Sliding mode observation and control for semiactive vehicle suspensions

This paper investigates the application of robust, nonlinear observation and control strategies, namely sliding mode observation and control (SMOC), to semiactive vehicle suspensions using a model reference approach. The vehicle suspension models include realistic nonlinearities in the spring and magnetorheological (MR) damper elements, and the nonlinear reference models incorporate skyhook damping. Since full state measurement is difficult to achieve in practice, a sliding mode observer (SMO) that requires only suspension deflection as a measured input is developed. The performance and robustness of sliding mode control (SMC), SMO, and SMOC are demonstrated through comprehensive computer simulations and compared to popular alternatives. The results of these simulations reveal the benefits of sliding mode observation and control for improved ride quality, and should be directly transferable to commercial semiactive vehicle suspension implementations.     

A reduced-order nonlinear sliding mode observer for vehicle slip angle and tyre forces

In this paper, a reduced-order sliding mode observer (RO-SMO) is developed for vehicle state estimation. Several improvements are achieved in this paper. First, the reference model accuracy is improved by considering vehicle load transfers and using a precise nonlinear tyre model ‘UniTire’. Second, without the reference model accuracy degraded, the computing burden of the state observer is decreased by a reduced-order approach. Third, nonlinear system damping is integrated into the SMO to speed convergence and reduce chattering. The proposed RO-SMO is evaluated through simulation and experiments based on an in-wheel motor electric vehicle. The results show that the proposed observer accurately predicts the vehicle states.     

Fuzzy adaptive sliding mode controller for an air spring active suspension

A fuzzy adaptive sliding mode controller for an air spring active suspension system is developed. Due to nonlinearity, preload-dependent spring force and parameter uncertainty in the air spring, it is difficult to control the suspension system. To achieve the desired performance, a fuzzy adaptive sliding mode controller (FASMC) is designed to improve the passenger comfort and the manipulability of the vehicle. The fuzzy adaptive system handles the nonlinearity and uncertainty of the air suspension. A normal linear suspension model with an optimal state feedback control is designed as the reference model. The simulation results show that this control scheme more effectively and robustly isolates vibrations of the vehicle body than the conventional sliding mode controller (CSMC).     

State observer-based sliding mode control for semi-active hydro-pneumatic suspension

This paper proposes an improved virtual reference model for semi-active suspension to coordinate the vehicle ride comfort and handling stability. The reference model combines the virtues of sky-hook with ground-hook control logic, and the hybrid coefficient is tuned according to the longitudinal and lateral acceleration so as to improve the vehicle stability especially in high-speed condition. Suspension state observer based on unscented Kalman filter is designed. A sliding mode controller (SMC) is developed to track the states of the reference model. The stability of the SMC strategy is proven by means of Lyapunov function taking into account the nonlinear damper characteristics and sprung mass variation of the vehicle. Finally, the performance of the controller is demonstrated under three typical working conditions: the random road excitation, speed bump road and sharp acceleration and braking. The simulation results indicated that, compared with the traditional passive suspension, the proposed control algorithm can offer a better coordination between vehicle ride comfort and handling stability. This approach provides a viable alternative to costlier active suspension control systems for commercial vehicles.     

Semiactive Suspension Design with an Optimal Gain Switching Target

The paper presents a two-phase design technique for semiactive suspensions. In the first phase, we use a procedure proposed by Yoshida et al. to compute a target active control law that can be implemented by Optimal Gain Switching. This control law is such that the force generated by the suspension system is bounded within a set U . In the second phase, we approximate this target by controlling the damper coefficient of the semiactive suspension. We also compute the region of the state space in which the force generated by the semiactive suspension is still within the set U . The results of several simulations show that the use of a semiactive suspension leads to minimal loss with respect to optimal performance of an active suspension.     

Semiactive Suspension Design with an Optimal Gain Switching Target

The paper presents a two-phase design technique for semiactive suspensions. In the first phase, we use a procedure proposed by Yoshida et al. to compute a target active control law that can be implemented by Optimal Gain Switching. This control law is such that the force generated by the suspension system is bounded within a set U. In the second phase, we approximate this target by controlling the damper coefficient of the semiactive suspension. We also compute the region of the state space in which the force generated by the semiactive suspension is still within the set U. The results of several simulations show that the use of a semiactive suspension leads to minimal loss with respect to optimal performance of an active suspension.     

基于扰动观测的客车ESC自适应神经网络滑模控制

为了提高客车电子稳定性控制系统(ESC)的控制精度,针对实际车辆系统建模中存在各种非线性扰动项以及传统滑模控制(Sliding Mode Control,SMC)中抖振较大的问题,提出一种自适应神经网络滑模控制算法.基于2自由度车辆模型,首先设计一个二阶滑模(Second-order Sliding Mode,SOSM...     

基于参考模型的半主动悬架滑模控制

在分析电流变阻尼器工作原理与结构的基础上,基于参考模型设计了1/4车辆悬架系统的滑模控制器.研究了系统在随机路面激励条件下车身加速度、悬架动行程和轮胎动位移等性能指标的控制效果.运用Simu-link在不同的车速和车身质量的情况下进行了仿真分析,结果表明:控制后悬架各性能指标均得到明显改善,滑模控制器性能稳定,对系统参数的改变具有很好的鲁棒性.     

A driver-adaptive stability control strategy for sport utility vehicles

Conventional vehicle stability control (VSC) systems are designed for average drivers. For a driver with a good driving skill, the VSC systems may be redundant; for a driver with a poor driving skill, the VSC intervention may be inadequate. To increase safety of sport utility vehicles (SUVs), this paper proposes a novel driver-adaptive VSC (DAVSC) strategy based on scaling the target yaw rate commanded by the driver. The DAVSC system is adaptive to drivers’ driving skills. More control effort would be exerted for drivers with poor driving skills, and vice versa. A sliding mode control (SMC)-based differential braking (DB) controller is designed using a three degrees of freedom (DOF) yaw-plane model. An eight DOF nonlinear yaw-roll model is used to simulate the SUV dynamics. Two driver models, namely longitudinal and lateral, are used to ‘drive’ the virtual SUV. By integrating the virtual SUV, the DB controller, and the driver models, the performance of the DAVSC system is investigated. The simulations demonstrate the effectiveness of the DAVSC strategy.     

Handling Capabilities of Vehicles in Emergencies Using Coordinated AFS and ARMC Systems

In this paper, an advanced control technique that can be implemented in hard emergency situations of vehicles is introduced. This technique suggests integration between Active Front Steering (AFS) and Active Roll Moment Control (ARMC) systems in order to enhance the vehicle controllability. For this purpose, the AFS system applies a robust sliding mode controller (SMC) that is designed to influence the steering input of the driver by adding a correction steering angle for maintaining the vehicle yaw rate under control all the time. The AFS system is then called active-correction steering control. The ARMC system is designed to differentiate the front and rear axles' vertical suspension forces in order to alter the vehicle yaw rate and to eliminate the vehicle roll motion as well. Moreover, the operation of the SMC is based on tracking the behavior of a nonlinear 2-wheel model of 2-DOF used as a reference model. The 2-wheel model incorporates real tire characteristics, which can be inferred by the use of trained neural networks. The results clearly demonstrate the enhanced characteristics of the proposed control technique. The SMC with the assistance of the ARMC provides less correction of the steering angle and accordingly reduces the possibility of occurrence of the saturation phenomenon that is likely to take place in the operation of the SMC systems.     

Handling Capabilities of Vehicles in Emergencies Using Coordinated AFS and ARMC Systems

In this paper, an advanced control technique that can be implemented in hard emergency situations of vehicles is introduced. This technique suggests integration between Active Front Steering (AFS) and Active Roll Moment Control (ARMC) systems in order to enhance the vehicle controllability. For this purpose, the AFS system applies a robust sliding mode controller (SMC) that is designed to influence the steering input of the driver by adding a correction steering angle for maintaining the vehicle yaw rate under control all the time. The AFS system is then called active-correction steering control. The ARMC system is designed to differentiate the front and rear axles' vertical suspension forces in order to alter the vehicle yaw rate and to eliminate the vehicle roll motion as well. Moreover, the operation of the SMC is based on tracking the behavior of a nonlinear 2-wheel model of 2-DOF used as a reference model. The 2-wheel model incorporates real tire characteristics, which can be inferred by the use of trained neural networks. The results clearly demonstrate the enhanced characteristics of the proposed control technique. The SMC with the assistance of the ARMC provides less correction of the steering angle and accordingly reduces the possibility of occurrence of the saturation phenomenon that is likely to take place in the operation of the SMC systems.     

EPS与主动悬架系统自适应模糊集成控制的仿真与试验研究

在建立的汽车整车主动悬架和EPS动力学模型(包含转向运动、俯仰运动和侧倾运动等模型)的基础上,运用自适应模糊控制方法,利用车身姿态的变化动态地调节主动悬架控制器和EPS控制器的输出,实现了对EPS和主动悬架系统的集成控制。为了验证控制系统的可行性和有效性,分别进行了仿真和实车道路试验。结果表明,集成控制显著提高了汽车的行驶平顺性和操纵稳定性,整车综合性能明显优于传统的悬架和转向系统。     

基于滑模自抗扰的电制动系统动态负载模拟

为精准模拟传动系弹性及齿隙作用下电制动系统非线性机械负载,提出了自适应模糊滑模自抗扰的测功机控制算法。首先,针对一款前驱电动汽车,建立融合感应电机模型的车辆及台架机电一体化模型,引入典型正常制动和防抱死制动控制作为测试对象。其次,构建扩张状态观测器估计台架系统未建模动态,以自适应模糊滑模控制测功机实时模拟高度非线性机械负载。最后,开展了制动控制策略台架测试的仿真研究。结果表明:提出的方法可精确模拟电制动系统动态负载,有效提高制动控制算法台架测试精度。     

汽车磁流变半主动悬架的控制研究

为了改善汽车的乘坐舒适性和行驶安全性,提出了一种汽车磁流变半主动悬架的控制策略。首先,设计了磁流变减振器的工作模式,通过试验获得了其速度特性和力学特性,建立了磁流变减振器的数学模型;其次,建立了带磁流变减振器的二自由度车辆简化模型及其参数表;最后,基于双环控制理论,设计了一种控制系统,其外环产生理想的结构阻尼力,内环调节电流驱动器的电流,以使磁流变减振器实时地产生控制阻尼力。仿真结果表明:以磁流变减振器为基础,通过半主动控制技术,悬架系统的振动动态性能得到了有效的控制。     

Force control for path following of a 4WS4WD vehicle by the integration of PSO and SMC

The aim of this paper is to present a novel control method for a four-wheel steer and four-wheel drive (4WS4WD) vehicle. The novelty is in the integration of sliding mode control (SMC) and particle swarm optimization (PSO) that is proposed to solve the control problem caused by the nonlinear, highly coupled and over-actuated characteristics of the four-wheel steer and four-wheel drive (4WS4WD) vehicle. The validity of the control method is evaluated by two criterions, namely path following performance assessed by the vehicle's position errors with respect to the reference path, and motion quality reflected by the smoothness of vehicle's velocities and accelerations. In vehicle modelling, a kinematic model and a dynamic model considering all slip forces are proposed for the controller design. Simulation results are provided to demonstrate the applicability of the proposed methodology and its robustness.     

无人驾驶机器人车辆非线性模糊滑模车速控制

为了实现不同行驶工况下车速的精确、稳定控制,提出一种基于非线性干扰观测器的无人驾驶机器人车辆模糊滑模车速控制方法。考虑模型不确定性和外部干扰对车速控制的影响,建立车辆纵向动力学模型。通过分析无人驾驶机器人油门机械腿、制动机械腿的结构、机械腿操纵自动挡车辆踏板的运动,建立油门机械腿和制动机械腿的运动学模型。在此基础上,分别设计油门/制动切换控制器、油门模糊滑模控制器以及制动模糊滑模控制器,并进行控制系统的稳定性分析。油门/制动切换控制器以目标车速的导数为输入来进行油门与制动之间的切换控制。油门模糊滑模控制器和制动模糊滑模控制器以当前车速以及车速误差为输入,分别以油门机械腿直线电机位移和制动机械腿直线电机位移为输出来实现对油门与制动的控制。模糊滑模控制器中,为了减少控制抖振,滑模控制的反馈增益系数由模糊逻辑进行在线调节。模糊滑模控制器中的非线性干扰观测器用于估计和补偿无人驾驶机器人车辆的模型不确定性与外部干扰。仿真及试验结果对比分析表明：本文方法能够精确地估计和补偿无人驾驶机器人车辆的模型不确定性和外部干扰,避免了油门控制与制动控制之间的频繁切换,并实现了精确稳定的车速控制。     

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.     

可调减振器阻尼控制与半主动悬架的试验研究

建立车辆半主动悬架1/4模型,提出可调减振器阻尼控制的实现方法,设计半主动悬架台架试验系统。在可调减振器试验的基础上,建立了可调减振器阻尼与步进电机转角之间的关系。最后,对半主动悬架1/4物理模型进行了台架试验。结果表明,试验系统稳定可靠,阻尼控制易于实现,半主动悬架能较好地适应不同的路面输入,为半主动悬架及控制系统的进一步研究奠定了基础。     

基于带切换增益模糊调节的滑模控制算法的车辆电液制动系统

针对汽车线控电液制动系统建立了单轮车辆模型,研制了一种新的状态观测器对车速进行估算,试验结果表明该方法正确实用.采用切换增益模糊调节的滑模控制算法对非线性时变的车辆实施基于最佳滑移率的制动控制,在Matlab/Simulink中的仿真结果和验证试验都表明在汽车线控制动系统应用该算法是可行、有效的,在该算法的控制下汽车可获得比一般滑模控制更好的制动性能.     

基于滑模控制的时滞自动车辆跟随系统数学模型

基于非线性车辆动力学方程和固定车辆间距跟随策略,对具有时间滞后的自动化公路系统车辆纵向跟随控制问题进行了研究。在假定车队中的每个被控制车辆能够接收到车队领头车辆以及该车前面一个车辆的位移、速度和加速度信息的情况下,应用滑模变结构控制方法,通过对滑模运动方程的分析,得到了关于车辆间距误差的车辆纵向跟随系统的数学模型。该模型属于一类具有时间滞后的无限维非线性关联大系统。在具有时间滞后的车辆纵向跟随控制器设计中,利用该类非线性关联大系统的稳定性判定条件来设计控制参数,可确保车辆纵向跟随控制系统的稳定性。