飞思卡尔智能车大奖赛（电磁组2）软件控制系统设计与开发开题报告.docx
飞思卡尔智能车大奖赛（电磁组2）软件控制系统设计与开发
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飞思卡尔智能车大奖赛（电磁组2）软件控制系统设计与开发,卡尔,智能,大奖赛,电磁,软件,控制系统,设计,开发
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基于PID和模糊逻辑车辆控制策略分析控制Hui-min Lia, *, Xiao-bo Wangb, Shang-bin Songa, Hao Lia摘要: 建立二自由度车辆动力学模型，根据PID理论控制与模糊逻辑控制，通过使用横摆力矩控制与不同控制策略的方法来实现车辆的稳定性控制。通过对比和分析PID控制与模糊逻辑控制算法影响，得到如下结论：根据对比控制效果，偏移角和偏移率结合的控制优于单一的偏移角和偏移率控制；模糊逻辑控制拥有一个更好的鲁棒性而以分析控制理论PID控制更是简单与实用。依据实际的控制需要，不同的控制方法可能用于相同的控制系统中。此结果可以提高并增强客车的可操作性与稳定性，同时在某一程度上会给予其一定的参考。简介车辆的稳定性控制（VSC）在90年代得到发展，它是一种新的安全控制系统，当车辆在转向或者是受到侧向力时，它会适应与匹配轮胎纵向力，使它拥有更好的可操作性与稳定性。对于轮胎的非线性特征，车辆的可操作性与稳定性控制的主要方法是从四轮转向控制（4WS）最初的直接横摆力矩控制和主动前轮转向开发控制（AFS）。在特殊情况下，在车辆可操作性与稳定性控制条件下DYC已经成为一种更有效的控制方法了。根据不同的控制方法，偏移率，偏移角度，横向加速度轮胎的滑移率与各个控制要素的结合被用作不同的控制变量。控制理论的应用从PID控制，最优控制和适应性控制发展到滑移系统中的多变量结构控制，模糊逻辑控制和人工神经网络控制等等。建立两种自由度的车辆动力学模型，根据通过使用PID 与模糊逻辑控制的DYC控制方法，滑移角度与偏移率被用作控制变量。不同控制理论与控制方法的特征与影响会通过模拟进行对比与分析。1. 车辆模型的建立控制系统根据二自由度的线性车辆模型而设计。车辆运动依照不同的等式：式子中的M为车辆质量，V为其速度，Yf为其前轮所受侧向力，Yr是后轮所受到的侧向力，是滑移角，r为滑移率，I是垂直轴时刻的惯性，If与Ir是前车轴与后车轴到车辆中心质量的距离。轮胎所受侧向力公式:根据拉普拉斯变换，转换两个自由度线性等式方程，得到如下等式两个自由度的线性车辆模型原生率可以被用作名义的车辆偏移率，在等式(4)中可以看到；为随后的在轮胎附着力的限制侧向力的条件，车辆理想原生率必须受到路附着系数的限制，满足其约束。(5)当滑移角非常小时，侧向加速度可以有以下公式表示（6）：车辆理论上的偏移率必须满足下等式（7）；名义上的偏移率可由等式（8）得到：二自由度线性车辆模型滑移角可当用于车辆滑移角，由等式（9）得到；考虑到轮胎与道路附着系数之间的密切关系，滑移角由等式（10）表示；一下的等式源于等式（10）；名义滑移角的最小绝对值应该落在N 与nmax之间。控制系统的设计是基于两个自由度线性车辆模型。正如它是基于采用横摆力矩车辆的不同控制算法，两个自由度线性车辆模型依据下面等式是正确的。这是Mz是添加的横摆动量。2. 控制系统的设计2.1. PID控制角速度图一反应了角速度的反馈控制，角速度传感器传输车辆角速度与名义角速度的不同到控制器中。当输入变量改变，控制器会调节角动量。接着制动力会作用在每个轮胎上，增强型PID控制算法会被使用到，有关它的等式符合以下关系：2.2. PID角度滑移控制 图二反应了角度滑移反馈控制。滑移角在大多数车辆稳定控制系统中作为控制变量。理想滑移角与真实滑移角的差值被当作输入控制器变量：2.3. 模糊逻辑控制 双公路列车的结果为留在一级公路路口模拟显示在图 8。模糊逻辑的反馈控制示于图3。在偏移率和名义偏移率N之间的误差e和误差Ec，被用作为逻辑控制器的输入变量，输出变量为相应的动量变化M6.在理想滑移角与真实滑移角之间的不同误差e和误差率ec，被用作模糊逻辑控制的输入变量，所对应的输出变量是偏移调整动量M，在图4中展示。根据逻辑控制器的设计需要，模糊变量E,EC和U的定量领域被如下定义。E和EC的模糊集定义为NB, NM, NS, ZE, PS, PM, PB.U的模糊集定义为ZE, PS, PM, PB共同的全集模糊定义为：E and EC -0.1, -0.8, -0.6, -0.4, -0.2, 0, 0.2, 0.4, 0.6, 0.8, 1; U 0, 0.1, 0.2, 0.3,0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1; E 与EC -0.1, -0.8, -0.6, -0.4, -0.2, 0, 0.2,0.4，0.6, 0.8, 1; U 0, 0.1, 0.2, 0.3, 0.4,0.5, 0.6, 0.7, 0.8, 0.9, 1.为了使全集变量误差e，误差率ec和控制变量u标准化，量子化因素和比例因子给予以下定义。把量子化误差变量定义为Ke=e-1把量子化误差率定义为Kec=ec-1控制变量的比例因子定义为Ku=u-1采用三角函数输入语言变量和输出语言变量，可以有更好的模拟效果和更高的精度的操作简单，如图5 图7所示。模糊逻辑控制规则可以由条件语句“ifthen”来表达，这代表了来源于一些前提条件的改变。总体来说，模糊逻辑控制形式被用作表达这些规则，正如表一所示。两个输入各自有七个模糊语言变量，因此有49种方式。规则1：如果R是NR 且RC 是 NR，则NR是PR：规则2：如果E是NB 且 EC是NM ，则U 是PB规则49：如果E是PB 且 EC是PB ，则U 是PB;NB是负大值，NM是负中值，NS为负小值，ZE几乎为零；PS为正小值，PM为正中值；PB为正大值。马丹妮方法被用于模糊逻辑控制，用最大最小方法做推导，用二等分线方法当去模糊化处理。在matlab的模糊控制工具作为模糊控制器。输入与输出的模糊控制表面在图8中2.4. 综合控制通常情况下，依据控制参数的调整，对于外界干扰PID控制不能获得理想的控制效果。在PID控制中，偏移率的控制变量应该要通过模糊逻辑控制器的调节来控制。实际中，角偏移与角偏移率被传感器所估量与测量。因此偏移率的误差被PID控制器作为输入参数，偏移角度误差被模糊逻辑控制器作为输入参数使用。整合了模糊PID控制的系统结构在图9中展示。增强型PID控制算法可以用作PI控制。PID控制参数Kp和Ki通过即时的模糊控制器调节2.5. 临界值控制 根据PID偏移率控制算法和结合了逻辑控制的滑移角度模糊控制，建立起关于偏移率与偏移角模型，在图10中展现。该控制器并不影响其独立性，但只有增加的阈值规则的控制。当车辆需要制动时，控制器控制滑移角满足约束和滑移率PID控制器控制车辆的行驶。当滑移角不满足约束，这是需要通过滑移角模糊逻辑控制器控制车辆的驾驶。所有上述是基于稳定的公差。3. 模拟分析中型车辆被选为模拟试验车辆。车辆速度为70公里每小时和方向盘角在角度步骤输入70度设置。仿真曲线的实线是原来没有控制，虚线由控制得到的。 3.1. PID 控制PID控制的偏移率和滑移角的模拟展示在图11，响应指数的比较在表二。PID控制偏移率稳定值下降10.07，超调量明显下降。响应间也减少了显然，很明显，反应速度增加。 PID控制的滑移角稳定值下降12.52，略超调增大。响应时间明显增加，反应速度明显下降。3.2. 模糊逻辑控制双公路列车右转上二级公路路口模拟结果，显示在图12。横摆率和滑移角的模糊逻辑控制模拟，示于图 12与响应的比较指数示于表3。模糊逻辑控制的偏移率稳定值下降14.50，超调明显增大。 响应时间明显下降，响应速度明显增加。模糊逻辑的车辆稳态值的偏移角速度控制下降14.75，超调略增大。响应时间略有增加，响应速度明显增加。3.3. 综合模拟 双公路列车转向对二级公路交叉口的结果仿真如图13所示和响应指数的比较，如表4所示。 组合控制下偏移率稳定值降17.04，超调明显增大。响应时间明显下降，响应速度明显增加。在联合控制模糊逻辑控制的侧滑角稳定值下降17.07，超调量明显下降。响应时间略有增加，响应速度略有下降。3.4. 临界值控制PID控制临界值和模糊逻辑控制偏移率与模糊逻辑控制滑移角在图14中，响应指数的比较在表5。 在逻辑临界下，PID控制中的偏移率稳定值下降19.08，超调量略有下降。 响应时间明显下降，响应速度明显增加。在逻辑临界下，模糊逻辑的偏角稳态值下降14.75，超调略增大。 响应时间明显增加，响应速度明显下降。4. 结论结论可以从方向盘角度阶跃输入模拟得出，没有可以满足车辆行驶的所有条件，并取得了良好的效果的控制理论与控制策略。但是在一般的操作条件下，有必要研究更有效的理论方法和控制策略。例如，当车辆打滑和不稳定时，有必要施加横摆力矩的控制。它可以控制滑移角的大小和有效的偏移率。在这种情况下，横向加速度不会超过后表面附着的极限。如果充分满足上述条件时，控制较好；若基本满足，控制效果一般；若不满足，该控制是不太有效。当车辆是中高速行驶时，滑移角才是相对大的，车辆显示主要动态特点。控制的主要目的是为稳定，因此横摆率控制可以实现更好的控制。随着角度的增加，当单独控制偏移率时候，其结果将会变得糟糕。在高附着地面系数下，联合控制能够有效地控制滑移角和滑移率并且满足车辆动态特征。参考书目；1Zhou, H.N., 2007. Study on Vehicle Stability Control Strategy, Journal of Hubei Automotive Industries Institute, 21,26-31.2Yu, Z.S., 2000. Automobile theory (The third edition). Beijing: Machinery Industry Press3Abe, M., 1998 Vehicle movement and manipulation, Machinery Industry Press4Yao, S.Y., 2008. Study on vehicle stability control system, Xihua University, pp.16-275Yang, S.Z., Yang K.C., 2005. Mechanical Engineering Control Basis, Huazhong science university Press6Zhu, J., 2005. Fuzzy Control Theory and System Principle, Machinery Industry Press外文二：Vehicle Control Strategies Analysis Based on PID and Fuzzy Logic ControlHui-min Li a, *, Xiao-bo Wang b , Shang-bin Song a , Hao Li aa Research Institute of Highway Ministry of Transport, Beijing 100088, Chinab China National Construction Machinery Quality Supervision Testing Center, No.55 Dong Wai Street YanQing City Beijing 102100, ChinaAbstractTwo degrees of freedom vehicle dynamic model is established. Based on theories of PID control and fuzzy logic control, controller of vehicle stability is designed by using the method of direct yaw moment control and the different control strategies. By comparing and analyzing control effect of PID control and fuzzy logic control, the result shows as follows: slip angle and yaw rate combined control is better than slip angle and yaw rate controlled individually by comparing control effect; fuzzy logic control have a better robustness and PID control is simple and practical by analyzing control theories. Different control methods can be used in the same control systems according to the need of practical application. The result can improve and enhance passenger car maneuverability and stability control and also can give some reference in a way. Peer-review under responsibility of the Department of Transportation Engineering, Beijing Institute of Technology.Keywords: Vehicle dynamic; Control strategies; PID control; Auto Turn; Fuzzy logic control.1. IntroductionVehicle Stability Control (VSC) is developed during the 90s. Its a new active safety control system with a better maneuverability and stability by regulating and matching tire longitudinal force when vehicle is steering or under the lateral force. For tire nonlinear characteristic, main methods of vehicle maneuverability and stability control are developed from four-wheel steering control (4WS) initial to direct yaw moment control and active front steering control (AFS). In particular, DYC has become a more effective method in vehicle maneuverability and stability control. According to the difference control strategies, yaw rate, slip angle, lateral acceleration tire slip ratio and combination of them are used as control variables 1. The application of control theories is developed from PID control, optimal control and adaptive control to variable-structure control system with sliding mode, fuzzy logic control and artificial neural network control and so on. Two degrees of freedom (2 DOF) vehicle dynamic model is established. Slip angle and yaw rate are used as control variables based on control method of DYC by using control theories of PID and fuzzy logic control. The characteristics and effect of different control theory and control strategy are compared and analyzed by simulation.2. Establishment of Vehicle ModelControl system is designed based on two degrees of freedom liner vehicle model 2,3. Vehicle movement differential equations are given as follows:Where, M is mass of vehicle, V is vehicle velocity, Yf is lateral force of front tire, Yr is the lateral force of rear tire, is side slip angle, r is yaw rate, I is inertia of vehicle around the vertical axis moments, lf and lr are the distance between the center of mass with front axle and rear axle.Equations about lateral force of tire are given as follows:Transfer functional equations of two degrees of freedom liner vehicle model are given though Laplace transformation shown as Equ. (1).Where,Raw rate of two degrees of freedom liner vehicle model can be used as vehicle nominal yaw rate, as shown in Equ. (4).Vehicle ideal raw rate must be restricted by road adhesion coefficient and satisfied the constraints as followed on the condition of lateral force in tire adhesion limitation.When slip angle is very small, lateral acceleration can be expressed as followsVehicle ideal yaw rate must be satisfied the following equation.Nominal yaw rate is shown as Equ. (8).Slip angle of two degrees of freedom liner vehicle model can be used as vehicle nominal slip angle, as shown in Equ. (9).Considering the restriction between tire and maximum road adhesion coefficient, slip angle is expressed in Equ.(10).The following equation can be derived from Equ. (10)Nominal slip angle should be the minimum absolute value N between and nmax.The design of control system is based on two degrees of freedom liner vehicle model. As it is based on different control algorithms applying yaw moment to vehicle, two degrees of freedom liner vehicle model is corrected as follows.Where Mz is additional yaw moment.3. Design of Control System3.1. PID control of yaw rateFeedback control of yaw rate is shown in Fig. 1. Yaw rate sensor is used to transit the difference between vehicle yaw rate and nominal yaw rate to controller 4,5.When input variables changed, yaw moment will be adjusted by controller. Then brake force is distributed to every wheel. Increasing PID control algorithm is used and its equations are given as follows:3.2 PID control of slip angleFeedback control of slip angle is shown in Fig. 2. Slip angle is used as control variable in most vehicle stability control systems. The difference between ideal slip angle and the actual one is used as input to controller.3.3. Fuzzy logic controlResult of double road train turns left on first class highway intersection simulation is shown in Fig. 8.Feedback control of fuzzy logic is shown in Fig. 3.The error e and its rate ec of difference between yaw rate and nominal yaw rate N are used as input variable of fuzzy logic controller, output variable is yaw adjusted momentM6.The error e and its rate ec of difference between ideal slip angle and the actual one are used as input variable of fuzzy logic controller, output variable is yaw adjusted moment M, as shown in Fig.4 (Zhu, 2005).According to the need of fuzzy logic controller design, quantificational field of fuzzy variables E、EC and U is defined as follows 4.The fuzzy sets of E and EC are defined as NB, NM, NS, ZE, PS, PM, PB.The fuzzy set of U is defined as ZE, PS, PM, PB.Universes of them are defined as: E and EC -0.1, -0.8, -0.6, -0.4, -0.2, 0, 0.2, 0.4, 0.6, 0.8, 1; U 0, 0.1, 0.2, 0.3,0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1; E and EC -0.1, -0.8, -0.6, -0.4, -0.2, 0, 0.2, 0.4?0.6, 0.8, 1; U 0, 0.1, 0.2, 0.3, 0.4,0.5, 0.6, 0.7, 0.8, 0.9, 1.In order to make universe of variable error e, error rate ec and control variable u to correspond the standardization, quantization factor and scale factor are defined as follows.The quantization factor of variable error is defined as Ke=e-1.The quantization factor of the error rate is defined as Kec=ec-1.The scale factor of control variable is defined as Ku=u-1.Triangular membership functions are adopted to input linguistic variable and output linguistic variable, which operates simply with a better effect of simulation and a higher precision, as shown from Fig. 5 to Fig.7.The rules of fuzzy logic control can be expressed by conditional statement of “ifthen”, which represent decision result derived from many change premises. In general, fuzzy logic control form is used to express those rules, as shown in table 1.Two inputs have seven fuzzy linguistic variables separately, so there are 49 rules in total as follows.Rule 1：if R is NR and RC is NR then U is PRRule 2：if E is NB and EC is NM then U is PBRule 49：if E is PB and EC is PB then U is PBWhere, NB is Negative Big; NM is Negative Medium; NS is Negative Small; ZE is Almost Zero; PS is Positive Small; PM is Positive Medium; PB is Positive Big.Method of “Mamdani” is used for fuzzy logic control, and “max-min” is used for fuzzy reasoning, bisector of area is used for defuzzification method. Fuzzy control toolbox in Matlab is used for fuzzy logic controller. Input and out input surface of fuzzy controller is showed in Fig. 8.3.4. Combined controlIn general, PID control cant get the ideal control effect for interfered by the method of control parameter adjustment. Control variable of yaw rate in PID control should be adjusted in fuzzy logic controller. In practice, slip angle is estimated and yaw rate can be measured by the sensor. So the error of yaw rate can be used for PID controller input parameter, error of slip angle can be used for fuzzy logic controller input parameter. The system structure of combined fuzzy PID control is showed in Fig. 9.Increasing PID control algorithm is used for PI control 5.PID control parameters KP and KI are adjusted by fuzzy controller real-time.3.5. Threshold controlBased on the algorithm of yaw rate PID control and slip angle fuzzy control, combined PID fuzzy logic controller is established about yaw rate and slip angle, as shown in Fig.10. The controller doesnt affect their independence but only increases control of the threshold rules. When vehicle need to brake, controller control slip angle to satisfy constraints and control vehicles driving by yaw rate PID controller. When slip angle doesnt satisfy constraints, it is necessary to control vehicles driving by slip angle fuzzy logic controller. All above is based on steady tolerance.4. Simulation AnalysisMedium vehicle is selected as simulation test vehicle. Vehicle speed of 70 km / h and the steering wheel angle of 70 deg in the angle step input are set. The solid line of simulation curves is original with no control, the dashed line is obtained by control.4.1. PID controlPID control simulation of yaw rate and slip angle is shown in Fig. 11 and the comparison of response index is shown in table 2.Yaw rate steady value of PID control decreases 10.07%, overshoot decreases obviously. Step time also decreases obviously, the reaction speed increases obviously. Slip angle steady value of PID control decreases 12.52%, overshoot increases slightly. Step time increases obviously, the reaction speed decreases obviously.4.2. Fuzzy logic controlResult of double road train turns right on second class highway intersection simulation is shown in Fig. 12.Fuzzy logic control simulation of yaw rate and slip angle is shown in Fig. 12 and the comparison of response index is shown in table 3.Yaw rate steady value of fuzzy logic control decreases 14.50%, overshoot increases obviously. Response time decreases obviously, response speed increases obviously. Yaw angle velocity of vehicle steady value of fuzzy logic control decreases 14.75%, overshoot increases slightly. Response step time increases slightly, response speed increases obviously.4.3. Combined simulationResult of double road train turn around on second class highway intersection simulation is shown in Fig. 13 and the comparison of response index is shown in table 4.Yaw rate steady value decreases 17.04% in combined control, overshoot increases obviously. Response time decreases obviously, response speed increases obviously. Slip angle steady value of fuzzy logic control in combined control decreases 17.07%, overshoot decreases obviously. Response time increases slightly, response speed decreases slightly.4.4. Threshold controlThreshold control of PID control and fuzzy logic control simulation of yaw rate and slip angle is shown in Fig. 14 and the comparison of response index is shown in table 5.Yaw rate steady value of PID control in logic threshold decreases 19.08%, overshoot decreases slightly. Response time decreases obviously, response speed increases obviously.Slip angle steady value of fuzzy logic in logic threshold decreases 14.75%, overshoot increases slightly. Response time increases obviously, response speed decreases obviously.5. ConclusionConclusions c
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