三轴重载车辆半主动控制研究

建立了三轴重载车辆半主动悬架的整车动力学模型,并对该车辆模型平顺性的优化控制进行了研究.应用总成分解法计算整车的转动惯量,建立半主动悬架系统的三轴重载车辆整车动力学模型,采用整体式平衡悬架,并考虑车辆的驾驶室模型以更符合实际;针对半主动悬架系统,以车体垂向速度﹑俯仰角速度和侧倾角速度及其变化率,为输入设计了三个模糊控制器;利用MATLAB/Simulink软件对所建半主动悬架整车车辆模型应用相应控制策略进行模拟仿真,并与被动悬架系统车辆模型的仿真结果作比较;结果表明：半主动悬架车辆模型的车体和驾驶室的振动特性都有了明显的改善,有效地提高了该重载车辆模型的平顺性,对提高驾驶员的驾驶舒适性和减少货物破损率提供理论基础.     

三轴重载车辆半主动控制研究

建立了三轴重载车辆半主动悬架的整车动力学模型,并对该车辆模型平顺性的优化控制进行了研究.应用总成分解法计算整车的转动惯量,建立半主动悬架系统的三轴重载车辆整车动力学模型,采用整体式平衡悬架,并考虑车辆的驾驶室模型以更符合实际;针对半主动悬架系统,以车体垂向速度﹑俯仰角速度和侧倾角速度及其变化率,为输入设计了三个模糊控制器;利用MATLAB/Simulink软件对所建半主动悬架整车车辆模型应用相应控制策略进行模拟仿真,并与被动悬架系统车辆模型的仿真结果作比较;结果表明：半主动悬架车辆模型的车体和驾驶室的振动特性都有了明显的改善,有效地提高了该重载车辆模型的平顺性,对提高驾驶员的驾驶舒适性和减少货物破损率提供理论基础.     

三轴重载车辆半主动控制研究

建立了三轴重载车辆半主动悬架的整车动力学模型,并对该车辆模型平顺性的优化控制进行了研究.应用总成分解法计算整车的转动惯量,建立半主动悬架系统的三轴重载车辆整车动力学模型,采用整体式平衡悬架,并考虑车辆的驾驶室模型以更符合实际;针对半主动悬架系统,以车体垂向速度﹑俯仰角速度和侧倾角速度及其变化率,为输入设计了三个模糊控制器;利用MATLAB/Simulink软件对所建半主动悬架整车车辆模型应用相应控制策略进行模拟仿真,并与被动悬架系统车辆模型的仿真结果作比较;结果表明:半主动悬架车辆模型的车体和驾驶室的振动特性都有了明显的改善,有效地提高了该重载车辆模型的平顺性,对提高驾驶员的驾驶舒适性和减少货物破损率提供理论基础.     

三轴重载车辆半主动控制研究

建立了三轴重载车辆半主动悬架的整车动力学模型,并对该车辆模型平顺性的优化控制进行了研究.应用总成分解法计算整车的转动惯量,建立半主动悬架系统的三轴重载车辆整车动力学模型,采用整体式平衡悬架,并考虑车辆的驾驶室模型以更符合实际;针对半主动悬架系统,以车体垂向速度﹑俯仰角速度和侧倾角速度及其变化率,为输入设计了三个模糊控制器;利用MATLAB/Simulink软件对所建半主动悬架整车车辆模型应用相应控制策略进行模拟仿真,并与被动悬架系统车辆模型的仿真结果作比较;结果表明：半主动悬架车辆模型的车体和驾驶室的振动特性都有了明显的改善,有效地提高了该重载车辆模型的平顺性,对提高驾驶员的驾驶舒适性和减少货物破损率提供理论基础.     

三轴重载车辆半主动控制研究

建立了三轴重载车辆半主动悬架的整车动力学模型,并对该车辆模型平顺性的优化控制进行了研究.应用总成分解法计算整车的转动惯量,建立半主动悬架系统的三轴重载车辆整车动力学模型,采用整体式平衡悬架,并考虑车辆的驾驶室模型以更符合实际;针对半主动悬架系统,以车体垂向速度﹑俯仰角速度和侧倾角速度及其变化率,为输入设计了三个模糊控制器;利用MATLAB/Simulink软件对所建半主动悬架整车车辆模型应用相应控制策略进行模拟仿真,并与被动悬架系统车辆模型的仿真结果作比较;结果表明：半主动悬架车辆模型的车体和驾驶室的振动特性都有了明显的改善,有效地提高了该重载车辆模型的平顺性,对提高驾驶员的驾驶舒适性和减少货物破损率提供理论基础.     

基于双模糊控制器的车辆半主动悬架仿真研究

设计了以簧载质量垂向速度、簧载质量俯仰角速度及其变化率作为模糊控制器输入的控制策略,基于该双模糊控制器的车辆半主动悬架能够综合改善车辆的垂向和俯仰振动.以某型车半车悬架模型作为研究对象,对采用双模糊控制器控制的半主动悬架系统进行了计算机仿真分析.仿真结果表明,采用双模糊控制器控制策略能较好地改善车辆乘坐舒适性,达到综合减振效果.     

汽车整车半主动悬架模块化并联模糊控制

针对汽车悬架复杂大系统的特点,在车辆动力学模型的基础上,研究半主动悬架系统对车身姿态的影响关系,运用模块化并联模糊控制和矩阵逆运算策略建立汽车整车半主动悬架控制系统;对不同的车身运动姿态采用不同的控制规则,设计整车车身姿态协调控制的模糊控制器.以某车型为例进行了仿真分析,结果表明,汽车整车半主动悬架模块化并联模糊控制器结构简单,并能有效抑制车身俯仰和侧倾运动,改善轮胎的接地性能,提高车辆的平顺性能.     

汽车电子稳定程序模糊控制仿真

关于汽车安全和稳定性问题,针对汽车电子稳定程序(Electronic stability program,ESP)是一种主动安全系统.为了提高汽车在转向工况下的侧向稳定性,采用快速控制原型的方法,建立了基于H.B.Pacejka 轮胎模型的三自由度整车模型和车辆参考模型.选取车辆横摆角速度和质心侧偏角作为综合控制变量,设计了模糊控制器,并对某款车型进行了离线仿真和在线实时仿真.结果表明,设计的控制器可以很好的控制汽车的横摆角速度和质心侧偏角.改进后的方法可以提高控制系统的实时性和稳定性,为ESP的快速开发提供了参考依据.     

车辆直行爆胎过程的模糊PI控制

车辆的爆胎过程是一个非常复杂的非线性过程,轮胎的力学特性会发生非常快的变化,理论和实验研究都非常困难。根据对爆胎车辆运动特性的分析,建立了二自由度车辆理想运动模型,利用刷子模型模拟爆胎过程中轮胎力学特性的变化,并在Simulink中搭建了七自由度整车模型,仿真车辆在高速直行情况下前轮或后轮发生爆胎后的运动状态。以车辆的横摆角速度作为控制变量,并考虑质心侧偏角的影响,设计了模糊PI控制器,利用差别制动的方式对爆胎车辆进行控制仿真,仿真结果表明,模糊PI控制器能够很好地控制车辆爆胎过程的稳定性,并且能够缩短制动距离。     

新型轮边驱动电动车平顺性仿真分析

为解决非簧载质量增加导致轮边驱动电动汽车平顺性下降问题,提出了一种包含磁流变半主动悬架的新型轮边驱动系统,并将其与四轮独立驱动电动车相耦合,建立了整车振动系统动力学模型,应用单一输入规则群模糊推理模型构建半主动悬架内环模糊控制器,使建立的车身运动外环控制器,通过变量转换器实现了内外环控制的有效连接,从而构建了整车平顺性双环模糊控制器。以某型四轮独立驱动电动车为例在随机路面激励下进行仿真,结果表明相对于被动悬架,双环模糊控制器结构简单,能够显著抑制车身振动,削弱了轮边驱动系统对电动车带来的垂向振动负效应,提高了行驶平顺性。     

基于LabVIEW的模糊控制器设计与仿真

以LabVIEW编程软件为基础,介绍了其中控制模块中模糊逻辑工具包(Fuzzy LogicToolkit)中的子程序(VI),并应用其中模糊逻辑控制器设计VI构建模糊控制器的方法。设置隶属度函数,建立模糊控制规则,创建模糊推理关系,实现对模糊控制器设计的具体步骤,并结合电液伺服模糊控制系统实例,利用模糊逻辑(Fuzzy logic)模块及LabVIEW提供的仿真模块(Simulation Module)建立系统仿真框图。通过仿真曲线,分析模糊控制器控制效果及其影响因素,从而大大缩短模糊控制器的设计周期,具有较大的工程实用价值。     

Target tracking using a hierarchical grey-fuzzy motiondecision-making method

This paper presents a hierarchical grey-fuzzy motion decision-making (HGFMD) algorithm, which is capable of integrating multiple sequential data for decision making and for the design of the control kernel of the target tracking system. The algorithm combines multiple grey prediction modules, each of which can estimate a suitable model from sequential sensory information for approximating the observed dynamic system for future-trend prediction and for decision making through a multilayer fuzzy logic inference engine. We have designed the HGFMD controller for a target tracking system and implemented it in our autonomous mobile robot. The HGFMD is compared with the conventional fuzzy logic controller, multilayer fuzzy controller, and the original grey-fuzzy controller developed previously in various target-tracking experiments. We demonstrated the high reliability of the HGFMD controller and tracking system even when encountering the uncertain status of slow sensory response time and the nonlinear motion behaviors of the target     

Modeling, control, and stability analysis for time-delay TLP systems using the fuzzy Lyapunov method

In this study, we present a Takagi–Sugeno (T–S) fuzzy model for the modeling and stability analysis of oceanic structures. We design a nonlinear fuzzy controller based on a parallel distributed compensation (PDC) scheme and reformulate the controller design problem as a linear matrix inequalities (LMI) problem as derived from the fuzzy Lyapunov theory. The robustness design technique is adopted so as to overcome the modeling errors for nonlinear time-delay systems subject to external oceanic waves. The vibration of the oceanic structure, i.e., the mechanical motion caused by the force of the waves, is discussed analytically based on fuzzy logic theory and a mathematical framework. The end result is decay in the amplitude of the surge motion affecting the time-delay tension leg platform (TLP) system. The feedback gain of the fuzzy controller needed to stabilize the TLP system can be found using the Matlab LMI toolbox. This proposed method of fuzzy control is applicable to practical TLP systems. The simulation results show that not only can the proposed method stabilize the systems but that the controller design is also simplified. The effects of the amplitude damping of the surge motion on the structural response are obvious and work as expected due to the control force.     

Fuzzy controller for wall-climbing microrobots

This paper presents a fuzzy control system that incorporates sensing, control and planning to improve the performance of the wall-climbing microrobots in unstructured environments. After introduction of the robot system, a task reference method is proposed which is based on a fuzzy multisensor data fusion scheme. The method provides a novel mechanism to efficiently integrate task scheduling, action planning and motion control in a unified framework. A robot gait generation method is described which switches the robot locomotion between different motion modes with the help of a finite state machine driven by sensory information. A fuzzy motion controller is designed to improve control performance and reduce power consumption by the suitable selection of fuzzy sets and inference methods, as well as the definition of corresponding membership functions and control rule bases. A fuzzy logic compensator is developed to compensate the gravitational effects according to different robot configurations and task situations. Experimental results prove the validity of the proposed methods.     

高速公路非线性反馈模糊逻辑匝道控制器

入口匝道控制是高速公路交通控制和智能运输系统的重要组成部分,但现有的入口匝道控制效果尚不理想.为此,本文提出一种非线性反馈方法用模糊逻辑进行入口匝道控制.建立了高速公路交通流动态模型,在此基础上,结合模糊逻辑理论设计了非线性反馈匝道控制器,根据密度误差和误差变化用模糊控制决定匝道调节率,模糊变量选用三角形隶属度函数,并制定了包含56条模糊规则的规则库,最后用MATLAB软件进行系统仿真.结果表明该控制器具有优越的动态和稳态性能,它能使高速公路主线交通流密度保持为设定的期望密度,该方法用在高速公路入口匝道控制中效果良好.     

Fuzzy sliding mode control of a finger of a humanoid robot hand

Abstract: The motion control problem for the finger of a humanoid robot hand is investigated. First, the index finger of the human hand is dynamically modelled as a kinematic chain of cylindrical links. During construction of the model, special attention is given to determining bone dimensions and masses that are similar to the real human hand. After the kinematic and dynamic analysis of the model, in order to ensure that the finger model tracks its desired trajectory during a closing motion, a fuzzy sliding mode controller is applied to the finger model. In this controller, a fuzzy logic algorithm is used in order to tune the control gain of the sliding mode controller; thus, an adaptive controller is obtained. Finally, numerical results, which include a performance comparison of the proposed fuzzy sliding mode controller and a conventional sliding mode controller, are presented. The results demonstrate that the proposed control method can be used to perform the desired motion task for humanoid robot hands efficiently.     

基于模糊控制的某教练机飞行姿态控制器设计

提出了一种基于规则的模糊逻辑飞行控制系统的设计方法,用以解决某教练机训练系统中数学模型时变性和不确定性问题.为了避免建模的困难,某教练机飞控系统采用模糊逻辑控制设计其控制律,结合飞行员的操纵经验,对系统进行动态调整.以俯仰角为研究对象,利用MATLAB中的fuzzy工具箱实现了模糊控制器设计,给出了俯仰角模糊控制器的控制曲面视图,并在SIMULINK仿真环境下建立了仿真模型.结果表明,所设计的模糊逻辑控制器满足操作品质的要求,具有较好的鲁棒性,对教练机驾驶训练仿真平台的飞行控制系统设计具有一定的应用价值.     

遗传算法和模糊控制的融合研究与设计

将遗传算法与模糊控制相结合，能达到取长补短的作用，一方面，模糊控制能够表达非线性和模糊性的系统知识；另一方面，通过遗传算法所拥有的高度并行，随机、全局搜索能力，使控制系统具有了学习能力，提出了先使用模糊逻辑的思想进行交叉概率和变异概率的整定，再利用模糊遗传算法对模糊子集进行了划分，可获得一个基于一定性能指标的次优或最优模糊控制器，又以二阶系统为例进行了计算机仿真，研究结果表明这种方法是有效的。     

A self-organizing fuzzy logic controller for dynamic systems usinga fuzzy auto-regressive moving average (FARMA) model

The paper proposes a complete design method for an online self-organizing fuzzy logic controller without using any plant model. By mimicking the human learning process, the control algorithm finds control rules of a system for which little knowledge has been known. In a conventional fuzzy logic control, knowledge on the system supplied by an expert is required in developing control rules, however, the proposed new fuzzy logic controller needs no expert in making control rules, Instead, rules are generated using the history of input-output pairs, and new inference and defuzzification methods are developed. The generated rules are stored in the fuzzy rule space and updated online by a self-organizing procedure. The validity of the proposed fuzzy logic control method has been demonstrated numerically in controlling an inverted pendulum     

A self-learning fuzzy logic controller using genetic algorithmswith reinforcements

This paper presents a new method for learning a fuzzy logic controller automatically. A reinforcement learning technique is applied to a multilayer neural network model of a fuzzy logic controller. The proposed self-learning fuzzy logic control that uses the genetic algorithm through reinforcement learning architecture, called a genetic reinforcement fuzzy logic controller, can also learn fuzzy logic control rules even when only weak information such as a binary target of “success” or “failure” signal is available. In this paper, the adaptive heuristic critic algorithm of Barto et al. (1987) is extended to include a priori control knowledge of human operators. It is shown that the system can solve more concretely a fairly difficult control learning problem. Also demonstrated is the feasibility of the method when applied to a cart-pole balancing problem via digital simulations