Performance Index for a Preview Active Suspension Applied to a Quarter-Car Model

A simple and convenient matrix expression is derived for the performance index in the case of a linear vehicle model with two degrees of freedom and a preview active suspension, subject to a unit step road input and employing optimal control. The usual quadratic integral-type performance index is assumed and the effect of an additional form of constraint is described briefly. The effects of preview time on the performance index and the optimal feed-forward control are illustrated graphically for a particular example.     

Optimization and Performance Enhancement of Active Suspensions for Automobiles under Preview of the Road

The problem of deriving control laws which minimize specified performance indices for a vehicle moving on a rough surface with preview of the surface elevation is considered. The approach is based on linear optimal tracking theory and consequently the system elements are taken to be linear and the performance index is constrained to be of quadratic form.

The ideas of overtaking optimality are applied to the problem in order to achieve a closed form solution for the control. Then, using the control laws derived, computer simulations of performance are conducted and time histories are shown. In the absence of limitations on either processing or actuator speeds, and for cases in which the preview is sufficient to give good control laws, the value of the preview in enhancing vehicle suspension performance is assessed. Comparisons are made with results in the literature.     

改进的有限时间最优预瞄横向控制器设计

提出了有限时间最优预瞄横向控制算法,根据高速公路车道保持系统实际控制要求,使用了同时考虑车辆当前偏差、预瞄点偏差和控制变量的有限时间二次型性能指标函数。运用最优跟踪算法并进行预瞄距离内曲率恒定的假设,使得控制器参数可以离线求解,保证了车辆控制的实时性。通过仿真及实车试验验证,该控制算法具有较好的跟踪效果。     

Physically Realisable Feedback Controls for a Fully Active Preview Suspension Applied to a Half-Car Model

An optimal preview type active suspension with feedback control based on easily measured relative displacements, velocities and accelerations is proposed. Measurements relative to the road, except by the preview sensor, are not required and the front and rear spring rates are quite arbitrary. Also, state estimators or observers are unnecessary and as a practical alternative the body accelerometers may be replaced by load cells. The effects of preview on the performance, for a theoretical step type road input and an analogous random road input, are described. The definition of the optimum preview function, and its consequent effects on performance, receives attention and an example is given.     

Optimal Preview Control of a Two-dof Vehicle Model Using Stochastic Optimal Control Theory

An optimal preview control algorithm is applied to a two degree of freedom(dof) vehicle model travelling with constant velocity on a randomly profiled road. The road roughness is modelled as a homogeneous random process being the output of a linear first order filter to white noise. The input from the road irregularity is assumed to be measured at some distance in front of the vehicle and this measured infonnation is utilized by the active controller to prepare the system for the ensuing input. The preview control algorithm is obtained by minimizing a quadratic performance index and by describing the average behaviour of the system by the covariance matrix of the vehicle response state vector. Results are presented for full state feedback and significant improvements in sprung mass acceleration, suspension working space and road holding are observed.     

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.     

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.     

Optimal control to modelling motorcycle rider steering: local versus global coordinate systems in rider preview

The response of a motorcycle is heavily dependent on the rider’s control actions, and consequently a means of replicating the rider’s behaviour provides an important extension to motorcycle dynamics. The primary objective here is to develop effective path-following simulations and to understand how riders control motorcycles. Optimal control theory is applied to the tracking of roadway by a motorcycle, using a non-linear motorcycle model operating in free control by steering torque input. A path-following controller with road preview is designed by minimising tracking errors and control effort. Tight controls with high weightings on performance and loose controls with high weightings on control power are defined. Special attention is paid to the modelling of multipoint preview in local and global coordinate systems. The controller model is simulated over a standard single lane-change manoeuvre. It is argued that the local coordinates point of view is more representative of the way that a human rider operates and interprets information. The simulations suggest that for accurate path following, using optimal control, the problem must be solved by the local coordinates approach in order to achieve accurate results with short preview horizons. Furthermore, some weaknesses of the optimal control approach are highlighted here.     

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.     

基于轴间预瞄的主动悬架研究

以某型汽车的1／2车辆模型为研究对象，提出了一种轴间预瞄控制的方法，并结合最优控制理论设计了车辆悬架的控制策略。通过模拟和仿真分析，验证了所提出的轴间预瞄比传统的轴距预瞄具有更好的控制效果。     

Optimal Linear Preview Control of Active Vehicle Suspension

SUMMARY

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.     

Road disturbance estimation for the optimal preview control of an active suspension systems based on tracked vehicle model

This research investigates stochastic estimation of a look-ahead sensor scheme using the optimal preview control for an active suspension system of a full tracked vehicle (FTV). In this scheme, wheel disturbance input to the front wheels are estimated using the dynamic equations of the system. The estimated road disturbance input at the front wheels are utilized as preview information for the control of subsequently following wheels of FTV. The design of optimal preview control is used as a classical linear quadratic Gaussian problem by combining dynamics of the original system and estimation of previewed road inputs. The effectiveness of the preview controller is evaluated by comparing the estimated information with the measured information for different road profiles, where Kalman filter is used for the state-variables estimation of the FTV. This research also considers the reduced order estimation using commonly available sensors in order to decrease the number of sensors and measurements. The simulation results’ using an active suspension system with different preview information shows that the proposed system can be beneficial for the improvement of ride comfort of tracked vehicles without using any specialized sensors for preview information calculation.     

结合卡尔曼滤波器的车辆主动悬架轴距预瞄控制研究

利用轴距预瞄信息，即前后轮路面输入之关系，同时结合卡尔曼滤波器作为状态估计器，本文提出了一种算法用于车辆悬架控制律的设计，根据模拟结果，研究了算法的可行性，分析了卡尔曼滤波器对状态变量的估计精度，以及轴距预瞄控制对进一步改进车辆性能的潜力。     

单轨车辆驾驶员单点预瞄模型的参数优化

使用ADAMS软件建立摩托车的模型,并利用MATLAB软件建立驾驶员模型,构成驾驶员-车辆模型。采用MATLAB和ADAMS协同仿真,得出单移线和双移线工况实际行驶轨迹和手把力矩、侧倾力矩、转向角、侧倾角、横向加速度等。建立了摩托车移线工况的性能综合评价指标,并用此指标对摩托车在不同运动状况下的性能进行评价。以驾驶员模型反馈系数为设计变量,采用正交设计方法优选出一组参数。     

汽车主动悬架的最优预见控制

本文针对1/2车辆模型,应用最优预见控制理论对汽车主动悬架进行控制系统的设计和研究。计算机仿真结果表明,所提出的系统能有效改善汽车乘坐舒适性。     

新型视觉区域智能车辆导航控制器设计

简要介绍了基于机器视觉导航区域智能车辆(CyberCar)的导航原理和组成。首先采用逆M序列作为辨识输入信号和最小二乘算法得到车辆转向系统的系统辨识特征方程,结合预瞄运动学模型和车辆二自由度转向动力学模型,从而建立车辆基于视觉预瞄的转向动力学控制数学模型,根据线性二次型最优控制理论得到状态线性反馈的最优控制规律。通过仿真分析和试验,验证了最优控制器在CyberCar户外路径跟踪过程中平稳、可靠。     

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.     

RMS Values for Force, Stroke and Deflection in a Quarter-car Model Active Suspension with Preview

Summary In the case of a quarter-car vehicle model incorporating an active suspension, the addition of preview to the system necessitates finding new techniques for the direct computation of rms values for control force, suspension stroke and tyre deflection on a random road. Here we obtain these rms values from the performance index components as derived analytically by means of matrix operations requiring the solution of Lyapunov equations and the use of similarity transformations. Some numerical examples and MATLAB programs are included.