测定了戊烯-1-炔-4、丁炔-2在若干酸醇中的γ~∞,分别求得了修正和未修正的UNlFAC参数,对kikic修正模型其值为ac=c,CooH=694.9(K)acooH,c=c=-99.08(K),ac=c,oH=812.99(K),aoH,c=c=143.36(K)     

利用ADCP回波强度估算河流悬移质含沙量的应用研究

通过简要介绍利用声学多普勒水流剖面仪(ADCP)回波强度估算悬移质含沙量的基本原理,结合梧州水文站的应用实例,利用ADCP的回波强度数据和现场采集的水样含沙量建立相关关系,根据相关关系由回波强度反演估算的水体悬移质含沙量验证了利用ADCP的回波强度估算悬移质含沙量的可行性。应用于梧州水文站的实践表明,左河道推算的系统误差为-0.1%,右河道推算的系统误差为1.5%。这种方法具有不扰动流场、测量剖面同步性较好、数据空间分辨率较高的优点,与传统的取样过滤称量法相比,计算效率显著提高。     

节涌流化床压力波动动力学研究(Ⅱ)模拟与实验

研究了节涌流化床多自由度随机波动模型参数,提出了确定气栓和料栓长度的试差迭代法、床层底部的“白噪声”扰动函数和床层表面的正态扰动函数.模型模拟计算的结果对于节涌流化床波动结构的动力学特征、压力波动的频谱特征和波动的传播与衰减等与实验结果取得了较好的吻合.     

Distributed receding horizon control of large-scale nonlinear systems: Handling communication delays and disturbances

This paper studies the robust distributed receding horizon control (DRHC) problem for large-scale continuous-time nonlinear systems subject to communication delays and external disturbances. A dual-mode robust DRHC strategy is designed to deal with the communication delays and the external disturbances simultaneously. The feasibility of the proposed DRHC and the stability of the closed-loop system are analyzed, and the sufficient conditions for ensuring the feasibility and stability are developed, respectively. We show that: (1) the feasibility is affected by the bounds of external disturbances, the sampling period and the bound of communication delays; (2) the stability is related to the bounds of external disturbances, the sampling period, the bound of communication delays and the minimum eigenvalues of the cooperation matrices; (3) the closed-loop system is stabilized into a robust invariant set under the proposed conditions. A simulation study is conducted to verify the theoretical results.