发动机管理策略对增程式电动车燃油经济性的影响研究

为了提高增程式电动车的燃油经济性,在传统定点控制式发动机管理策略的基础上,提出了模糊控制式管理策略,对不同的发动机管理策略进行了仿真研究。在CRUISE软件中建立了增程式电动车的驱动系统模型,并在Simulink软件中搭建了发动机管理策略模型。在模糊逻辑控制器中,使用T-S模糊系统和Mamdani模糊系统,建立了模糊控制规则。在全球统一轻型车测试规程(worldwide light-duty test cycle,WLTC)循环工况下进行了联合仿真,将模糊控制式管理策略与定点控制式管理策略的结果进行了对比分析。仿真结果显示,与定点控制式管理策略相比,基于T-S模糊系统和Mamdani模糊系统的发动机管理策略分别使增程式电动车的燃油经济性提高了12.52%和10.60%。其中,基于T-S模糊系统的发动机管理策略效果更好,燃油经济性更优。     

发动机管理策略对增程式电动车燃油经济性的影响研究

为了提高增程式电动车的燃油经济性,在传统定点控制式发动机管理策略的基础上,提出了模糊控制式管理策略,对不同的发动机管理策略进行了仿真研究。在CRUISE软件中建立了增程式电动车的驱动系统模型,并在Simulink中搭建了发动机管理策略模型。在模糊逻辑控制器中,使用T-S模糊系统和Mamdani模糊系统,并建立了模糊控制规则。在全球统一轻型车测试规程（worldwide light-duty test cycle,WLTC）循环工况下进行了联合仿真,将模糊控制式管理策略与定点控制式管理策略的结果进行了分析。仿真结果显示,与定点控制式管理策略相比,基于T-S模糊系统和Mamdani模糊系统的发动机管理策略使增程式电动车的燃油经济性分别提高了12.52%和10.60%。其中,基于T-S模糊系统的发动机管理策略效果更好,燃油经济性更高。     

基于Viking35的电液调速器PID设定对柴油机调速特性影响的研究

基于Viking35控制器和执行器组成的电液调速器在实船柴油机转速控制上的应用,详细介绍了该控制器的PID参数设定方法,并通过实船柴油机不同PID参数调速特性控制的测试结果,总结出柴油机转速响应特性及控制效果均理想的最优PID参数设定。     

基于遗传算法的怠速工况最优模糊控制系统设计

结合小排量电喷发动机的结构特点，设计了怠速工况的模糊控制系统。利用遗传算法对模糊隶属度函数进行优化，根据优化参数得到最优模糊控制器。在发动机试验台上对基于经验参数的模糊控制系统和最优模糊控制系统怠速工况下转速变化进行了测试，绘制了速度输出曲线。研究表明，最优模糊控制系统具有较好的控制效果。     

基于5MW风机模型的变初值模糊PI变桨控制技术研究

针对传统PID变桨控制器参数自调整性能较差,适应性不强的问题,文章提出了风电机组变初值模糊PI变桨控制算法。通过模糊控制算法实现了PI参数的自动调节,根据风速大小设计了合理的变初值调整算法,实现了模糊控制器初值的在线调节。基于FAST风机软件中5 MW陆上风电机组非线性模型,分析了风电机组在额定风速以上运行时变桨系统的动态特性,从算法结构出发,设计了合适的模糊规则和量化、比例因子以及变初值调整算法,在Matlab/Simulink中搭建了变初值模糊PI变桨控制策略。通过仿真验证了控制策略在抑制转速波动和风机叶片、塔基受力力矩波动方面具有较好的效果。     

基于智能控制的煤层气抽采监测系统的应用研究

提出了一种基于智能控制的煤层气抽采监测系统。该系统以可编程逻辑控制器为控制中心,通过监测传感器实现对煤层气抽采时的井底流压力、温度等的连续性监测,实现对抽采设备运行转速的调整。实际表明该控制系统能将井底流压力变化范围控制在0.4%范围内,有效提升了煤层气采集的控制精度和采集速度。     

串联谐振感应加热电源频率跟踪控制的研究

提出了一种基于模糊控制的感应加热控制系统。介绍了模糊的基本工作原理,对比了锁相环控制、查询表式模糊逻辑控制了与调整因子式模糊控制的特性实验结果表明,与传统的锁相五控制器及查询表式模糊控制器相比,采用调整因子式模糊逻辑控制器具有捕获速度快,鲁棒性强、启动成功率和稳定性高的特点。     

基于差分进化算法的燃气轮机转速模糊PID复合控制

为了改善电站燃气轮机转速控制系统的调节品质,采用模糊PID(比例积分微分调节器)复合控制,通过差分进化算法对模糊控制器参数进行寻优,基于电力系统高级仿真支撑平台(JSSC)对该方法进行了验证。试验结果表明:模糊PID复合控制无论在升速过程还是甩负荷等突变工况时,不管在超调量、过渡过程时间等动态性能上还是静态偏差等方面,转速控制结果均明显优于单纯的PID控制;当转速特性发生变化时,该控制方法表现出良好的鲁棒性。     

基于运行模式识别的球磨机自适应解耦模糊控制与仿真

针对目前球磨机系统控制存在的不足,提出了一种基于运行模式识别的球磨机系统自适应模糊解耦控制算法.首先,分析了球磨机系统的动态特性,并结合某电厂测试试验,得到了球磨机系统的数学模型;其次,采用LS-SVM算法对球磨机系统的运行状态进行识别和分类;对于正常工况,针对耦合回路,设计了自适应解耦模糊控制器对球磨机进行控制;对于异常工况,给出了相应的控制策略.最后对正常工况下的自适应解耦模糊控制算法进行了仿真研究.仿真结果表明,该算法能够有效地实现球磨机系统运行工况模式识别和耦合回路的解耦控制,控制系统具有良好的动态性和鲁棒性.     

白适应模糊控制在热力系统容错控制中的应用

在热力系统中采用容错控制技术，可以提高控制系统的可靠性，同时降低运行成本。研究采用自适应模糊控制实现非失效的被动容错控制。提出一种新的自适应模糊控制器；采用基于动态神经元网络的辨识模型学习被控对象的动态特性，设计了评价模糊控制器性能的FITAE指标；采用遗传算法优化模糊控制器参数，同时优化比例因子和控制规则表。为了提高模糊控制器的自适应调整速度，采用二阶段优化策略。仿真研究表明：自适应模糊控制器的热力系统被动容错控制方法是可行的。图3参7     

Fuzzy logic controller implementation for a solar air-conditioning system

The implementation of a variable structure fuzzy logic controller for a solar powered air conditioning system and its advantages are investigated in this paper. Two DC motors are used to drive the generator pump and the feed pump of the solar air-conditioner. Two different control schemes for the DC motors rotational speed adjustment are implemented and tested: the first one is a pure fuzzy controller, its output being the control signal for the DC motor driver. A 7 × 7 fuzzy matrix assigns the controller output with respect to the error value and the derivative of the error. The second scheme is a two-level controller. The lower level is a conventional PID controller, and the higher level is a fuzzy controller acting over the parameters of the low level controller. Step response of the two control loops are presented as experimental results. The contribution of this design is that in the control system, the fuzzy logic is implemented through software in a common, inexpensive, 16-bit microcontroller, which does not have special abilities for fuzzy control.     

Fuzzy logic control based maximum power tracking of a wind energy system

In this paper a utility interactive wind energy conversion system (WECS) with an asynchronous (AC–DC–AC) link is described. The control system has the objective of identifying and extracting the maximum power from the wind energy system and transferring this power to utility. A fuzzy logic control (FLC) technique has been implemented to design the tracking controller of the WECS. A wind speed step model was used in the design phase. The performance of the WECS with the proposed fuzzy logic controller is tested using real meteorological data. Its robustness and effectiveness are demonstrated by the simulation results of the controlled system.     

Novel power capture optimization based sensorless maximum power point tracking strategy and internal model controller for wind turbines systems driven SCIG

Under the trends to using renewable energy sources as alternatives to the traditional ones, it is important to contribute to the fast growing development of these sources by using powerful soft computing methods. In this context, this paper introduces a novel structure to optimize and control the energy produced from a variable speed wind turbine which is based on a squirrel cage induction generator (SCIG) and connected to the grid. The optimization strategy of the harvested power from the wind is realized by a maximum power point tracking (MPPT) algorithm based on fuzzy logic, and the control strategy of the generator is implemented by means of an internal model (IM) controller. Three IM controllers are incorporated in the vector control technique, as an alternative to the proportional integral (PI) controller, to implement the proposed optimization strategy. The MPPT in conjunction with the IM controller is proposed as an alternative to the traditional tip speed ratio (TSR) technique, to avoid any disturbance such as wind speed measurement and wind turbine (WT) characteristic uncertainties. Based on the simulation results of a six KW-WECS model in Matlab/Simulink, the presented control system topology is reliable and keeps the system operation around the desired response.     

基于模糊神经元网络的球磨机控制系统的改进

球磨机是一个多变量的复杂非线性系统,介绍了设计的由模糊控制器和神经元控制器结合的控制系统,用PLC加以实现,取得了对球磨机良好的控制效果.     

Hybrid fuzzy controller for speed control of switched reluctance motor drives

A hybrid fuzzy controller for speed control of switched reluctance motor (SRM) drives is presented in this paper. The developed hybrid fuzzy control law consists of a proportional integral (PI) controller at steady state, a PI type fuzzy logic controller (FLC) at transient state and a simple logic controller between the steady and transient states to achieve the desired performance at various operating conditions under soft chopping operation. The importance of the hybrid fuzzy controller is highlighted by comparing the performance of various control approaches, including PI control, PI type fuzzy logic control and PD type fuzzy control for speed control of SRM motor drives. The complete control algorithm is demonstrated by intensive experimental results. It is shown that the presented hybrid controller for SRM drive has fast tracking capability, less steady state error and is robust to load disturbance. The complete speed control scheme of the SRM drive incorporating the hybrid control is experimentally implemented and validated using a high speed digital signal processor board TMS320F2812 for a prototype 1.2 kW SRM.     

Maximum power point tracking using adaptive fuzzy logic control for grid-connected photovoltaic system

This paper proposes a method of maximum power point tracking using adaptive fuzzy logic control for grid-connected photovoltaic systems. The system is composed of a boost converter and a single-phase inverter connected to a utility grid. The maximum power point tracking control is based on adaptive fuzzy logic to control a switch of a boost converter. Adaptive fuzzy logic controllers provide attractive features such as fast response, good performance. In addition, adaptive fuzzy logic controllers can also change the fuzzy parameter for improving the control system. The single phase inverter uses predictive current control which provides current with sinusoidal waveform. Therefore, the system is able to deliver energy with low harmonics and high power factor. Both conventional fuzzy logic controller and adaptive fuzzy logic controller are simulated and implemented to evaluate performance. Simulation and experimental results are provided for both controllers under the same atmospheric condition. From the simulation and experimental results, the adaptive fuzzy logic controller can deliver more power than the conventional fuzzy logic controller.     

Intelligent power management in a vehicular system with multiple power sources

This paper presents an optimal online power management strategy applied to a vehicular power system that contains multiple power sources and deals with largely fluctuated load requests. The optimal online power management strategy is developed using machine learning and fuzzy logic. A machine learning algorithm has been developed to learn the knowledge about minimizing power loss in a Multiple Power Sources and Loads (M_PS&LD) system. The algorithm exploits the fact that different power sources used to deliver a load request have different power losses under different vehicle states. The machine learning algorithm is developed to train an intelligent power controller, an online fuzzy power controller, FPC_MPS, that has the capability of finding combinations of power sources that minimize power losses while satisfying a given set of system and component constraints during a drive cycle. The FPC_MPS was implemented in two simulated systems, a power system of four power sources, and a vehicle system of three power sources. Experimental results show that the proposed machine learning approach combined with fuzzy control is a promising technology for intelligent vehicle power management in a M_PS&LD power system.     

车用柴油机电子调速器的模糊控制研究

应用模糊控制方法对车用柴油机电子调速器的油门执行器位置伺服控制进行研究。模糊控制方法的显著特点是不需要对控制目标建立精确的数学模型。在常规的模糊控制器上提出一种在线修正因子和量化因子的自调整模糊控制器。这种修正算法是基于仿人工智能控制的思想,分析和识别系统的输出状态,动态地、独立地对量化因子和比例因子进行在线修正,以调整整个控制过程不同阶段的控制特性。发动机的台架试验结果证明油门执行机构获得了良好的动态响应特性。