基于模糊控制实现采煤机的自动化调速控制

为了提高采煤机应对煤质变化较大的开采环境，提高采煤机的截割效率，保护电机，将模糊控制应用于采煤机的牵引电机调速系统中。分析了牵引部的调速原理，在此基础上分析了采煤机的负载特性；设计了模糊控制器；在仿真软件中比较了传统PID控制器和模糊控制器的控制效果，结果表明模糊控制器的控制效果更优。     

皮带传动的计算机模糊控制

对皮带传动的模糊控制方法进行了研究，并将模糊控制分为四个步骤。第一，偏差量和控制量的模糊化；第二，求解模糊关系；第三，模糊推理；第四，解模糊。     

半主动悬架控制与整车性能匹配研究

建立了两自由度汽车半主动悬架系统的数学模型，以提高乘坐舒适性而不损害行车安全性作为控制策略的出发点，采用自适应模糊控制方法，该控制方法将模糊系统辩识和模糊控制结合起来，针对自适应算法的复杂性，采用最大隶属度法对控制规则进行修正，简化了运算。仿真表明，与被动悬架和模糊控制相比，可显著地减少汽车振动及干扰，操纵稳定性得到改善，可较好解决乘坐舒适性和操纵稳定性矛盾，使汽车在各种行驶条件下舒适性和安全性最佳匹配。     

用FCGA优化的四辊冷轧机恒张力模糊控制系统

为了保证冷轧机轧制中带钢恒张这一特点，设计了四辊冷轧机恒张力模糊控制系统。该系统含有电压、电流和速度三个内环，最外环为张力环；电压环和电流环采用一维模糊控制器控制，速度环和张力环采用二维模糊控制器控制。为了加强模糊控制的自适应性，采用了一种模糊控制的遗传算法将外张力模糊控制器的隶属参数进行优化。理论分析和仿真结果都表明该系统对带钢恒张控制具有很强的鲁棒性和实时性，即使在变工况下（大范围变负荷下）也保持了良好的控制性能。     

模糊控制磁流变减振器在起落架中的应用探析

建立以磁流变液为介质的飞机起落减震系统模型，应用模糊控制理论，设计出该模型的模糊控制器，实现对减震器系统的半主动控制。通过仿真得出半主动模糊控制的减震效果优于被动控制的减震系统，有效减少了系统的震动。在此基础上，探讨了基于模糊控制的磁流变减震器在飞机起落架系统中应用的可行性。     

电子膨胀阀在冷水机组中的试验研究

介绍了控制电子膨胀阀的两种控制技术。将模糊控制技术应用于电子膨胀阀控制中，设计出模糊控制器并应用到冷水机组中；同时提出一种多单元式冷水机组节能优化运行模式并进行了试验。试验结果表明，模糊控制器具有良好的控制效果，其节能模式具有较大的节能效果和潜力。     

特种运输车装备主动悬架振动控制策略研究

为提高某型特种运输车辆装备的抗振性能,建立了六自由度装备与车辆系统动力学模型并简化为五自由度装备主动悬架模型。根据PID控制与模糊控制的各自特点,设计了模糊-PID控制器并对装备与车辆系统进行了振动控制。该控制器的工作原理是利用模糊控制促成了PID调节过程中参数的自动调整,来适应变化复杂的情况,使被运送装备的振动性能得到了改善。在白噪声模拟C级路面作为随机激励和矩形冲击激励2种工况下,对装备与车辆系统进行了动力学仿真分析。结果表明,相对于模糊控制和PID控制策略,模糊-PID控制对装备的振动加速度、速度、动位移控制性能更优,可以有效地提高装备运输的安全性。     

基于混沌退火算法的非线性系统Fuzzy控制规则自调整

非线性系统模糊控制实际应用中存在控制精度和自适应能力问题，模糊控制规则的自调整和自寻优，是提高和改善模糊控制器性能重要手段。带有修正因子的控制规则能够根据误差E和误差变化EC自动产生规则，利用全局寻优的混沌模拟退火算法对修正因子优化，优化模糊规则；并引入综合考虑误差、误差绝对值时间、控制能量、超调诸因素的性能函数。非线性系统仿真实例说明该方法响应上升时间快，超调小提高了模糊控制器性能。     

开关磁阻电机的模糊自适应简化控制

为解决开关磁阻电机运行中存在的强耦合、非线性等问题,将模糊自适应控制技术应用到开关磁阻电机调速控制中。通过分析运行中速度误差和误差增量对模糊控制输出量的影响,同时考虑到开关磁阻电机自身的特性,建立了带有校正因子的输出量与误差、误差变量之间的关系,在兼顾计算量和性能的基础上,提出了一种简化的校正因子选择方法,从而实现了根据运行条件在线改变模糊规则。同时,在一台8/6四相开关磁阻电机上分别采用传统PI控制和简化模糊自适应方法进行了试验验证。对比研究结果证明,简化的模糊自适应方法能够有效提高开关磁阻电机调速的鲁棒性。通过使用Matlab仿真,验证了校正因子对模糊控制的影响,简化的模糊自适应方法能够应用于不同的情况,具有较好的动静态性能。     

模糊控制系统的辨识及稳定性保证问题

模糊控制系统的设计面临着两个难题,一是隶属度函数及模糊控制规则的适应性问题;二是稳定性分析问题。为解决这两个问题。受经典控制利用控制对象的数学模型分析系统稳定性的启示。构造模糊对象的模糊模式来分析模糊系统的稳定性,并提出型式控制GA,用以解决模糊模式的系统辨识问题。     

基于Takagi-Sugeno模型的嵌入式模糊控制器设计

在对一类模糊控制器的实现方式进行了分析的基础上，针对嵌入式系统的特点，提出了一种基于Takagi—Sugeno模型的工程化实时模糊控制算法，并将其应用于小型数控机床的主轴电机驱动系统设计。实验结果表明，该嵌入式模糊控制系统具有实时性强、响应速度快、精度高的特点，在现场控制中有效可行。     

T–S fuzzy model predictive speed control of electrical vehicles

This paper proposes a novel nonlinear model predictive controller (MPC) in terms of linear matrix inequalities (LMIs). The proposed MPC is based on Takagi–Sugeno (TS) fuzzy model, a non-parallel distributed compensation (non-PDC) fuzzy controller and a non-quadratic Lyapunov function (NQLF). Utilizing the non-PDC controller together with the Lyapunov theorem guarantees the stabilization issue of this MPC. In this approach, at each sampling time a quadratic cost function with an infinite prediction and control horizon is minimized such that constraints on the control input Euclidean norm are satisfied. To show the merits of the proposed approach, a nonlinear electric vehicle (EV) system with parameter uncertainty is considered as a case study. Indeed, the main goal of this study is to force the speed of EV to track a desired value. The experimental data, a new European driving cycle (NEDC), is used in order to examine the performance of the proposed controller. First, the equivalent TS model of the original nonlinear system is derived. After that, in order to evaluate the proficiency of the proposed controller, the achieved results of the proposed approach are compared with those of the conventional MPC controller and the optimal Fuzzy PI controller (OFPI), which are the latest research on the problem in hand.     

H∞ output-feedback fuzzy proportional-integral control of fully delayed input/output systems

This paper presents the output-feedback fuzzy proportional-integral (PI) controller design for uncertain nonlinear systems with both fully delayed input and output. Based on the Takagi–Sugeno (T–S) fuzzy model representation, the output-feedback PI control is realized via parallel distributed PI compensation and novel LMI gain design. Although the T–S fuzzy PI controller is simple, asymptotic output regulation is assured to overcome the effect of uncertainty, state delay, and full input/output delays. When considering disturbance and measurement noise, the control performance is achieved by robust gain design. Furthermore, state observers and bilinear matrix inequality conditions are removed in this paper. Finally, time-delay Chua׳s circuit system and a continuous-time stirred tank reactor are taken as applications to show the expected performance.     

Takagi–Sugeno fuzzy model based robust dissipative control for uncertain flexible spacecraft with saturated time-delay input

In this paper, the problem of robust dissipative control is investigated for uncertain flexible spacecraft based on Takagi–Sugeno (T–S) fuzzy model with saturated time-delay input. Different from most existing strategies, T–S fuzzy approximation approach is used to model the nonlinear dynamics of flexible spacecraft. Simultaneously, the physical constraints of system, like input delay, input saturation, and parameter uncertainties, are also taken care of in the fuzzy model. By employing Lyapunov–Krasovskii method and convex optimization technique, a novel robust controller is proposed to implement rest-to-rest attitude maneuver for flexible spacecraft, and the guaranteed dissipative performance enables the uncertain closed-loop system to reject the influence of elastic vibrations and external disturbances. Finally, an illustrative design example integrated with simulation results are provided to confirm the applicability and merits of the developed control strategy.     

Navigation of autonomous mobile robot using adaptive network based fuzzy inference system

Intelligent soft computing techniques such as fuzzy inference system (FIS), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are proven to be efficient and suitable when applied to a variety of engineering systems. The hallmark of this paper investigates the application of an adaptive neuro-fuzzy inference system (ANFIS) to path generation and obstacle avoidance for an autonomous mobile robot in a real world environment. ANFIS has also taken the advantages of both learning capability of artificial neural network and reasoning ability of fuzzy inference system. In this present design model different sensor based information such as front obstacle distance (FOD), right obstacle distance (ROD), left obstacle distance (LOD) and target angle (TA) are given input to the adaptive fuzzy controller and output from the controller is steering angle (SA) for mobile robot. Using ANFIS tool box, the obtained mean of squared error (MSE) for training data set in the current paper is 0.031. The real time experimental results also verified with simulation results, showing that ANFIS consistently perform better results to navigate the mobile robot safely in a terrain populated by variety obstacles.     

Resilient guaranteed cost control for uncertain T–S fuzzy systems with time-varying delays and Markov jump parameters

This paper aims to investigate the problem of resilient guaranteed cost control for uncertain Takagi–Sugeno fuzzy systems with Markov jump parameters and time-varying delay. A resilient mode-dependent fuzzy controller is designed and a weak sufficient condition is developed to ensure that the resulting closed-loop system is robust almost surely asymptotically stable with guaranteed cost index not exceeding the specified upper bound. Subsequently, the controller gain and upper bound of the guaranteed cost index can be obtained by solving a set of linear matrix inequalities. Finally, numerical and practical examples of the single-link robot arm system are provided to demonstrate the performance of the proposed approach.     

基于模糊自适应PID的摆式列车倾摆控制系统研究

针对摆式列车伺服倾摆系统大惯性、非线性和时变性等特点,结合PID和模糊控制两者的优点,提出了一种模糊自适应PID控制方法。对模糊自适应PID算法进行了理论分析,对摆式列车的简化模型做了仿真研究。结果表明,采用模糊自适应PID控制,系统的调节时间缩短,响应速度加快,抗干扰能力和适应参数变化的能力都优于常规PID控制,具有更好的动态特性和稳定性。     

汽车磁流变悬架垂直振动控制与试验研究

在对整车振动系统进行分解和简化的基础上，提出一种分级智能控制系统，设计了用仿人智能思想来在线修改模糊控制器参数的1/4车磁流变悬架振动模糊自适应局部控制器，设计了整车悬架垂直振动的协调控制规则用于调整4个局部控制器的输出值。在MATLAB平台上对分级控制系统进行仿真，构建了磁流变悬架系统垂直振动的整车测控与评价系统，在不同条件下进行了道路试验。相对于被动悬架，分级控制的磁流变悬架使汽车底板振动加速度下降了约15％，使座椅振动加速度下降了约23％，表明用分级控制来减小磁流变悬架系统的垂直振动是可行的，可降低因模型的简化带来的影响，提高汽车的平顺性。     

Neural network controller for robotic motion control

Since a robotic manipulator has a complicated mathematical model, it is difficult to design a control system based on the complicated multi-variable nonlinear coupling dynamic model. Intelligent controllers using fuzzy and neural network approaches do not need a real mathematical model to design the control structure and have attracted the attention of robotic control researchers recently. A traditional fuzzy logic controller does not have learning capability and it needs a lot of effort to search for the optimal control rules and the shapes of membership functions. Owing to the time-varying behaviour of the system, the required fine tracking accuracy is difficult to achieve by adjusting the fuzzy rules only. The implementation problems of neural network control are the initial training and initial transient stability. In order to improve the position control accuracy and system robustness for industrial applications, a neural controller is first trained off-line by using the input and output (I/O) data of a traditional fuzzy controller. Then the neural controller is implemented on a five-degrees-of-freedom robot with a back propagation algorithm for online adjustment. The experimental results show that this neural network controller achieved the required trajectory tracking accuracy after 15 on-line operations.     

基于电液比例位置系统的模糊自整定PID控制器

针对FESTO TP701电液比例实验台的比例阀控活塞位置控制系统进行了建模仿真,设计了模糊自整定PID参数控制器。运用基于MATLAB中的模糊工具箱和simulink进行仿真研究,结果表明控制器结构简单,系统实时性和稳态精度都得到改善。