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。     

应用于车辆纵向控制的无模型自适应滑模预测控制方法

鉴于汽车纵向动力学系统为典型的参数时变不定、多扰动的非线性离散系统,基于精确数学模型的控制算法较难取得理想效果,本文中采用无需模型、基于输入/输出数据的控制算法.首先,基于紧格式动态线性化数据模型,将无模型自适应控制(MFAC)算法、滑模控制(SMC)算法和模型预测控制(MPC)算法相结合,设计了无模型自适应控制器.接...     

基于非线性扰动观测的商用车ESC自适应滑模控制研究

针对商用车ESC控制中,实际车辆存在各种扰动,难以建立精确的车辆模型,传统滑模控制存在较大抖振等问题,本文中提出基于非线性扰动观测(NDOB)的自适应滑模控制(ADSMC)算法。首先,利用非线性扰动观测器对车辆建模的扰动项进行估计;然后,采用径向基神经网络对滑模控制器的关键参数进行自适应调节,以简化参数调节过程、减小滑模抖振、提高控制精度;最后,在TruckSim中建立车辆模型,在MATLAB中建立控制策略模型,在电控气压硬件在环试验台上,对控制算法进行试验验证。试验结果表明,NDOB-ADSMC算法的ESC控制效果良好,能够满足车辆ESC控制需求。     

汽车半主动悬架的模型参考自适应控制

在1／4车辆动力学模型的基础上，基于李雅普诺夫稳定性理论，以天棚阻尼半主动悬架为参考模型，设计了半主动悬架模型参考自适应控制器。自适应控制器包括可调前置控制器和状态反馈控制器两个部分。推导了自适应控制律与相应的约束条件。仿真结果表明：该控制器对于模型参数的不确定性具有良好的鲁棒特性。自适应控制器不仅明显降低了车身加速度，提高了平顺性，同时也使汽车的行驶安全性获得了改善，悬架动变形稍有增大。     

混合动力传动系 Tip-in/out工况的模型预测控制

为抑制混合动力汽车加减速过程中传动系统振荡,以电机转矩为控制量,提出一种基于模型预测主动控制混合动力传动系统振荡的策略,基于 Matlab/Simulink平台搭建动态系统模型,实时计算电机转矩补偿优化发动机输出转矩,准确跟踪目标转矩的同时减少传动系统振荡。探索不同控制器参数设置对于驾驶动力性和舒适性的增益效果,通过硬件在环 （Hardware-in-Loop,HIL） 试验表明,所设计的 MPC控制器能使汽车平稳地加减速,迅速跟踪目标转速,求解时间控制在10 ms以内,具有较好的实时性,同时对传动系统中的非线性因素和参数变化有较好的鲁棒性。     

汽车ESP模糊自适应控制

基于模糊控制理论设计了一种模糊自适应ESP控制器,该控制器可根据反馈的车辆状态信息自动适应车速和路面附着系数的变化.采用Matlab/Smulink建立了14自由度车辆模型和控制器模型,考虑了轮胎的非线性特性对汽车转向特性与行驶稳定性的影响.仿真结果表明,该控制器能够提高汽车在高速或光滑路面上的操纵稳定性,有较强的适应性和鲁棒性.     

基于自适应控制系统的智能车横向轨迹跟踪控制

智能车辆轨迹跟踪控制的研究中,由于不同工况下控制算法的适应性不强,存在着较大的跟踪误差。为提高智能车辆行驶的自适性、跟踪精度与鲁棒性,提出了一种基于模型参考自适应控制系统（MRAC）的自适应PID控制方法。利用车辆模型与参考模型输出误差自适应PID控制器的3个参数,从而得到一种基于MRAC的自适应PID控制方法。MATLAB仿真对比结果表明,2种工况基于MRAC的自适应PID最大侧向跟踪误差分别为0.036 m和0.076 m,相比传统PID控制模型,该控制模型的控制精度分别提升52.10%、11.76%,表现出更好的横向轨迹跟踪性能。     

基于多维耦合仿真的冷却系统控制策略优化

电动车驱动系统冷却系统具有非线性、传热迟滞和时变性等特点，电机控制器功耗受工作温度影响较大，传统的冷却辅机控制策略无法满足最低功耗需求。针对此问题，利用STAR-CCM+和Amesim仿真软件建立冷却系统多维耦合仿真模型，研究电机控制器冷却系统最优门限值，并联合Matlab/Simulink仿真平台对比多挡、PID和自适应模糊PID3种控制方法的系统功耗。结果表明自适应模糊PID控制能够提升冷却系统的动态性能，降低整车能耗。     

汽车主动悬架系统的自适应模糊控制方法

本文提出了一种汽车主动悬架系统的自适应模糊控制方法，该模糊控制方法可以有线自适应调整模糊控制的有关参数。1/4车辆模型作为系统仿真对象，模糊逻辑控制器可以显著发减小车辆的振动及干扰，提高车辆受路面激励时车辆的舒适性。仿真结果清楚地表明该模糊控制方法的有效性。另外，当主动悬架系统模型参数发生变化时该模糊控制器表现出良好的鲁棒性。     

基于DSP的汽车无级变速器电控系统的研究

在分析了金属带式无级变速器的变速原理后，设计了基于DSP的无级变速器的电控系统的硬件和软件。考虑到CVT传动系统的复杂化，设计了模糊自适应PID控制器，开发了基于速比反馈的速比控制系统。试验结果表明，所设计的控制系统是可行和实用的。     

Adaptive Throttle Control for Speed Tracking

Electronic throttle control is an important part of every advanced vehicle control system. In this paper we design an adaptive control scheme for electronic throttle that achieves good tracking of arbitrary constant speed commands in the presence of unknown disturbances. The design is based on a simplified linear vehicle model which is derived from a validated nonlinear one. The designed control scheme is simulated using the validated full order nonlinear vehicle model and tested on an actual vehicle. The simulation and vehicle test results are included in this paper to show the performance of the controller. Due to the learning capability of the adaptive control scheme, changes in the vehicle dynamics do not affect the performance of the controller in any significant manner.     

Adaptive Throttle Control for Speed Tracking

SUMMARY

Electronic throttle control is an important part of every advanced vehicle control system. In this paper we design an adaptive control scheme for electronic throttle that achieves good tracking of arbitrary constant speed commands in the presence of unknown disturbances. The design is based on a simplified linear vehicle model which is derived from a validated nonlinear one. The designed control scheme is simulated using the validated full order nonlinear vehicle model and tested on an actual vehicle. The simulation and vehicle test results are included in this paper to show the performance of the controller. Due to the learning capability of the adaptive control scheme, changes in the vehicle dynamics do not affect the performance of the controller in any significant manner.     

Second-order sliding mode controller with model reference adaptation for automatic train operation

In this paper, a new approach to model reference based adaptive second-order sliding mode control together with adaptive state feedback is presented to control the longitudinal dynamic motion of a high speed train for automatic train operation with the objective of minimal jerk travel by the passengers. The nonlinear dynamic model for the longitudinal motion of the train comprises of a locomotive and coach subsystems is constructed using multiple point-mass model by considering the forces acting on the vehicle. An adaptation scheme using Lyapunov criterion is derived to tune the controller gains by considering a linear, stable reference model that ensures the stability of the system in closed loop. The effectiveness of the controller tracking performance is tested under uncertain passenger load, coupler-draft gear parameters, propulsion resistance coefficients variations and environmental disturbances due to side wind and wet rail conditions. The results demonstrate improved tracking performance of the proposed control scheme with a least jerk under maximum parameter uncertainties when compared to constant gain second-order sliding mode control.     

基于反馈线性化的汽车AMT离合器滑模控制

电控离合器系统中，因为外界环境相当复杂，控制系统存在多种非线性，使得数学模型中参数是摄动的并且同时受到干扰，给机械式自动变速系统（AMT）的控制带来困难，针对离合器液压扫行机构，建立系统非线性动力学模型，进一步应用基于微分几何的反馈线性化方法，将原非线性系统等价为完全可控型线性化模型，然后设计滑模控制器，实车实验结果表明，设计的非线性控制器跟踪精度高，鲁棒性好。     

汽车主动悬架的单神经元自适应控制

在1/4汽车动力学模型的基础上,设计了汽车主动悬架的自适应神经元控制器。以车辆的行驶平顺性为主要控制目标,车身垂直加速度、悬架动挠度、车轮动位移为具体评价参数,研究了系统在随机路面激励条件下的时域响应,计算了振动响应的均方根值,考察了在变参数条件下控制器的鲁棒性。仿真结果表明,该控制器能有效改善车辆的综合性能,尤其是平顺性和舒适性,并且具有较好的鲁棒性,对模型参数的变化有一定的适应性。     

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.     

Inverse neuro-fuzzy MR damper model and its application in vibration control of vehicle suspension system

In this paper, a magneto-rheological (MR) damper-based semi-active controller for vehicle suspension is developed. This system consists of a linear quadratic Gauss (LQG) controller as the system controller and an adaptive neuro-fuzzy inference system (ANFIS) inverse model as the damper controller. First, a modified Bouc–Wen model is proposed to characterise the forward dynamic characteristics of the MR damper based on the experimental data. Then, an inverse MR damper model is built using ANFIS technique to determine the input current so as to gain the desired damping force. Finally, a quarter-car suspension model together with the MR damper is set up, and a semi-active controller composed of the LQG controller and the ANFIS inverse model is designed. Simulation results demonstrate that the desired force can be accurately tracked using the ANFIS technique and the semi-active controller can achieve competitive performance as that of active suspension.     

Model predictive control with constraints for a nonlinear adaptive cruise control vehicle model in transition manoeuvres

A vehicle following control law, based on the model predictive control method, to perform transition manoeuvres (TMs) for a nonlinear adaptive cruise control (ACC) vehicle is presented in this paper. The TM controller ultimately establishes a steady-state following distance behind a preceding vehicle to avoid collision, keeping account of acceleration limits, safe distance, and state constraints. The vehicle dynamics model is for continuous-time domain and captures the real dynamics of the sub-vehicle models for steady-state and transient operations. The ACC vehicle can execute the TM successfully and achieves a steady-state in the presence of complex dynamics within the constraint boundaries.     

A vehicle stability control strategy with adaptive neural network sliding mode theory based on system uncertainty approximation

Modelling uncertainty, parameter variation and unknown external disturbance are the major concerns in the development of an advanced controller for vehicle stability at the limits of handling. Sliding mode control (SMC) method has proved to be robust against parameter variation and unknown external disturbance with satisfactory tracking performance. But modelling uncertainty, such as errors caused in model simplification, is inevitable in model-based controller design, resulting in lowered control quality. The adaptive radial basis function network (ARBFN) can effectively improve the control performance against large system uncertainty by learning to approximate arbitrary nonlinear functions and ensure the global asymptotic stability of the closed-loop system. In this paper, a novel vehicle dynamics stability control strategy is proposed using the adaptive radial basis function network sliding mode control (ARBFN-SMC) to learn system uncertainty and eliminate its adverse effects. This strategy adopts a hierarchical control structure which consists of reference model layer, yaw moment control layer, braking torque allocation layer and executive layer. Co-simulation using MATLAB/Simulink and AMESim is conducted on a verified 15-DOF nonlinear vehicle system model with the integrated-electro-hydraulic brake system (I-EHB) actuator in a Sine With Dwell manoeuvre. The simulation results show that ARBFN-SMC scheme exhibits superior stability and tracking performance in different running conditions compared with SMC scheme.     

基于驾驶员行为模拟的ACC控制算法

基于驾驶员最优预瞄加速度模型建立了一种适用于多种典型行驶工况的ACC控制算法。该算法采用基于多目标模糊决策方法的驾驶安全性、工效性、轻便性与合法性评价指标以及基于预瞄跟随理论的微分校正函数，描述了ACC控制系统对自由工况、跟随工况和切入工况等不同行驶条件及汽车动力学系统强非线性特性的考虑。