基于冲击负荷扰动的自校正控制器设计

本文针对攀钢自备电厂１００ＭＷ单元机组过热蒸汽温度系统承受冲击负荷的问题，提出了一种实用的自校正算法。先利用广义内模概念，导出最优滑动平均滤波器，然后设计出改进的广义预测自校正控制器。理论分析和数字仿真表明，所设计的自校正控制器能抑制冲击负荷干扰，具有较强的鲁棒性。     

一种模糊神经网络智能控制器及其应用

本文提出了一种模拟推理神经网络模型，并用于实现模糊控制，在此基础上提出了基于模糊神经网络的自组织控制器和有效的快速Ｋａｌｍａｎ学习算法，经仿真结果和实际温度控制表明，这种新型的自组织控制器性能优于一般Ｆｕｚｚｙ自组织控制器。     

参数模糊自整定PID在飞机全电刹车中的应用

提出了一种参数模糊自整定PID的控制策略在飞机全电刹车系统中的应用。滑移率的偏差和偏差变化率作为控制器的输入,PID控制器3个参数的自整定值作为控制器的输出,实现了PID参数的在线自整定。分析了参数模糊自整定PID的原理。飞机全电刹车系统的仿真曲线表明:以滑移率为控制对象,参数模糊自整定PID控制与常规PID相比,控制系统的响应速度快、超调量减小、过渡过程时间大大缩短、振荡次数少,具有较强的鲁棒性和稳定性。     

基于模糊神经网络的粗纱机电气控制系统

文章在现有的纺织机械电气控制系统中,引入了一种模糊神经网络控制器,利用神经网络控制器的自学习能力来获得精度、速度及稳定性都较高的控制系统,从而提高了出纱速度和质量。     

一种实用的自校正控制器及其应用

介绍一种由８０３１单片机系统，ＡＤＣ０８０９芯片等组成的自校正控制器的原理及硬件与软件设计，该控制器采用一种实用的自校正算法，应用于甲醛生产过程反应器的温度控制，取得了满意的效果。     

小型漂浮式卫星天线自抗扰控制器的研究

研究了一种适用于军事隐蔽式侦察的小型漂浮式卫星天线双环自抗扰控制器,实现了在中小涌浪扰动下天线自动跟踪目标卫星的俯仰伺服控制. 以自抗扰理论为基础,结合小型天线俯仰控制的特点分别设计了位置环和速度环自抗扰控制器;比较了双环自抗扰控制器和比例-积分-微分(Proportional-Integral-Derivative,PID)控制器的阶跃响应,验证了双环自抗扰控制器性能优于PID控制器性能;利用半实物仿真平台,以中小涌浪扰动下天线系统俯仰角的变化为控制器的输入,进行天线俯仰伺服控制,通过检测到的信标信号验证了双环自抗扰控制器可以实现中小涌浪扰动下天线对目标卫星的稳定跟踪.     

惯性系统平台的自适应稳定回路研究

稳定回路在平台惯性系统中起着关键作用.但在设计稳定回路的控制器时,发现普通PID控制有抗干扰能力不强、控制参数易改变的缺点;因此设计了一种基于模糊控制原理的自适应模糊自抗扰控制器.该控制器充分结合了模糊控制器和自抗扰控制器的各自优势,利用模糊控制的推理能力,达到自动调整参数的目的.通过对光纤陀螺随动系统的仿真结果表明,在控制效果上,该控制器的自适应能力和抗干扰能力都优于经典PID控制器.说明自适应模糊自抗扰控制器在稳定回路中具有很好的应用前景.     

基于MATLAB的参数模糊自整定PID控制器的设计与仿真研究

本文介绍了一种基于模糊控制原理的参数自整定PID控制器的设计方法,同时利用MATLAB中的SIMULINK和模糊工具箱进行了仿真研究.仿真结果表明,该控制器具有鲁棒性强、没有超调、快速性和稳定性较好等特点.     

一种DWDM光源混合式控制器设计

分析了数字控制器和模拟控制器在DWDM(密集波分复用)光源控制器设计中的优缺点,提出了一种混合式控制器的设计.该控制器的核心是模拟控制器,能够较好地保证系统的稳态精度.同时利用数字控制器对其进行监控,调整控制参数,保证DWDM光源的长期工作稳定.实验研究表明,该系统工作波长稳定性在0.005 nm以内,而且能够对系统参数漂移进行自校正.     

电烤箱温度系统的模糊自整定PID控制方法的研究

提出利用模糊自整定PID控制器实现对电烤箱温度控制的方法,将模糊控制器和PID控制器结合在一起,利用模糊逻辑控制实现了控制器参数在线自调整,完善了传统PID控制器的性能,提高了系统的控制精度.并把MATLAB中的Fuzzy Toolbox和Simulink结合起来,方便实现了自整定模糊PID控制系统的计算机仿真.     

Multiuser detection using a genetic algorithm in CDMAcommunications systems

In this study, a hybrid approach that employs a genetic algorithm (GA) and a multistage detector (MSD) for the multiuser detection problem in a code-division multiple-access communications system is proposed. Using this approach: (1) the GA is used as the first stage of the MSD to provide a good initial point for successive stages of the MSD and (2) the MSD is embedded into the GA as a “genetic operator” to improve further the fitness of the population at each generation. Such a hybridization of the GA with the MSD reduces its computational complexity by providing faster convergence. In addition, a better initial data estimate supplied by the GA improves the performance of the MSD, and the embedded MSD improves the performance of the GA. Simulation results for the synchronous and asynchronous cases are provided to show that the approach is promising     

Array beamforming in the presence of a mounting tower using genetic algorithms

A beamforming method based on genetic algorithms (GAs) is proposed for antenna arrays in the presence of strong platform effects. A GA is used to maximize the mainbeam radiation and minimize sidelobe levels based on the active element patterns of the array. Simulation results show that the array excitations optimized by the GA can achieve superior beamforming performance than those from array theory or phase conjugation. Experimental results collected from a seven-element circular array further demonstrate the beamforming and direction finding performance of the GA based beamformer. In addition, an array element position optimization is carried out. Simulation results show further improvement in the beamforming performance.     

Design and analysis of planar monopole antennas using a genetic algorithm approach

The application of genetic algorithm (GA) optimization to the design and analysis of planar monopole antennas is presented. GA is first used to optimize the impedance matching bandwidth of two particular planar element shapes, the bow-tie (BT) and reverse bow-tie (RBT). The results of this study indicate that the RBT can achieve a significantly wider bandwidth with a much smaller size than the traditional BT. In a follow-on study, GA is used to generate arbitrarily shaped planar monopole designs, which exhibit improved broadband performance and/or reduced size compared with the RBT. The designs generated by the GA demonstrate a better tradeoff between matching bandwidth and electrical size compared with planar monopole designs previously characterized in the literature. Analysis of results from simulation and measurement are presented, which provide insight into the operation of these antennas as well as the key parameters that lead to improved performance. Finally, a performance bound is generated to relate the bandwidth limitation of planar monopoles to size.     

Smart UWB System Based on STC and TR Techniques for BER Performance Enhancement and Interference Rejection

In this research, a novel smart UWB system is introduced. The proposed system is based on using an adaptive maximum ratio combining (MRC) Rake receiver. The proposed adaptive Rake receiver uses Genetic algorithm (GA) to adaptively select the delays of the fingers of the Rake receiver depending on the channel impulse response. It adaptively selects the delays that will allow the Rake receiver to capture most of the energy in the multipath components with minimum complexity. This adaptive Rake receiver is referred to as a GA Rake. The adaptive GA Rake is applied to a single-input single-output and space time coding (STC) multi-input single-output UWB systems. The performance of those systems using a GA Rake is compared to their performance when using a conventional MRC-Rake receiver and showed a great enhancement in performance with less receiver complexity. Also, in this paper, the smart UWB system using STC is modified by using the time reversal (TR) pre-coding technique. The modified system is referred to as a TR smart UWB system. This modification leads to more enhancements in performance and more reduction in receiver complexity over the smart UWB system. Moreover, this paper also shows the ability a TR smart UWB system in combating interference from other UWB systems.     

遗传算法在FIR滤波器设计——频率抽样法中的应用

本文介绍了遗传算法在FIR滤波器设计——频率抽样法中的应用.用遗传算法确定过渡带样本值,解决了传统方法(查表法)不能保证数据是最优的问题.本文还对标准遗传算法进行了适当的改进.给出了FIR数字低通、带通滤波器设计的两个例子.实验结果说明通过遗传算法设计的FIR滤波器性能较查表法得到了改善.     

Cognitive Radio Engine Design Based on Ant Colony Optimization

In this letter, a mutated ant colony optimization (MACO) cognitive radio engine is proposed, and it is the first time to apply ACO algorithm to this problem. The cognitive radio is a promising technology nowadays to alleviate the apparent scarcity of available radio spectrum, and the cognitive radio engine determines the optimal radio transmission parameters for the system. The cognitive engine problem is usually solved by genetic algorithm (GA), however, the GA converges slowly and its performance can still be improved. Hence, MACO algorithm with excellent performance is applied to the cognitive engine in this letter. Simulation results show that the fitness scores obtained by the MACO engine are much better than the ACO and GA engines in different scenarios.     

Multiuser Detection Based on Adaptive LMS and Modified Genetic Algorithm in DS-CDMA Communication Systems

In this paper, we present an efficient evolutionary algorithm for the multi-user detection (MUD) problem in direct sequence-code division multiple access (DS-CDMA) communication systems. The optimum detector for MUD is the maximum likelihood (ML) detector, but its complexity is very high and involves an exhaustive search to reach the best fitness of transmitted and received data. Thus, there has been considerable interest in suboptimal multiuser detectors with less complexity and reasonable performance. The proposed algorithm is a combination of adaptive LMS Algorithm and modified genetic algorithm (GA). Indeed the LMS algorithm provides a good initial response for GA, and GA will be applied for this response to reach the best answer. The proposed GA reduces the dimension of the search space and provides a suitable framework for future extension to other optimization algorithms. Our algorithm is compared to ML detector, Matched Filter (MF) detector, conventional detector with GA; and Adaptive LMS detector which have been used for MUD in DS-CDMA. Simulation results show that the performance of this algorithm is close to the optimal detector with very low complexity, and it works better in comparison to other algorithms.     

FPGA placement optimization by two-step unified Genetic Algorithm and Simulated Annealing algorithm

Genetic Algorithm （GA） is a biologically inspired technique and widely used to solve numerous combinational optimization problems. It works on a population of individuals, not just one single solution. As a result, it avoids converging to the local optimum. However, it takes too much CPU time in the late process of GA. On the other hand, in the late process Simulated Annealing （SA） converges faster than GA but it is easily trapped to local optimum. In this letter, a useful method that unifies GA and SA is introduced, which utilizes the advantage of the global search ability of GA and fast convergence of SA. The experimental results show that the proposed algorithm outperforms GA in terms of CPU time without degradation of performance. It also achieves highly comparable placement cost compared to the state-of-the-art results obtained by Versatile Place and Route （VPR） Tool.     

FPGA-based genetic algorithm implementation for AC chopper fed induction motor

Genetic algorithm (GA)-based harmonic elimination technique is proposed for designing AC chopper. GA is used to calculate optimal firing angles to eliminate lower order harmonics in output voltage. Total harmonic distortion of output voltage is taken for the fitness function used in the GA. Thus, the ratings of the load are not mandatory to be known for calculating the switching angles using proposed technique. For the performance assessment of GA, Newton–Raphson (NR) method is applied in this present work. Simulation results show that the proposed technique is better in terms of less computational complexity and quick convergence. Simulation results were verified by field programmable gate array controller-based prototype. Simulation study and experimental investigations show that the proposed GA method is superior to the conventional methods.     

遗传算法在生物组织电导率重构中的应用研究

在载流线圈作用下，通过测量生物组织产生的二次场对线圈阻抗的改变，应用遗传算法重构分层生物组织各层电导率和厚度的方法。结合惩罚函数法对适应度进行了优化。数值计算结果表明改进后的算法在搜索速度和精确度上明显提高，并且具有较高的抗噪性能。