翼滑艇推进系统模糊控制的仿真比较研究

在Matlab平台上，设计了基于模糊逻辑工具箱的模糊控制器和规则自动拟合模糊控制器；建立了翼滑艇的智能推进系统模型，并用Matlab／Sireulink编制了仿真模型；进行了实时仿真。仿真结果表明：规则自动拟合模糊控制器，无论是从超调量方面还是从稳定性方面，都比工具箱的模糊控制器的控制效果好。     

模糊PID控制器的改进

为提高模糊ＰＩＤ控制器的控制效果，分别对模糊控制器的量化因子，比例因子和ＰＩＤ参数进行了分析，并在此基础上对模糊ＰＩＤ控制器进行改进，给出了控制模型。仿真结果表明，改进后的模糊ＰＩＤ控制器的动态抗扰能力增强，超调量减小。     

汽车防抱制动模糊控制器的设计

讨论了汽车防抱制动系统模糊控制器的设计。以双参数逻辑门限控制方法为基础提出了新的模糊控制逻辑，设计了基于角加速度的两级模糊防抱控制器，并对这种模糊控制器进行了仿真分析，结果表明，该控制器能有效地适应不同的路况，与传统的控制器相比有较强的鲁棒性。     

采用遗传算法的交通信号模糊控制器优化设计

一般模糊控制器控制规则依赖于专家经验，量化因子固定，不能随交通流的变化而动态改变，针对这种情况，提出了基于遗传算法的模糊控制器的优化控制．对模糊控制器所作出的决策进行动态调整，文章介绍了控制过程、染色体的编码及遗传算子的实现方法。以车辆平均延误为目标函数，在模糊控制器作出判决的基础上，对控制规则的调整量进行全局寻优，加快了收敛速度。通过对某一4相位交叉口进行仿真的结果表明．控制效果有明显的改善。     

前向式混合动力系统模型中控制器建模与仿真

在Matlab／Simulik和Matlab／Stateflow平台上建立了某一混合动力汽车的前向式仿真模型及控制器模型．研究了基于PID控制和模糊逻辑控制的两种驾驶员模型，通过仿真过程对该前向式混合动力系统控制器模型进行了验证，并对两种驾驶员模型控制器的控制效果进行了比较。     

模糊控制方法在汽车防抱制动系统中的应用

将模糊控制理论引入汽车防抱制动系统，用以确定防抱制动系统的参数。针对简化的汽车模型，设计了普通模糊控制器和一种自透应模糊控制器。计算机数字仿真试验结果表明这两种控制器能取得较好的控制效果。     

基于模糊控制的某重型车辆起步仿真分析

根据湿式离合器起步过程的工作状态,利用MATLAB软件的模糊逻辑工具箱设计了某重型车起步模糊控制器,建立了起步仿真模型并进行仿真。仿真结果表明,所设计的模糊控制器能够显著减少滑摩功和冲击度,具有较好的控制效果。     

基于模糊PID控制的汽车防抱制动系统控制算法研究

提出一种基于模糊PID控制的防抱制动系统控制方式。针对简化的汽车纵向双轮模型,设计了模糊PID控制器,讨论了模糊 PID控制切换参数的选取,并具体介绍了模糊控制器的设计。仿真对比试验验证了模糊PID控制性能优于单一的PID控制和模糊控制。     

基于模糊PID控制的汽车道路模拟系统研究与仿真

根据模糊控制理论，以汽车道路模拟系统为研究对象，将模糊控制与常规的PID控制相结合，设计出一套模糊PID控制系统。并对正弦波、方波以及随机波进行仿真再现，仿真结果表明，该模糊PID控制器比常规PID控制器具有更高的控制精度和更好的动态性能。     

一种用于1／2汽车主动悬架的可调模糊控制器

本文对用于1／2汽车主动悬架系统的模糊控制器进行了研究。在设计模糊控制器时，引入模糊控制规则调整因子，使得控制策略灵活，适应性强，并以模拟路面时间历程为输入对汽车1／2主动悬架模型进行计算机仿真，结果表明用这种可调模糊控制器控制的主动悬架，汽车的适性和操纵稳定性都得到了明显改善。     

PID plus fuzzy logic method for torque control in traction control system

A Traction Control System (TCS) is used to control the driving force of an engine to prevent excessive slip when a vehicle starts suddenly or accelerates. The torque control strategy determines the driving performance of the vehicle under various drive-slip conditions. This paper presents a new torque control method for various drive-slip conditions involving abrupt changes in the road friction. This method is based on a PID plus fuzzy logic controller for driving torque regulation, which consists of a PID controller and a fuzzy logic controller. The PID controller is the fundamental component that calculates the elementary torque for traction control. In addition, the fuzzy logic controller is the compensating component that compensates for the abrupt change in the road friction. The simulation results and the experimental vehicle tests have validated that the proposed controller is effective and robust. Compared with conventional PID controllers, the driving performance under the proposed controller is greatly improved.     

无人驾驶机器人车辆非线性模糊滑模车速控制

为了实现不同行驶工况下车速的精确、稳定控制，提出一种基于非线性干扰观测器的无人驾驶机器人车辆模糊滑模车速控制方法。考虑模型不确定性和外部干扰对车速控制的影响，建立车辆纵向动力学模型。通过分析无人驾驶机器人油门机械腿、制动机械腿的结构、机械腿操纵自动挡车辆踏板的运动，建立油门机械腿和制动机械腿的运动学模型。在此基础上，分别设计油门/制动切换控制器、油门模糊滑模控制器以及制动模糊滑模控制器，并进行控制系统的稳定性分析。油门/制动切换控制器以目标车速的导数为输入来进行油门与制动之间的切换控制。油门模糊滑模控制器和制动模糊滑模控制器以当前车速以及车速误差为输入，分别以油门机械腿直线电机位移和制动机械腿直线电机位移为输出来实现对油门与制动的控制。模糊滑模控制器中，为了减少控制抖振，滑模控制的反馈增益系数由模糊逻辑进行在线调节。模糊滑模控制器中的非线性干扰观测器用于估计和补偿无人驾驶机器人车辆的模型不确定性与外部干扰。仿真及试验结果对比分析表明：本文方法能够精确地估计和补偿无人驾驶机器人车辆的模型不确定性和外部干扰，避免了油门控制与制动控制之间的频繁切换，并实现了精确稳定的车速控制。     

基于神经网络的半主动悬架自适应模糊控制

提出了基于神经网络的自适应模糊控制策略。模糊控制主要用来对付系统的非线性；神经网络根据振动响应的方差递推结果来辨识车体的振动情况实时调节模糊控制器的量化因子，使模糊控制器对路面的变化具有自适应的能力。在半主动悬挂1／4车非线性模型的基础上进行了仿真研究。     

Hybrid state-feedback sliding-mode controller using fuzzy logic for four-wheel-steering vehicles

This paper presents a fuzzy controller for high-speed four-wheel-steering (4WS) vehicles based on the state-feedback and the sliding-mode control methods. In the proposed fuzzy controller, the consequent part of the fuzzy IF-THEN rules consists of either a sliding-mode controller or a state-feedback controller. Also, it will be proved that, if every fuzzy rule is stable in the sense of Lyapunov for a general Lyapunov function, defined for the whole system, then the whole system is stable in the sense of Lyapunov. The effectiveness of the proposed method for handling improvement of the 4WS systems will be demonstrated by simulations using a nonlinear vehicle model. The simulation results show that the proposed control method can enhance the dynamic response of the 4WS vehicles by reducing the transient response time and improving vehicle stability as compared to the sliding-mode and the fuzzy sliding-mode control methods.     

汽车主动悬架自调整模糊控制试验

以汽车操纵稳定性及行驶平顺性为控制目标,提出一种在线可调整的模糊控制算法,其模糊控制规则表可以用解析的方法进行计算。针对简化的汽车模型,为控制悬架系统的振动设计了自调整模糊控制器。与自适应控制主动悬架系统相比较,在两自由度悬架系统试验台架上进行了对比试验研究,结果表明该算法对汽车的振动控制具有明显效果,进一步说明提出的算法对汽车悬架系统的振动控制具有较好的适应性。     

Fuzzy adaptive sliding mode controller for an air spring active suspension

A fuzzy adaptive sliding mode controller for an air spring active suspension system is developed. Due to nonlinearity, preload-dependent spring force and parameter uncertainty in the air spring, it is difficult to control the suspension system. To achieve the desired performance, a fuzzy adaptive sliding mode controller (FASMC) is designed to improve the passenger comfort and the manipulability of the vehicle. The fuzzy adaptive system handles the nonlinearity and uncertainty of the air suspension. A normal linear suspension model with an optimal state feedback control is designed as the reference model. The simulation results show that this control scheme more effectively and robustly isolates vibrations of the vehicle body than the conventional sliding mode controller (CSMC).     

电控汽油机怠速控制方式

针对怠速工况分别设计了变参数PID控制系统和模糊控制系统，并进行了实车试验对比。结果表明，怠速工况下，模糊控制方式比PID控制方式转速波动小，控制效果好。     

DSP-based self-organising fuzzy controller for active suspension systems

This study proposed a self-organising fuzzy controller (SOFC) for controlling an active suspension system to evaluate its control performance. During the control process, the SOFC continually updated the learning strategy in the form of fuzzy rules. The fuzzy rule table of this SOFC could be initially set to zero. This not only overcame the difficulty in finding appropriate membership functions and control rules for designing a fuzzy controller, but also solved the database problem where the fuzzy rules of a fuzzy controller, once determined, remained fixed and could not suitably regulate them in real time to optimise the dynamic response of the system required to gain the desired control performance. To demonstrate the applicability of the proposed SOFC for active suspension systems, a quarter-car hydraulic-servo suspension system was designed and constructed to evaluate the feasibility of active suspension control. Additionally, to conform to real-time application requirements in the vehicular industry, the SOFC was implemented with a digital signal processor to control the hydraulic-servo suspension system so that the control performance could be determined. The SOFC has shown a better control performance in suppressing the vibration amplitude of the vehicle body for enhancing the structural safety of the vehicle and increasing the life of the suspension system. It also effectively suppressed the amplitude of the vehicle body acceleration and reduced the tire deflection for improving the ride and the handling quality of a vehicle better than a passive control, as verified in experimental results.     

Novel coordinated algorithm for Traction Control System on split friction and slope road

A Traction Control System (TCS) is used to avoid excessive wheel-slip via adjusting active brake pressure and engine torque when vehicle starts fiercely. The split friction and slope of the road are complicated conditions for TCS. Once operated under these conditions, the traction control performance of the vehicle might be deteriorated and the vehicle might lack drive capability or lose lateral stability, if the regulated active brake pressure and engine torque can’t match up promptly and effectively. In order to solve this problem, a novel coordinated algorithm for TCS is brought forward. Firstly, two brake controllers, including a basic controller based on the friction difference between the two drive wheels for compensating this difference and a fuzzy logic controller for assisting the engine torque controller to adjust wheel-slip, are presented for brake control together. And then two engine torque controllers, containing a basic PID controller for wheel-slip control and a fuzzy logic controller for compensating torque needed by the road slope, are built for engine torque control together. Due to the simultaneous and accurate coordination of the two regulated variables the controlled vehicle can start smoothly. The vehicle test and simulation results on various road conditions have testified that the proposed method is effective and robust.