基于LQR控制的现代客车自适应空气悬架

长期在不良工况的道路上驾驶会降低驾驶员的乘坐舒适性。随着人们对乘坐舒适性需求不断提升,空气弹簧的优势尤为明显。文章提出了一种基于LQR控制策略的自适应空气悬架系统的创新设计方案,提出的LQR控制器采用粒子群算法进行优化。以客车空气悬架为研究对象,采用MATLAB软件对空气悬架系统的被动和自适应动力学模型进行了设计和仿真。仿真结果表明,自适应空气悬架系统在保证车辆稳定性的同时,降低了车辆在随机道路上的最大位移幅值,从而提高了车辆的平顺性。     

悬架弹性元件最佳刚度分布的工程设计方法

基于车辆系统动、静力学分析及优化设计原理,提出了一种旨在揭示汽车悬架弹性元件最佳刚度分布的工程设计新方法,可确定满足整车平顺性和操纵稳定性协调设计要求的悬架弹性元件的刚度特性,从而为弹性元件的结构优化设计提供了目标依据,并通过一个工程实例验证了所提方法的有效性。     

Structured H2 Optimization of Vehicle Suspensions Based on Multi-Wheel Models

Summary Various control techniques, especially LQG optimal control, have been applied to the design of active and semi-active vehicle suspensions over the past several decades. However passive suspensions remain dominant in the automotive marketplace because they are simple, reliable, and inexpensive. The force generated by a passive suspension at a given wheel can depend only on the relative displacement and velocity at that wheel, and the suspension parameters for the left and right wheels are usually required to be equal. Therefore, a passive vehicle suspension can be viewed as a decentralized feedback controller with constraints to guarantee suspension symmetry. In this paper, we cast the optimization of passive vehicle suspensions as structure-constrained LQG/H2 optimal control problems. Correlated road random excitations are taken as the disturbance inputs; ride comfort, road handling, suspension travel, and vehicle-body attitude are included in the cost outputs. We derive a set of necessary conditions for optimality and then develop a gradient-based method to efficiently solve the structure-constrained H2 optimization problem. An eight-DOF four-wheel-vehicle model is studied as an example to illustrate application of the procedure, which is useful for design of both passive suspensions and active suspensions with controller-structure constraints.     

铰接式自卸车橡胶弹簧悬架系统试验研究

采用模拟随机输入路面谱激励室内台架试验的方法,对装有新型橡胶弹簧悬架系统的某型号铰接式自卸车进行台架试验研究,以评价橡胶弹簧悬架系统的减振性能和整车行驶平顺性。试验结果表明由于橡胶弹簧悬架系统某些参数匹配不合理导致该车行驶平顺性很不理想,通过优化悬架及座椅的刚度和阻尼参数,可提高整车行驶平顺性,并给出了座椅弹簧的优化结果。     

A design methodology for mechatronic vehicles: application of multidisciplinary optimization, multibody dynamics and genetic algorithms

A design methodology for mechatronic vehicles is presented. With multidisciplinary optimization (MDO) methods, strongly coupled mechanical, control and other subsystems are integrated as a synergistic vehicle system. With genetic algorithms (GAs) at the system level, the mechanical, control and other relevant parameters can be optimized simultaneously. To demonstrate the feasibility and efficacy of the proposed design methodology for mechatronic vehicles, it is used to resolve the conflicting requirements for ride comfort, suspension working spaces and unsprung mass dynamic loads in the optimization of half-vehicle models with active suspensions. Both deterministic and random road excitations, both rigid and flexible vehicle bodies and both perfect measurement of full state variables and estimated limited state variables are considered. Numerical results show that the optimized vehicle systems based on the methodology have better overall performance than those using the linear quadratic Gaussian (LQG) controller. It is shown that the methodology is suitable for complex design optimization problems where: (1) there is interaction between different disciplines or subsystems; (2) there are multiple design criteria; (3) there are multiple local optima; (4) there is no need for sensitivity analysis for the optimizer at the system level; and (5) there are multiple design variables.     

A design methodology for mechatronic vehicles: application of multidisciplinary optimization,multibody dynamics and genetic algorithms

A design methodology for mechatronic vehicles is presented. With multidisciplinary optimization (MDO) methods, strongly coupled mechanical, control and other subsystems are integrated as a synergistic vehicle system. With genetic algorithms (GAs) at the system level, the mechanical, control and other relevant parameters can be optimized simultaneously. To demonstrate the feasibility and efficacy of the proposed design methodology for mechatronic vehicles, it is used to resolve the conflicting requirements for ride comfort, suspension working spaces and unsprung mass dynamic loads in the optimization of half-vehicle models with active suspensions. Both deterministic and random road excitations, both rigid and flexible vehicle bodies and both perfect measurement of full state variables and estimated limited state variables are considered. Numerical results show that the optimized vehicle systems based on the methodology have better overall performance than those using the linear quadratic Gaussian (LQG) controller. It is shown that the methodology is suitable for complex design optimization problems where: (1) there is interaction between different disciplines or subsystems; (2) there are multiple design criteria; (3) there are multiple local optima; (4) there is no need for sensitivity analysis for the optimizer at the system level; and (5) there are multiple design variables.     

Robustness analysis of bogie suspension components Pareto optimised values

Bogie suspension system of high speed trains can significantly affect vehicle performance. Multiobjective optimisation problems are often formulated and solved to find the Pareto optimised values of the suspension components and improve cost efficiency in railway operations from different perspectives. Uncertainties in the design parameters of suspension system can negatively influence the dynamics behaviour of railway vehicles. In this regard, robustness analysis of a bogie dynamics response with respect to uncertainties in the suspension design parameters is considered. A one-car railway vehicle model with 50 degrees of freedom and wear/comfort Pareto optimised values of bogie suspension components is chosen for the analysis. Longitudinal and lateral primary stiffnesses, longitudinal and vertical secondary stiffnesses, as well as yaw damping are considered as five design parameters. The effects of parameter uncertainties on wear, ride comfort, track shift force, stability, and risk of derailment are studied by varying the design parameters around their respective Pareto optimised values according to a lognormal distribution with different coefficient of variations (COVs). The robustness analysis is carried out based on the maximum entropy concept. The multiplicative dimensional reduction method is utilised to simplify the calculation of fractional moments and improve the computational efficiency. The results showed that the dynamics response of the vehicle with wear/comfort Pareto optimised values of bogie suspension is robust against uncertainties in the design parameters and the probability of failure is small for parameter uncertainties with COV up to 0.1.     

A Two DOF Model for Jerk Optimal Vehicle Suspensions

Researchers have proposed various active suspension concepts to optimize the tradeoff between ride and handling in passenger vehicles. A few investigators suggested inclusion of the passenger jerk, the derivative of the passenger acceleration, as a measure of ride quality in the performance index. Minimization of a performance index then optimizes both the acceleration and jerk as well as other outputs representing handling quality and design constraints. This approach is called jerk optimal control.

This paper compares two different vehicle models of increasing complexity (the one and two DOF quarter car) using jerk optimal control. Different aspects of suspension performance are investigated, including the structure of the system transfer functions, the structure of the force control laws, and the tradeoffs between the various root mean square (rms) outputs defining system ride and handling performance. Tables compare the numerical results of the two models, allowing predictions of actual vehicle performance.

The results of the two models show the same basic trend for the tradeoff between ride and handling quality: at a constant level of rms passenger acceleration the rms passenger jerk can be reduced significantly, but only at a cost of increased rms tire deflections. In physical terms, a softer ride results in degraded handling performance. For a chosen level of ride improvement, the more realistic two DOF quarter car model predicts more severe degradation of handling. The latter nevertheless predicts a substantial increase in vehicle ride quality is possible through a 55% reduction in jerk. It is expected that actual suspensions could also produce significant increases in ride quality through jerk reduction. Jerk optimal suspensions could find use both in higher end passenger vehicles and in transports for vibration sensitive cargo.     

Analysis and optimization of air suspension system with independent height and stiffness tuning

Suspensions play a crucial role in vehicle comfort and handling. Different types of suspensions have been proposed to address essential comfort and handling requirements of vehicles. The conventional air suspension systems use a single flexible rubber airbag to transfer the chassis load to the wheels. In this type of air suspensions, the chassis height can be controlled by further inflating the airbag; however, the suspension stiffness is not controllable, and it depends on the airbag volume and chassis load. A recent development in a new air suspension includes two air chambers (rubber airbags), allowing independent ride height and stiffness tuning. In this air suspension system, stiffness and ride height of the vehicle can be simultaneously altered for different driving conditions by controlling the air pressure in the two air chambers. This allows the vehicle’s natural frequency and height to be adjusted according to the load and road conditions. This article discusses optimization of an air suspension design with ride height and stiffness tuning. An analytical formulation is developed to yield the optimum design of the new air suspension system. Experimental results verify the mathematical modeling and show the advantages of the new air suspension system.     

基于多目标粒子群优化算法的某轻型商用车操纵稳定性优化研究

针对某轻型商用车稳态回转时侧倾度偏大的问题对其悬架进行优化改进。基于ADAMS/car搭建整车多体动力学模型,通过前悬架反向平行轮跳试验、后悬架理论计算验证了悬架仿真模型的准确性。进行整车稳态回转工况和转向盘中间位置转向工况仿真分析,结果表明,车身侧倾度偏高。为实现操纵稳定性优化分析的流程自动化,提出了基于modeFRONTIER的联合仿真方法。以悬架设计参数为优化变量,以汽车的侧倾度与横摆角速度响应滞后时间为优化目标,采用拉丁超立方试验设计方法拟合得到混合代理模型,并结合多目标粒子群优化算法对悬架系统进行多目标优化,获得了悬架系统优化方案。优化结果显示,在不影响平顺性的前提下,汽车车身侧倾度降低了13.93%,横摆角速度响应滞后时间降低了2.75%,整车操纵稳定性得到了提升。     

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.     

Optimization for Vehicle Suspension II: Frequency Domain

The objective of this study is optimizing the components design of a vehicle suspension system under excitation due to road roughness. The vehicle is modelled as a dynamic system made of masses interconnected by, linear, springs and dampers. The optimizing code provides values corresponding to the caracteristics of masses, dampers and springs which, within a range, minimize the objective function for a defined excitation. This objective function auantifies the vehicle comfort level.

The optimization method used is the sequential linear programming by iteratively applying the Simplex algorithm. The model response is obtained in frequency domain and the vehicle excitation can be either random or deterministic.

The exact nature of the optimization problem, objective function and restrictions, depend on the type of excitation considered.

In succeeding paragraphs, the problem formulation together with a comparison with other authors is presented.     

Robust design optimization of the dynamic responses of a tracked vehicle system

This paper presents the robust design optimization of the dynamic responses of a heavy military tracked vehicle system. The tracked vehicle model addressed in this study has 954 degrees of freedom and consists of 189 bodies in total: 37 bodies for the chassis, such as sprockets, road wheels, road arms, etc.; 76 track link bodies for each track subsystem; 36 revolute joints; and 152 bushing elements. The design objectives were to minimize the maximum vertical acceleration of the hull and its variance while satisfying the wheel travel constraints for torsion bars and the hydro-pneumatic suspension units within ±1σ ranges. To avoid the difficulty of the design sensitivity analysis and to overcome the numerical noise, a progressive meta-model technique was employed in the optimization process. First, space-filling methods were used to determine the minimum number of sample points. Second, the simultaneous kriging method was used to construct the initial meta-models, and the augmented Lagrange multiplier (ALM) method was then used to solve the robust design problems of the meta-models. Third, the new design results were added to the analysis results for the initial sample points, and the meta-models were updated automatically. Next, the optimizer resolved the robust design problems of the updated meta-models. These processes were repeated until the convergence tolerances were satisfied. The robust design optimization of the tracked vehicle system, with 11 random design variables, was solved in only 26 analyses, including 12 analyses for the initial meta-models and 14 analyses added during the iterative optimization process.     

Designing a non-linear tracking controller for vehicle active suspension systems using an optimization process

In this paper, a new non-linear tracking controller for vehicle active suspension systems is analytically designed using an optimization process. The proposed scheme employs a realistic non-linear quarter-car model, which is composed of a hardening spring and a quadratic damping force. The control input is the external active suspension force and is determined by minimizing a performance index defined as a weighted combination of conflicting objectives, namely ride quality, handling performance and control energy. A linear skyhook model with standard parameters is used as the reference model to be tracked by the controller. The robustness of the proposed controller in the presence of modeling uncertainties is investigated. The performed analysis and the simulation results indicate that both vehicle ride comfort and handling performance can be improved using the minimum external force when the proposed non-linear controller is engaged with the model. Meanwhile, a compromise between different objectives and control energy can easily be made by regulating their respective weighting factors, which are the free parameters of the control law.     

Sequential Approximate Optimization of MacPherson Strut Suspension for Minimizing Side Load by Using Progressive Meta-Model Method

In a MacPherson strut suspension, the side load is inevitably generated and it causes friction at the damper reducing riding comfort. In this paper, to solve this problem, progressive meta-model based sequential approximate optimization (SAO) is performed to minimize the side load. To calculate the side load, a wheel travel analysis is performed by using flexible multi-body dynamics (FMBD) model of suspension, which can consider both finite element method (FEM) and multi-body dynamics (MBD). In the optimal design process, meta-model is generated by using extracted sampling points and radial basis function (RBF) method. As a result of optimal design, spring setting positions that minimize the side load are obtained and by using optimal spring setting positions, the suspension FMBD model was constructed.     

Enhancing grey prediction fuzzy controller for active suspension systems

A grey prediction fuzzy controller (GPFC) was proposed to control an active suspension system and evaluate its control performance. The GPFC employed the grey prediction algorithm to predict the position output error of the sprung mass and the error change as input variables of the traditional fuzzy controller (TFC) in controlling the suspension system to suppress the vibration and the acceleration amplitudes of the sprung mass for improving the ride comfort of the TFC used; however, the TFC or GPFC was employed to control the suspension system, resulting in a large tire deflection so that the road-holding ability in the vehicle becomes worse than with the original passive control strategy. To overcome the problem, this work developed an enhancing grey prediction fuzzy controller (EGPFC) that not only had the original GPFC property but also introduced the tire dynamic effect into the controller design, also using the grey prediction algorithm to predict the next tire deflection error and the error change as input variables of another TFC, to control the suspension system for enhancing the road-holding capability of the vehicle. The EGPFC has better control performances in suppressing the vibration and the acceleration amplitudes of the sprung mass to improve the ride quality and in reducing the tire deflection to enhance the road-holding ability of the vehicle, than both TFC and GPFC, as confirmed by experimental results.     

Enhancing grey prediction fuzzy controller for active suspension systems

A grey prediction fuzzy controller (GPFC) was proposed to control an active suspension system and evaluate its control performance. The GPFC employed the grey prediction algorithm to predict the position output error of the sprung mass and the error change as input variables of the traditional fuzzy controller (TFC) in controlling the suspension system to suppress the vibration and the acceleration amplitudes of the sprung mass for improving the ride comfort of the TFC used; however, the TFC or GPFC was employed to control the suspension system, resulting in a large tire deflection so that the road-holding ability in the vehicle becomes worse than with the original passive control strategy. To overcome the problem, this work developed an enhancing grey prediction fuzzy controller (EGPFC) that not only had the original GPFC property but also introduced the tire dynamic effect into the controller design, also using the grey prediction algorithm to predict the next tire deflection error and the error change as input variables of another TFC, to control the suspension system for enhancing the road-holding capability of the vehicle. The EGPFC has better control performances in suppressing the vibration and the acceleration amplitudes of the sprung mass to improve the ride quality and in reducing the tire deflection to enhance the road-holding ability of the vehicle, than both TFC and GPFC, as confirmed by experimental results.     

Optimization and integration of ground vehicle systems

This article deals with the optimal design of ground vehicles and their subsystems, with particular reference to 'active' safety and comfort. A review of state-of-the-art optimization methods for solving vehicle system design problems, including the integration of electronic controls, is given, thus further encouraging the use of such methods as standard tools for automotive engineers. Particular attention is devoted to the class of methods pertaining to complex system design optimization, as well as approaches for the optimal design of complex systems under uncertainty. Some examples of design optimizations are given in the fields of vehicle system dynamics, powertrain/internal combustion engine design, active safety and ride comfort, vehicle system design and lightweight structures, advanced automotive electronics, and smart vehicles.     

基于舒适性和轮胎动载的车辆悬架参数优化

为了改善汽车行驶的舒适性并减小轮胎对路面的动载，以某载货汽车为研究对象，建立了多目标优化模型，并采用统一目标函数法对车辆悬架参数进行优化。优化结果表明：优化后悬架刚度减小而阻尼增大，且前悬架参数变化较小，后悬架参数变化较大；相比于优化前，车身垂直方向加速度均方根值减小了20％，前轮动栽均方根值减小了40％，后轮变化更显著，减小了49％；采用多目标优化设计方法不仅可提高车辆自身的舒适性，而且可减小轮胎对路面的动载。     

Stochastic Optimal Control of Highway Tractors with Active Suspensions

Stochastic optimal control and estimation theories are used to design an active suspension system for a cab ride in a tractor-semitrailer vehicle. A discrete-continuous vehicle model with eleven degrees of freedom is augmented by a stochastic road excitation model and a human perception of vibration shape filter. Both perfect measurement and estimated state cases are considered. The impact of the measurement noise on the design of the optimal controller is demonstrated. The performance of the optimally controlled system is compared with an optimal passive system. It is shown that significant improvements in ride comfort can be achieved through the use of actively controlled cab suspensions.