Fuzzy uncertainty modelling for project planning: application to helicopter maintenance

Maintenance is an activity of growing interest, especially for critical systems. In particular, aircraft maintenance costs are becoming an important issue in the aeronautical industry. Managing an aircraft maintenance centre is a complex activity. One of the difficulties comes from the numerous uncertainties that affect the activity and disturb the plans in the short and medium term. Based on a helicopter maintenance planning and scheduling problem, we study in this paper the integration of uncertainties into tactical and operational multi-resource, multi-project planning (respectively Rough Cut Capacity Planning and the Resource Constraint Project Scheduling Problem). Our main contributions are in modelling the periodic workload on a tactical level considering uncertainties in macro-task work content, and modelling the continuous workload on the operational level considering uncertainties in task duration. We model uncertainties using a fuzzy/possibilistic approach instead of a stochastic approach since very limited data are available. We refer to the problems as the Fuzzy Rough Cut Capacity Problem (FRCCP) and the Fuzzy Resource Constraint Project Scheduling Problem (RCPSP). We apply our models to helicopter maintenance activity within the frame of the Helimaintenance project, an industrial project approved by the French Aerospace Valley cluster that aims at building a centre for civil helicopter maintenance.     

Tuning fuzzy PD and PI controllers using reinforcement learning

In this paper, we propose a new auto-tuning fuzzy PD and PI controllers using reinforcement Q-learning (QL) algorithm for SISO (single-input single-output) and TITO (two-input two-output) systems. We first, investigate the design parameters and settings of a typical class of Fuzzy PD (FPD) and Fuzzy PI (FPI) controllers: zero-order Takagi–Sugeno controllers with equidistant triangular membership functions for inputs, equidistant singleton membership functions for output, Larsen’s implication method, and average sum defuzzification method. Secondly, the analytical structures of these typical fuzzy PD and PI controllers are compared to their classical counterpart PD and PI controllers. Finally, the effectiveness of the proposed method is proven through simulation examples.     

基于模糊免疫PID的纸浆漂白过程控制算法研究

利用模糊技术的非线性特点和生物系统的免疫反馈原理,提出基于模糊免疫的PID控制器,该控制器根据控制量的变化自适应调整比例系数。仿真结果表明,该控制器具有良好的跟踪输入能力和满意的控制效果。目前,该算法已经成功应用于某造纸厂的漂白工段,一定程度上提高了漂白后的纸浆质量,取得了较好的经济效益。     

基于模糊自适应PI控制的有源电力滤波器仿真

有源电力滤波器(APF)的控制方法是影响有源电力滤波器补偿性能的关键因素,同时也影响其应用的范围。本文提出了一种更为先进、更能能体现有源电力滤波器非线性控制特点的控制策略—模糊自适应PI控制,并利用Matlab软件对该控制策略的可行性进行了仿真。     

基于DSP的电阻炉温度模糊控制系统

为了提高电阻炉温度控制系统的控制精度和动态性能,提出了基于DSP的电阻炉温度模糊控制系统设计方法。该系统采用TMS320LF2407A DSP为微处理器,利用温度传感器AD590采集数据,经过模糊控制算法处理控制PWM输出占空比,实现电阻炉温度的调功控制。实验结果表明：该系统具有较高的稳态精度、较快的动态响应速度。系统所采取的软、硬件设计措施及模糊控制器设计方法具有一定通用性,也可用于其他温度控制系统中。     

Comparative evaluation of sliding mode fuzzy controller and PID controller for a boost converter

Nonlinear controllers such as fuzzy controllers and sliding mode controllers have been applied to boost converters because of their nonlinear properties. Although both fuzzy and sliding mode controllers have desirable characteristics, they have disadvantages in practice when applied individually. A sliding mode fuzzy controller is proposed to control boost converters. The sliding mode fuzzy controller combines the advantages of both fuzzy controllers and sliding mode controllers. It also has advantages of its own that are well suited for digital control design and implementation. A sliding mode fuzzy controller is designed and verified with experimental results using a prototype boost converter with a DSP-based digital controller. Experimental results of the boost converter using sliding mode fuzzy control are evaluated in comparison with experimental results using a linear PID and PI controller. The comparison indicates that the sliding mode fuzzy controller is able to obtain the desired transient response under varying operating points without chattering. The startup response using sliding mode fuzzy control is superior to the response using PID and PI control, while the load transient response shows no obvious advantage.     

架空线路覆冰闪络跳闸特性分析与风险建模

极端冰灾天气下线路覆冰闪络跳闸是激发大电网脆弱性、导致大面积停电的重要因素。覆冰闪络跳闸等现象的风险评估是建立冰灾防御体系的基础。基于电网层面,进行了冰灾天气下线路覆冰闪络跳闸的风险状态识别与风险建模。根据覆冰闪络跳闸特性分析,界定了输电网绝缘系统的脆弱点,即覆冰期绝缘系统冰凌桥接、融冰期冰凌断流等临界点。进一步建立了覆冰闪络状态的划分原则与风险等级,并对预测冰况进行了模糊模式识别和风险评级,为运行人员提供了动态风险信息。针对数据的小样本、多输入等特点,采用统计学习理论结构风险最小化方法,构建了最小二乘支持向量机（least squares support vector machine,LSSVM）冰闪跳闸风险评估模型,依据贝叶斯（Bayesian）证据推理优化模型参数。通过与误差反向传播人工神经网络（artificial neural networkwith error back propagation,BP-ANN）算法对比,验证了该模型的有效性。最后通过脆弱性指标分析了网架结构破坏的严重性与电网绝缘系统的脆弱性。     

考虑能源环境效益的含风电场多目标优化调度

随着大规模风电场的并网运行,电力系统调度过程需要考虑风电的影响;而日益严重的气候变化问题和人类社会可持续发展战略,也对电力行业提出了清洁化发展的要求.在传统电力系统优化调度的基础上,引入"能源环境效益"概念对包含风电场的电力系统优化调度模型进行修正,同时     

基于间接矢量控制的感应电机高速弱磁区电流控制

感应电机弱磁高速运行,由于低磁场、强耦合和大时滞的影响,使得目前的间接矢量控制系统难以实现电流有效控制。针对5～6倍基速及以上速度运行时的电流控制问题,考虑电压、电流、参数变化以及转速等多状态的耦合影响,以基于模糊推理的专家系统,构建电流给定自整定控制器,即使在整定失败的情况下也可以使用一个次优参数替代,克服了一般专家系统存在无法找到满足性能指标参数,导致整定失败的缺点,通过控制器对电流给定进行预处理,保证电流的有效跟随,提高电流控制的稳定性。最后进行仿真和物理实验验证,结果表明本方法具有有效性和实用性。     

行波型超声波电动机定位控制系统

速度控制和定位控制是超声波电动机控制的难点,而定位控制又因其时常包含速度调节过程(即速度控制)成为研究热点.由于超声波电动机复杂的非线性关系,常规的控制方法根本不能满足其控制需求.本文设计了一种新型的超声波电动机定位控制系统,该系统结合了模糊与PID控制算法的优点,即首先采用模糊算法调节频率控制量的参数值,改变电机速度实现初步定位;其次在设定目标值附近切换成PID控制策略,通过调节两相输入电压的相位差使其精确定位.本系统通过模糊PID控制器分别实现对超声波电动机两相输入电压的频率和相位差进行控制,通过实验证明,模糊PID控制策略能有效的实现对行波型超声波电动机的定位控制,其定位精确性可以得到保证.