Road Friction Estimation under Complicated Maneuver Conditions for Active Yaw Control

Road friction coefficient is a key factor for the stability control of the vehicle dynamics in the critical conditions. Obviously the vehicle dynamics stability control systems, including the anti-lock brake system（ABS）, the traction control system（TCS）, and the active yaw control（AYC） system, need the accurate tire and road friction information. However, the simplified method based on the linear tire and vehicle model could not obtain the accurate road friction coefficient for the complicated maneuver of the vehicle. Because the active braking control mode of AYC is different from that of ABS, the road friction coefficient cannot be estimated only with the dynamics states of the tire. With the related dynamics states measured by the sensors of AYC, a comprehensive strategy of the road friction estimation for the active yaw control is brought forward with the sensor fusion technique. Firstly, the variations of the dynamics characteristics of vehicle and tire, and the stability control mode in the steering process are considered, and then the proper road friction estimation methods are brought forward according to the vehicle maneuver process. In the steering maneuver without braking, the comprehensive road friction from the four wheels may be estimated based on the multi-sensor signal fusion method. The estimated values of the road friction reflect the road friction characteristic. When the active brake involved, the road friction coefficient of the braked wheel may be estimated based on the brake pressure and tire forces, the estimated values reflect the road friction between the braked wheel and the road. So the optimal control of the wheel slip rate may be obtained according to the road friction coefficient. The methods proposed in the paper are integrated into the real time controller of AYC, which is matched onto the test vehicle. The ground tests validate the accuracy of the proposed method under the complicated maneuver conditions.

Model Predictive Yaw Control Using Fuzzy-Deduced Weighting Factor for Large-Scale Wind Turbines

Yaw control system plays an important role in helping large-scale horizontal wind turbines capture the wind energy.To track the stochastic and fast-changing wind direction,the nacelle is rotated by the yaw control system.Therein,a difficulty consists in the variation speed of the wind direction much faster than the rotation speed of the nacelle.To deal with this difficulty,model predictive control has been recently proposed in the literature,in which the previewed wind direction is employed into the predictive model,and the estimated captured energy and yaw actuator usage are two contradictive objectives.Since the performance of the model predictive control strat-egy relies largely on the weighting factor that is designed to balance the two objectives,the weighting factor should be carefully selected.In this study,a fuzzy-deduced scheme is proposed to derive the weighting factor of the mod-el predictive yaw control.For the proposed fuzzy-deduced strategy,the variation degree and the increment of the wind direction during the predictive horizon are used as the inputs,and the weighting factor is the output,which is dynamically adjusted.The proposed model predictive yaw control is demonstrated by some simulations using real wind data and its performance is compared with the conventional model predictive control with thefixed weighting factor.Comparison results confirm the outweighing performance of the proposed control strategy over the conventional one.

A Genetic Algorithm for Optimizing Yaw Operation Control in Wind Power Plants

A genetic algorithm is proposed to optimize the yaw control system used for the stable and efficient operation of turbines in wind power plants.In particular,the factors that produce yaw static deviation are analyzed.Then,the sought optimization method for the yaw static deviation of the wind turbine is implemented by using a lidar wind meter in the engine room in order to solve the low accuracy problem caused by yaw static deviation.It is shown that fuzzy control can overcome problematic factors such as the randomness of wind direction and track the change of wind direction accurately.Power control implementation is simple,as only the voltage and current of the generator need to be measured.

Design of Yaw Controller for a Small Unmanned Helicopter Based on Improved ADRC

Yaw control is signi¯cant to the attitude control of small unmanned helicopters(SUHs).Since the existing robust control method cannot be applied to the SUH with unknown dynamics and disturbances,this paper proposes an improved active disturbance rejection control(IADRC)to solve the problem.The IADRC obtains the optimal solution of the actuator gain(b0)by gradient descent.Besides,this paper summarizes some experiences during the tuning process of ADRC,which signi¯cantly reduces the di±culty of designing ADRC.Finally,the experimental results show that the proposed method is better than the traditional PID in robust and tracking control performance.

An Adaptive Nonsingular Fast Terminal Sliding Mode Control for Yaw Stability Control of Bus Based on STI Tire Model

Due to the bus characteristics of large quality,high center of gravity and narrow wheelbase,the research of its yaw stability control(YSC)system has become the focus in the field of vehicle system dynamics.However,the tire nonlinear mechanical properties and the effectiveness of the YSC control system are not considered carefully in the current research.In this paper,a novel adaptive nonsingular fast terminal sliding mode(ANFTSM)control scheme for YSC is proposed to improve the bus curve driving stability and safety on slippery roads.Firstly,the STI(Systems Technologies Inc.)tire model,which can effectively reflect the nonlinear coupling relationship between the tire longitudinal force and lateral force,is established based on experimental data and firstly adopted in the bus YSC system design.On this basis,a more accurate bus lateral dynamics model is built and a novel YSC strategy based on ANFTSM,which has the merits of fast transient response,finite time convergence and high robustness against uncertainties and external disturbances,is designed.Thirdly,to solve the optimal allocation problem of the tire forces,whose objective is to achieve the desired direct yaw moment through the effective distribution of the brake force of each tire,the robust least-squares allocation method is adopted.To verify the feasibility,effectiveness and practicality of the proposed bus YSC approach,the TruckSim-Simulink co-simulation results are finally provided.The co-simulation results show that the lateral stability of bus under special driving conditions has been significantly improved.This research proposes a more effective design method for bus YSC system based on a more accurate tire model.

Direct Yaw Moment Control for Distributed Drive Electric Vehicle Handling Performance Improvement

For a distributed drive electric vehicle（DDEV） driven by four in-wheel motors, advanced vehicle dynamic control methods can be realized easily because motors can be controlled independently, quickly and precisely. And direct yaw-moment control（DYC） has been widely studied and applied to vehicle stability control. Good vehicle handling performance： quick yaw rate transient response, small overshoot, high steady yaw rate gain, etc, is required by drivers under normal conditions, which is less concerned, however. Based on the hierarchical control methodology, a novel control system using direct yaw moment control for improving handling performance of a distributed drive electric vehicle especially under normal driving conditions has been proposed. The upper-loop control system consists of two parts： a state feedback controller, which aims to realize the ideal transient response of yaw rate, with a vehicle sideslip angle observer; and a steering wheel angle feedforward controller designed to achieve a desired yaw rate steady gain. Under the restriction of the effect of poles and zeros in the closed-loop transfer function on the system response and the capacity of in-wheel motors, the integrated time and absolute error（ITAE） function is utilized as the cost function in the optimal control to calculate the ideal eigen frequency and damper coefficient of the system and obtain optimal feedback matrix and feedforward matrix. Simulations and experiments with a DDEV under multiple maneuvers are carried out and show the effectiveness of the proposed method： yaw rate rising time is reduced, steady yaw rate gain is increased, vehicle steering characteristic is close to neutral steer and drivers burdens are also reduced. The control system improves vehicle handling performance under normal conditions in both transient and steady response. State feedback control instead of model following control is introduced in the control system so that the sense of control intervention to drivers is relieved.

Research on Direct Yaw Moment Control Strategy of Distributed-Drive Electric Vehicle Based on Joint Observer

Combined with the characteristics of the distributed-drive electric vehicle and direct yaw moment control,a double-layer structure direct yaw moment controller is designed.The upper additional yaw moment controller is constructed based on model predictive control.Aiming at minimizing the utilization rate of tire adhesion and constrained by the working characteristics of motor system and brake system,a quadratic programming active set was designed to optimize the distribution of additional yaw moments.The road surface adhesion coefficient has a great impact on the reliability of direct yaw moment control,for which joint observer of vehicle state parameters and road surface parameters is designed by using unscented Kalman filter algorithm,which correlates vehicle state observer and road surface parameter observer to form closed-loop feedback correction.The results show that compared to the“feedforward+feedback”control,the vehicle’s error of yaw rate and sideslip angle by the model predictive control is smaller,which can improve the vehicle stability effectively.In addition,according to the results of the docking road simulation test,the joint observer of vehicle state and road surface parameters can improve the adaptability of the vehicle stability controller to the road conditions with variable adhesion coefficients.

基于主动尾流控制的风电机群协同优化调度

针对机组间尾流效应严重影响风电机组发电效率的问题,提出了风电机组安全偏航约束计算方法、尾流特性混合半机理建模方法以及风电机群多目标协同优化调度方法。基于FAST.FARM平台完善了多自由度可控机组与尾流的动态交互集成仿真环境,对比分析了2台机组串列式排布以及华东地区某海上风电场7台机组实际排布下的协同运行优化性能。结果表明:所建立的集成仿真模型能够合理表征风电机群与空气流场的多领域动态交互特性,所提方法能够有效提升风电机群发电效能,促进经济效益、资源利用和成本控制的均衡优化。

新能源汽车电机节能直接横摆力矩控制研究

为了降低电动汽车在行驶过程中的电机能量消耗,采用直接横摆力矩控制方法,并对车辆的车轮力矩和电机功率变化进行仿真验证。建立了车辆模型简图,根据牛顿第二定律推导出车辆转向运动方程式。针对传统两轮等力矩控制方法进行改进,提出了一种主动直接横摆力矩控制方法,推导出车辆连续行驶和转弯过程中的力矩分配律。利用转弯过程中车辆车轮的力矩变化来调节电机的功率变化,从而降低车辆电机的能量消耗。采用MATLAB软件对车辆横摆角速度、车轮力矩和电机能量消耗进行仿真,与两轮等力矩控制方法进行对比。结果显示:采用两轮等力矩控制方法,车辆横摆角速度跟踪误差较大,车轮力矩变化幅度较小,自适应调节能力较差,导致电机能量消耗较多;采用直接横摆力矩控制方法,车辆横摆角速度跟踪误差较小,车轮力矩变化幅度较大,自适应调节能力较好,使电机能量消耗较少。采用直接横摆力矩控制方法,能够自适应调整车轮力矩的变化,从而达到降低电机能量消耗目的。

电动汽车行驶稳定性模型预测控制仿真研究

为了提高电动汽车行驶的稳定性,提出了一种集成的汽车和车轮稳定性控制系统,并对汽车行驶的稳定性控制效果进行仿真。创建电动汽车模型简图,给出电动汽车动力学方程式。采用模型预测控制技术,设计了一种集成的汽车和车轮稳定性控制器。采用MATLAB软件对电动汽车横摆角速度、行驶速度和侧滑角进行仿真,与无模型预测控制器输出结果进行对比。结果显示:采用无模型预测控制器,电动汽车横摆角速度、行驶速度跟踪误差较大,振动幅度较大,产生的侧滑角较大。采用模型预测控制器,电动汽车横摆角速度、行驶速度跟踪误差较小,振动幅度较小,产生的侧滑角较小。采用模型预测控制器,能够提高电动汽车行驶的稳定性,避免汽车在突然转弯时发生侧翻现象。

基于风况预测误差自适应的海上风电场尾流偏转控制方法

风电场尾流偏转控制是降低尾流效应、提升整场发电量的重要手段。风况预测值是风电场尾流偏转控制的重要输入,其误差给实际控制效果带来巨大影响,甚至导致全场发电量“不增反降”,极大限制了风电场尾流偏转控制技术的工程应用。以某海上风电场实际运行数据为例,探索了分钟级风速和风向预测误差对风电场尾流偏转控制效果的影响,提出了风况预测误差自适应的海上风电场尾流偏转控制方法及基于深度神经网络的控制模型。研究结果表明:与不考虑风况预测误差自适应的传统风电场尾流偏转控制方法相比,所提方法的全场发电量提高了1.77%。

分布驱动无人车在极限工况下的稳定跟踪控制

针对分布式驱动无人车在低附着、高速的极限工况下行驶时偏离目标路径和失稳的现象,提出一种稳定-跟踪联合控制策略,策略包含路径跟踪控制和横向稳定控制,使无人车在提高跟踪精度的同时又使其稳定性得到保障。路径跟踪模块在汽车二自由度模型的基础上,建立了最优LQR路径跟踪控制器,并引入前馈用以改善稳态误差,其次考虑误差变化的规律和路-车位置关系以及驾驶员预瞄模型,设计了改进的权重参数自适应LQR控制器;稳定性模块基于可以减小系统抖振的滑模模糊控制原理,并结合汽车稳定性参数的实际值与期望值的差值,求解出横摆力矩。其次在分配各轮转矩时建立约束条件,使得轮胎负荷率最小用以提升汽车的操纵稳定性。CarSim/Simulink仿真表明在高速、低附着的换道工况下,改进后的LQR控制器有效地改善跟踪误差,具有良好的鲁棒性。且在加入稳定性控制器后,横摆角速度与质心侧偏角的调节时间变短,极大改善了无人车在极限工况下的稳定性。

分布驱动式纯电动汽车电子差速控制策略研究

以轮毂电机直接驱动的分布驱动式电动车为研究对象,为解决汽车转向行驶稳定性和主动安全性的问题,对其电子差速控制策略进行研究。将汽车转向时的横摆稳定性和车轮滑动率相结合,基于动力学计算转矩输出,使用模糊控制算法确定变量范围并设计出模糊控制器,采用双移线和方向盘角阶跃两种输入工况,通过CarSim搭建整车仿真联合模型,对两种电子差速控制策略的对比分析及仿真验证。结果表明,采用DYC+S控制策略比采用DYC+T控制策略的性能更加优越,提高了整车的驾驶性能。

基于功能链的北斗三号卫星控制技术

为适应卫星研制技术和规模空前发展,在卫星高可靠的同时做到低成本,将传统的分系统设计方法按特点和学科进行了重新梳理,提出国内首创的功能链设计理念和方法,并成功应用于北斗三号首发试验星、组网星及备份星中,实现了北斗三号卫星的快速批产组网.阐述基于功能链的设计理念,重点介绍基于功能链的北斗三号卫星姿态及轨道等控制技术,在轨运行结果表明,卫星实现了连续可靠的工作,全面适应ka及激光星间链路的高控制精度要求以及导航卫星对高连续性的要求.

分布式驱动车辆直接横摆力矩与转向控制

为了进一步提高车辆在转向行驶过程中的横向稳定性,针对分布式驱动电动汽车,提出了一种在分层集成架构下利用相平面法确定稳定区域控制车辆运动状态稳定的控制策略。由二自由度模型得到的理想侧向速度、理想横摆角速度与实际差值作为上层控制器输入值,上层控制器采用双滑膜控制器结构分别对理想侧向速度和理想横摆角速度进行追踪,分别得到控制量附加转角和附加横摆力矩;下层控制器主要依据相平面法得到的边界值协调控制转向控制和横摆力矩控制的输入输出值,通过最优分配算法将轮胎力和转向角分配到四轮,最终实现对车辆稳定性控制。对控制算法进行仿真验证,证明了所提控制策略的有效性和可行性。

轮毂电机驱动电动汽车4WS和DYC协调控制

为了提高轮毂电机驱动电动汽车的路径跟踪能力和操纵稳定性,本文针对主动四轮转向系统(4WS)和直接横摆力矩控制系统(DYC)提出一种新型的协调控制策略。首先,综合考虑车辆的路径跟踪性能和操纵稳定性,建立一种共享转向控制模型,并在此基础上提出基于非合作Nash博弈的4WS控制策略。其次,为了提高危险行驶工况下的车辆侧向稳定性,基于质心侧偏角相平面将车辆状态划分为稳定区域、过渡区域和失稳区域,并分区域建立DYC控制器。再次,为了实现后轮转向与直接横摆力矩的协同控制,建立基于模糊神经网络的ARS/DYC协调控制器。最后,利用CarSim/Simulink联合仿真平台和硬件在环平台,分别进行双移线工况下的试验验证。研究结果表明,所提出的控制策略能够有效地提高车辆在极端行驶工况下的路径跟踪精度和操纵稳定性能。

上游风力机偏航对于下游风力机气动特性的影响

针对倾斜尾迹导致下游风力机受到不平衡的气动载荷,疲劳载荷显著增加的问题,文章基于FAST.Farm开源软件,研究了在NREL 5 MW风力机错列布局下,上游风力机偏航对于下游风力机气动特性的影响。研究结果表明:随着上游风力机偏航角度的增加,下游风力机逐渐处于尾流风速亏损的核心区域,其风轮输出功率的减少量显著增大,风轮所承受的推力逐渐降低;相较于上游风力机未偏航的情况,当上游风力机偏航角度为40°时,下游风力机叶根处倾覆力矩的标准差增加了50.5%,波动性显著增大。该研究结果可为整场控制协同优化和偏航入流下的风力机气动特性分析提供参考。

基于场级偏航控制的风电场尾流敏感性分析

偏航偏转控制有利于减小风机尾流效应,通过场级偏航协调优化减小尾流损失,可使风电场总发电量达到最大化。采用FLORIS尾流代理模型,以各风机偏航角为优化对象,风电场总功率最大为优化目标,进行场级偏航寻优。针对不同风机间距、纵列个数、湍流强度、来流风速和来流风向等多个维度,对比分析了偏航优化对尾流损失及功率提升的敏感性。结果表明:当风电场排布间距小于5D、风机纵列大于3台且仅需优化前5排、纵列机位连线与风玫瑰图主频风向夹角小于15°、风场湍流小于0.1、来流风速位于风机“切入风速+2 m/s”至“额定风速+2 m/s”区间时,场级偏航控制对于尾流优化效果最佳;若仅采用单机偏航控制风向,前排风机保留3°~5°偏航误差有利于风电场整体的发电收益。

机械弹性电动轮驱动车辆横摆稳定性协调控制

针对匹配机械弹性电动轮(MEEW)车辆的横摆稳定性控制问题,提出一种基于主动前轮转向(AFS)与直接横摆力矩控制(DYC)的稳定性协调控制策略。为修正车辆行驶过程中的前轮转角输入,设计了基于微分平坦与RBF神经网络的AFS控制器,从而提高车辆的转向能力。针对AFS控制器在极限工况下易失效的缺陷,引入基于线性二次型调节器(LQR)的直接横摆力矩控制算法,并依照轴荷比分配四轮力矩。最后,依据机械弹性电动轮的质心侧偏角-质心侧偏角速度相平面图划分稳定域,实现AFS与DYC的协调控制。通过Matlab/Simulink和Carsim进行联合仿真,结果表明:所提出的AFS控制算法在高速高附着工况下有良好的稳定控制性能,但在高速、低附着极限工况下控制效果受到影响。而AFS/DYC协调控制策略效果较好,跟踪精度优于单一控制器,质心侧偏角和横摆角速度的最大跟踪误差仅为3.03°和1.82(°)/s,可保证汽车在极限工况下转向时的横摆稳定性。