模糊控制器中的智能积分

分析了传统模糊控制稳态精度差的缺陷，引入了智能积分以减少稳态误差和避免积分饱和，采用△ｕ＝ｆ（ｅ，ｅｃ）＋Ｅｄｔ（当Ｅ·ｃ＞０或ｃ＝０而Ｅ≠０）及△ｕ＝ｆ（ｅ，ｅｃ）（当Ｅ·ｃ＜０或Ｅ＝０）（其中ｅ为系统误差，ｃ为系统误差变化，ｆ（ｅ，ｅｃ）为模糊控制器输邮，ＫＫＥｄｔ为智能积分输出，△ｕ。为模糊控制器总输出）。然后设计了一个自组织模糊逻辑控制器以实现快速响应和小的超调，在金属镁还原炉上运行后获得较理想的效果。     

变稳飞机飞行模拟中的相似性匹配技术

针对在空中飞行模拟中变稳飞机与模型飞机的差异导致模拟逼真度下降的问题,提出一种相似性匹配技术,使驾驶员感受到一个更为接近模型飞机响应的飞行动力学特性.从理论上研究相似匹配技术,推导出合适的模型状态的转换逻辑;将转换逻辑加入到数值仿真试验和人机闭环地面试验中对其验证.结果表明,通过采用相似性匹配技术和调节跟踪参数值,整体跟踪效果得到改善,模拟逼真度得到显著提高.     

PLC与OP在综合测控系统的设计

基于可编程控制器(PLC)与操作员面板(OP)创新结合的平台,提供了一个安全可靠、实时操作和高效快捷的测量和控制系统,不仅扩展了PLC的功能,使其具有图形化、交互式工作界面的独立系统,而且大大减少了操作台上开关、按钮、仪表等的使用数量,使得编程和操作工作非常轻松舒适.提出了利用西门子S7-300系列PLC、OP构成的综合测控系统设计方案.设计了测控系统的组成、PLC和OP硬件结构及软件框图、操作面板组态画面以及主要监测功能,在实际的应用中提高了自动化测试程度及测试的数据精度和准确性,取得了良好的应用效果.     

用多值逻辑树表示的多状态系统可靠性分析

本文提出了多值逻辑树不交布尔代数法,可直接写出不交型结构函数,从而计算其可靠性,而不需先找出最小j值向量或质蕴涵、再将其不交化以计算其可靠性。故可节省计算量。本文还给出从系统不交布尔表达式反求全部最小j值向量的简便算法。用Prolog语言编制了本文算法的程序。     

导弹模糊增益调参鲁棒飞控系统设计

由于导弹飞控系统的高度非线性、时变性和不确定性,如何设计导弹高性能鲁棒控制系统是一项关键技术。利用模糊逻辑很强的知识综合和推理等特点,基于模糊控制和H∞鲁棒控制方法,建立了一种模糊参数化的增益调参飞控系统鲁棒设计方法,并利用线性矩阵不等式(LMI)技术,将鲁棒增益调参控制器设计转化成了相应的LMI凸优化问题,解决了复杂鲁棒控制器的求解问题。并将该方法应用于导弹鲁棒控制系统设计,仿真结果表明了该设计方法有效性。模糊逻辑、鲁棒控制以及线性矩阵不等式技术的有机结合可以设计具有全局稳定性及鲁棒性能要求的高性能导弹飞控系统。     

可编程逻辑器件的竞争—冒险现象

本文分析了可编程逻辑器件出现竞争冒险的原因，介绍了在数字系统设计过程中常用的几种消除竞争冒险的措施。     

基于DSP+CPLD的射频源控制

采用数字信号处理器(DSP)和可编程逻辑器件(CPLD)技术，设计了射频信号源控制电路板。介绍了控制部分硬件和软件的组成，并给出了所设计软件的主程序。用线性调频和脉冲两种信号进行了仿真实验。结果表明，所设计的射频源的输出符合要求，控制灵活。     

Zadeh模糊推理算法的直觉化扩展

对Zadeh模糊推理算法进行了直觉化扩展.将Zadeh定义的模糊关系Rm进行直觉化扩展,推出了其对应的直觉模糊取式推理算法和直觉模糊拒式推理算法.以具体算例叙述了推理计算过程中的细节,验证了方法的正确性和有效性,并依据直觉准则,对其性能进行了评价.     

Aircraft robust multidisciplinary design optimization methodology based on fuzzy preference function

This paper presents a Fuzzy Preference Function-based Robust Multidisciplinary Design Optimization (FPF-RMDO) methodology. This method is an effective approach to multidisciplinary systems, which can be used to designer experiences during the design optimization process by fuzzy preference functions. In this study, two optimizations are done for Predator MQ-1 Unmanned Aerial Vehicle (UAV): (A) deterministic optimization and (B) robust optimization. In both problems, minimization of takeoff weight and drag is considered as objective functions, which have been optimized using Non-dominated Sorting Genetic Algorithm (NSGA). In the robust design optimization, cruise altitude and velocity are considered as uncertainties that are modeled by the Monte Carlo Simulation (MCS) method. Aerodynamics, stability and control, mass properties, performance, and center of gravity are used for multidisciplinary analysis. Robust design optimization results show 46% and 42% robustness improvement for takeoff weight and cruise drag relative to optimal design respectively.     

Prediction of aerodynamic characteristics of an aircraft model with and without winglet using fuzzy logic technique

This paper describes the potentials of an aircraft model without and with winglet attached with NACA wing No. 65-3-218. Based on the longitudinal aerodynamic characteristics analyzing for the aircraft model tested in low subsonic wind tunnel, the lift coefficient (CL) and drag coefficient (CD) were investigated respectively. Wind tunnel test results were obtained for CL and CD versus the angle of attack α for three Reynolds numbers Re (1.7×¹⁰⁵, 2.1×¹⁰⁵, and 2.5×¹⁰⁵) and three configurations (configuration 1: without winglet, configuration 2: winglet at 0° and configuration 3: winglet at 60°). Compared with conventional technique, fuzzy logic technique is more efficient for the representation, manipulation and utilization. Therefore, the primary purpose of this work was to investigate the relationship between lift coefficients and drag coefficients with free-stream velocities and angle of attacks, and to illustrate how fuzzy expert system (FES) might play an important role in prediction of aerodynamic characteristics of an aircraft model with the addition of winglet. In this paper, an FES model was developed to predict the lift and drag coefficients of the aircraft model with winglet at 60°. The mean relative error of measured and predicted values (from FES model) were 6.52% for lift coefficient and 4.74% for drag coefficient. For all parameters, the relative error of predicted values was found to be less than the acceptable limits (10%). The goodness of fit of prediction (from FES model) values were found as 0.94 for lift coefficient and 0.98 for drag coefficient which were close to 1.0 as expected.