Optimal Preview Control of Rear Suspension Using Nonlinear Neural Networks

The performance of neural networks to be used for identification and optimal control of nonlinear vehicle suspensions is analyzed. It is shown that neuro-vehicle models can be efficiently trained to identify the dynamical characteristics of actual vehicle suspensions. After trained, this neuro-vehicle is used to train both front and rear suspension neuro-controllers under a nonlinear rear preview control scheme. To do that, a neuro-observer is trained to identify the inverse dynamics of the front suspension so that front road disturbances can be identified and used to improve the response of the rear suspension. The performance of the vehicle with neuro-control and with LQ control are compared.     

Optimal Linear Preview Control of Active Vehicle Suspension

The problem of linear preview control of vehicle suspension is considered as a continuous time stochastic optimal control problem. In the proposed approach minimal a priori information about the road irregularities is assumed and measurement errors are taken into account. It is shown that estimation and control issues can be decoupled. The problem formulation and the analytical solution are given in a general form and hence they apply to other problems in which the system disturbances are unknown a priori, even in a stochastic sense, but some preview information is possible.

The solution is applied to a two-degree-of-freedom (2-DOF) vehicle model. The effects of preview information on ride comfort, road holding, working space of the suspension and power requirements are examined in time and frequency domains. The results show that the greatest potential is for improving road holding properties. This effect could not have been observed in previous studies based on a 1-DOF vehicle model. It is also demonstrated that the presence of preview drastically reduces power requirements, thus relieving the performance versus actuator power dilemma.     

Constrained Optimal Control of Semi-Active Suspension Systems with Preview

Controllers for semi-active suspensions have to account for constraints on damper range, tire force and suspension travel. Two approaches to incorporate these constraints in the design of controllers to minimize peak values in the chassis acceleration are considered. It is assumed that information on the oncoming road elevations (preview) is available. In the soft constraint approach, the constraints on tire force and suspension travel are included in a quadratic performance index. Two clipped optimal control laws, which deal with preview in a different way, are presented. Simulation results with a 2-DOF vehicle model on some rounded pulses show that these laws do not work satisfactorily with respect to the constraints. Therefore, the control problem is reformulated as a constrained optimization problem with hard constraints on tire force and suspension travel. Simulations with the same model on the same rounded pulses show that the hard constraint approach handles the constraints more properly.     

基于轴距预瞄的半主动悬架模糊神经网络控制

应用模糊控制和神经网络控制理论,构建了1/2车辆的半主动悬架模型,设计了基于轴距预瞄的半主动悬架模糊神经网络控制系统.对前轮半主动悬架采用以对应处车身垂向加速度为目标的模糊控制,对后轮半主动悬架采用轴距预瞄模糊控制,并利用神经网络来调整模糊控制器的控制规则和隶属度函数.在不同车速下对所建的控制系统分别进行了白噪声和路面脉冲输入的仿真.结果表明,与传统的被动系统相比,轴距预瞄模糊神经网络控制的半主动悬架系统能有效降低车辆振动;与模糊控制的半主动悬架系统相比,质心垂向加速度和后轮对应处车身加速度均有显著减小,较好地改善了车辆的行驶平顺性.     

Nonlinear Design Approach to Four-Wheel-Steering Systems Using Neural Networks

Four-wheel-steering (4WS) systems have been studied and developed with remarkable success from the viewpoint of vehicle dynamics. Most of the control methods require a linearized bicycle model of the actual vehicle system which is however strongly influenced by tire nonlinearity. This paper proposes a new method to design the 4WS system taking into account the nonlinear characteristics of tires and suspensions. For this purpose integration of artificial neural network and linear control theory is introduced for the identification and control of a nonlinear vehicle model structured using a software for multi-body dynamic analysis (ADAMS). This model takes into account the nonlinear characteristics of actual vehicles with tires modeled by “magic formula“. The results of computer simulations show that the proposed nonlinear approach is efficient in improving the handling and stability of vehicles.     

Hydro-pneumatic Slow-active Suspension with Preview Control

In this work, the preview control problem is considered for fully active and hydro-pneumatic slow-active systems. Based on the quarter car model, linear optimal control theory is used to derive the control laws. The Pade approximation technique is used to represent the preview time resulting from a preview sensor mounted at the front bumper to measure the road irregularities ahead of the front wheels. The results for the slow-active system with preview showed that there is 15% improvement in ride comfort compared to slow-active without preview and 28.5% improvement over passive system at similar root mean square (r.m.s) dynamic tyre load and suspension working space. The performance gains are, however, lower by about 15% than those obtainable with the theoretically ideal, fully active system with preview. The power results for slow active with and without preview showed that a 2kW fixed displacement hydraulic pump is enough for full vehicle requirements.     

汽车半主动悬架的神经网络自适应控制

本文提出了用两个线性神经网络对汽车半主动悬架系统进行在线辨识和控制的策略，介绍了该控制系统中神经网络的在线训练方法，进行了仿真计算和结果分析。     

汽车主动悬架最优控制：采用频域计权形式性能指标函数

本文从提高汽车的乘坐舒适性角度出发，研究了主动悬架的最优控制问题。根据坐位人体的振动响应特性构造了频域计权形式二交型性能指标函数。     

Optimal Control of a Vehicle Suspension Incorporating the Time Delay between Front and Rear Wheel Inputs

An optimal control law for a vehicle suspension is developed using a discrete linear quadratic regulator framework. The time delay between the disturbance due to the road at the front and rear wheels is incorporated into the model, and it is shown that the optimal control law requires information gathered at the front wheels. A comparison is made between the optimal control law and a suboptimal one which does not incorporate front wheel road information.     

前期整车后悬架硬点优化设计

在某车型后悬架硬点前期开发中,确定了整车后悬架硬点设计开发以悬架KC特性作为优化设计工况。基于工程经验和四连杆式悬架特点,选择对悬架KC特性有较大影响的悬架设计参数,通过对比分析,选用合适的DOE方法、近似模型法和优化算法,实现了整车后悬架硬点的优化设计,提高了整车开发效率。     

Semi-Active Suspension with Preview Using a Frequency-Shaped Performance Index

The objective of this study is to develop a control law for a semi-active suspension for the purpose of ride quality improvement. The semi-active control law is determined by reproducing the control force of an optimally controlled active suspension while suppressing its damping coefficient variation. The performance index of the optimal control for the active suspension is modified to include frequency-shaping by use of Parseval's theorem, which allows us to de-emphasize the effects of particular variables over specific frequency bands.

Through the numerical simulations, it was found that the semi-active suspension may reduce the vertical acceleration of the driver's seat and the sprung mass motions significantly. The road-holding and tire deflections were not affected much.     

液压主动悬架的非线性自适应控制

以车身垂直加速度和悬架动行程为控制目标,同时引入非线性高通滤波器和非线性低通滤波器,基于逆向递推(Backstepping)技术,并考虑液压系统的非线性特性及其参数不确定性,提出了一种主动悬架的非线性自适应控制方法。仿真结果表明,在不同的激励信号作用下,都取得了较好的控制效果。     

Preview Control Algorithms for the Active Suspension of an Off-Road Vehicle

The potential performance improvement using preview control for active vehicle suspension was first recognized in the late nineteen sixties. All work done since that time has been based on optimal control theory using simple vehicle models.

In this article, the performance of quarter vehicle preview controllers when applied to a real off-road vehicle is simulated using both two degree of freedom quarter and ten degree of freedom full vehicle models. The results, which are compared with non-preview active and conventional passive suspensions, confirm that preview control reduces vertical acceleration of the body centre of gravity, which results in improved ride quality. Further, reductions in pitch and roll motion result from smaller vertical displacements of the vehicle quarters. Coupling between quarters, through the vehicle body, appears to have a smoothing effect on the control.

As an alternative to optimal control theory based controllers, a simple ad hoc preview controller based on isolating the vehicle body from dynamic loads transmitted through the suspension is proposed. Simulation results show that such a controller outperforms the optimal control theory based controllers over small discrete disturbances but responds poorly to disturbances encountered from other than steady state.     

非线性液压阻尼悬架分级控制

本文研究了主动阻尼悬架系统的分级控制策略，同时对各种影响系统性能参数的变化进行了研究，将分级控制的控制效果与最优阻尼控制作了比较。结果表明，结构简单的分级阻尼控制果接近最优阻尼控制。     

汽车半车主动悬架的最优控制研究

本文运用车辆动力学理论,建立了半车四自由度主动悬架系统的动力学模型。同时依据最优控制理论设计了主动悬架最优控制器并进行了Matlb+simulink仿真。研究结果表明采用最优控制的主动悬架比被动悬架具有更好的平顺性。     

主动悬架最优控制整车模型的研究

以一个车辆的整车模型为研究对象,通过利用轴距预瞄信息,应用最优控制理论设计了一个车辆的悬架控制策略,通过模拟和仿真的结果,验证了该模型和算法的可行性,并分析了轴距预瞄控制对于改进车辆性能的能力,检验了所建立的整车模型。     

基于混沌神经网络的最优路径选择算法

研究车载交通流诱导系统的最优路径选择问题。采用广义路阻的定义,考虑了驾驶员在路径选择中的不同要求,并借助一种具有暂态混沌和时变增益的神经网络(NNTCTG),针对最优路径选择问题设计了神经网络结构,构造了能量函数,提出了一种能够满足不同出行者偏好的最优路径选择算法。所提出的算法具有很多优良特性,即暂态混沌特性和平稳收敛性,能有效地避免传统Hopfield神经网络极易陷入局部极值的缺陷。它通过短暂的倒分叉过程,能很快进入稳定收敛状态。仿真结果表明,NNTCTG求解指定起讫点对之间的最优路径问题时,总能收敛到全局最优,同时具有更高的搜索效率。     

基于预测控制和频率成型性能指标的主动悬架控制策略研究

预测控制（包括轴距时间延迟）具有改善系统动态性能的能力,对性能指标的频率成型则可以改善乘坐舒适性。结合以上两种方法,提出了一种算法用于主动悬架控制策略的设计,并在时域和频域利用此算法对一个五自由度主动悬架车辆模型进行仿真,检验了算法的可行性和控制效果。