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.     

DSP-based self-organising fuzzy controller for active suspension systems

This study proposed a self-organising fuzzy controller (SOFC) for controlling an active suspension system to evaluate its control performance. During the control process, the SOFC continually updated the learning strategy in the form of fuzzy rules. The fuzzy rule table of this SOFC could be initially set to zero. This not only overcame the difficulty in finding appropriate membership functions and control rules for designing a fuzzy controller, but also solved the database problem where the fuzzy rules of a fuzzy controller, once determined, remained fixed and could not suitably regulate them in real time to optimise the dynamic response of the system required to gain the desired control performance. To demonstrate the applicability of the proposed SOFC for active suspension systems, a quarter-car hydraulic-servo suspension system was designed and constructed to evaluate the feasibility of active suspension control. Additionally, to conform to real-time application requirements in the vehicular industry, the SOFC was implemented with a digital signal processor to control the hydraulic-servo suspension system so that the control performance could be determined. The SOFC has shown a better control performance in suppressing the vibration amplitude of the vehicle body for enhancing the structural safety of the vehicle and increasing the life of the suspension system. It also effectively suppressed the amplitude of the vehicle body acceleration and reduced the tire deflection for improving the ride and the handling quality of a vehicle better than a passive control, as verified in experimental results.     

Using fuzzy logic controller and evolutionary genetic algorithm for automotive active suspension system

This study designs a fuzzy logic controller (FLC) for an active automobile suspension system in which the membership functions and control rules are optimized using a genetic algorithm (GA). The objective of the FLC is to strike an optimal balance between the ride comfort and the vehicle stability. The values of the crossover and mutation parameters in the GA are adapted dynamically during the convergence procedure using a fuzzy control scheme. The convergence state of the GA is determined by using a support vector machine (SVM) method to identify the variation in each of the genes of the best-fit GA chromosome following each iteration loop. The feasibility of the proposed GA-assisted FLC scheme is verified by performing a series of numerical simulations in which the characteristics of the controlled plant are compared with those observed in a passive suspension system and obtained under an optimal linear feedback controller. The results demonstrate that the GA-assisted FLC results in a lower suspension deflection, a reduced sprung mass acceleration and a lower bouncing distance between the tire and the ground.     

汽车主动悬架自调整模糊控制试验

以汽车操纵稳定性及行驶平顺性为控制目标,提出一种在线可调整的模糊控制算法,其模糊控制规则表可以用解析的方法进行计算。针对简化的汽车模型,为控制悬架系统的振动设计了自调整模糊控制器。与自适应控制主动悬架系统相比较,在两自由度悬架系统试验台架上进行了对比试验研究,结果表明该算法对汽车的振动控制具有明显效果,进一步说明提出的算法对汽车悬架系统的振动控制具有较好的适应性。     

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.     

基于参数自调整模糊控制方法的半主动空气悬架仿真分析

基于参数自调整模糊控制方法对半主动空气悬架系统进行了仿真分析和试验验证．以某空气悬架大客车1／4车辆模型为仿真对象，设计了参数自调整的模糊控制器，并以随机路面为输入、悬架动行程为约束条件、簧载质量振动加速度和车轮动载荷为评价指标，对模型进行了计算机仿真，同时依据仿真模型设计了空气悬架试验台。仿真和试验结果表明，当汽车行驶工况变化时，引入参数自调整模糊控制方法可以有效降低簧载质量振动加速度和车轮动载荷。     

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

SUMMARY

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.     

基于遗传算法的车辆半主动悬架控制系统参数优化

遗传算法(Genetic Algorithm,GA)是一种基于自然群体遗传演化机制的高效优化算法,它能模拟自然界生物进化过程,依据适者生存,优胜劣汰的进化规则,采用人工进化的方式对目标群体进行遗传操作,不断得到更优群体。文章根据遗传算法基本思路,在MATLAB/Simulink中搭建了基于遗传算法的车辆半主动悬架参数优化模型,利用该算法对半主动悬架控制系统参数进行了优化,并对优化结果进行了仿真分析,结果表明,优化后车辆簧载质量加速度均方根值降低31.1%、悬架动挠度均方根值降低11.2%、轮胎动载荷均方根值降低7.1%,车辆平顺性得到提升。     

Designing H∞/GH 2 static-output feedback controller for vehicle suspensions using linear matrix inequalities and genetic algorithms

This paper presents an approach to design the H_∞/GH ₂ static-output feedback controller for vehicle suspensions by using linear matrix inequalities (LMIs) and genetic algorithms (GAs). Three main performance requirements for an advanced vehicle suspension are considered in this paper. Among these requirements, the ride-comfort performance is optimized by minimizing the H_∞ norm of the transfer function from the road disturbance to the sprung mass acceleration, while the road-holding performance and the suspension deflection limitation are guaranteed by constraining the generalized H₂ (GH ₂) norms of the transfer functions from the road disturbance to the dynamic tyre load and the suspension deflection to be less than their hard limits, respectively. At the same time, the controller saturation problem is considered by constraining its peak response output to be less than a given limit using the GH ₂ norm as well. A four-degree-of-freedom half-car model with active suspension system is applied in this paper. Several kinds of H_∞/GH ₂ static-output feedback controllers, which use the available sprung mass velocities or the suspension deflections as feedback signals, are obtained by using the GAs to search for the possible control gain matrices and then resolving the LMIs together with the minimization optimization problem. These designed H_∞/GH ₂ static-output feedback controllers are validated by numerical simulations on both the bump and the random road responses which show that the designed H_∞/GH ₂ static-output feedback controllers can achieve similar or even better active suspension performances compared with the state-feedback control case in spite of their simplicities.     

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。     

Level and attitude control of the active suspension system with integral and derivative action

A 7-DOF full-car model with optimal active control suspension is utilized to evaluate the vehicle dynamic performances which are achieved through proposed controllers. The optimal controller, which includes the integral action for the suspension deflection, considerably improves the attitude control of a vehicle because the rolling and pitching motion in cornering and braking maneuvers are reduced, respectively. In the viewpoint of level control, the integral control acting on the suspension deflection results in the zero steady-state deflection in response to static body forces and ramp road input. The dynamic characteristics of the suspension control system are evaluated in terms of time domain and frequency domain. The simulations in the time domain demonstrate the advantages of the active suspension system obtained by penalizing the integral and derivative of suspension deflections and the derivative of roll and pitch angles in the performance index. The frequency characteristic curves obtained by simulations regarding integral action or derivative action show the increase of both ride comfort and road-holding performances by maximizing the use of suspension deflections. The potential of derivative control is shown by the performances of the car traveling over a bump and braking.     

基于磁流变阻尼器的半主动车辆座椅悬架模糊控制研究

设计基于磁流变阻尼器的半主动车辆座椅悬架系统的模糊控制器。用ADAMS对系统建立三维多刚体动力学模型,用MATLAB设计系统模糊控制器,并联合两者对整个系统进行仿真。仿真和台架试验结果表明,模糊控制策略能使该系统较好抑制垂直振动加速度,提高乘坐的舒适性。     

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.     

改进的模糊PID控制器对4自由度主动悬架振动控制的研究

建立了4自由度1／2车体力学模型,针对车辆悬架为一非线性、时滞、不确定系统,设计了一种改进的主动悬架模糊PID控制器。以SANTANA2000实车悬架为仿真参数,以阶跃信号激励为路面输入,在Matlab中进行了时域仿真。结果表明,改进的模糊PID控制的主动悬架对车身垂直加速度、悬架动挠度、轮胎动载荷等平顺性指标改善明显．响应达到稳定状态的时间也有了显著的缩短,车辆乘坐的舒适性和操纵稳定性优于被动悬架和单纯的模糊控制的主动悬架,对车辆主动悬架控制的开发具有参考价值。     

Level and attitude control of the active suspension system with integral and derivative action

A 7-DOF full-car model with optimal active control suspension is utilized to evaluate the vehicle dynamic performances which are achieved through proposed controllers. The optimal controller, which includes the integral action for the suspension deflection, considerably improves the attitude control of a vehicle because the rolling and pitching motion in cornering and braking maneuvers are reduced, respectively. In the viewpoint of level control, the integral control acting on the suspension deflection results in the zero steady-state deflection in response to static body forces and ramp road input. The dynamic characteristics of the suspension control system are evaluated in terms of time domain and frequency domain. The simulations in the time domain demonstrate the advantages of the active suspension system obtained by penalizing the integral and derivative of suspension deflections and the derivative of roll and pitch angles in the performance index. The frequency characteristic curves obtained by simulations regarding integral action or derivative action show the increase of both ride comfort and road-holding performances by maximizing the use of suspension deflections. The potential of derivative control is shown by the performances of the car traveling over a bump and braking.     

Decentralized neuro-fuzzy control for half car with semi-active suspension system

In this paper, a decentralized neuro-fuzzy controller has been created in order to improve the ride comfort and increase the stability for half car suspension system, which used the magneto-rheological damper as a semi-active device. Firstly, relative gain array and relative disturbance gain methods have been used for deriving a relation between inputs, disturbances and outputs to select pairing with minimum interaction to design a decentralize controller. Secondary, decentralized neuro-fuzzy controllers for front and rear chassis are designed to predict the required damping force taking the acceleration of the sprung mass and desired acceleration obtained by using pole-placement method as inputs. To predict the control voltage required for producing the force predicted by the controller, the inverse neuro-fuzzy model of MR damper has been designed. Simulation by using MATLAB programs has been created. The results show that the ride comforts and vehicle stability have been improved in comparison with the passive system.     

基于柔性模型的多轴汽车平顺性的仿真研究

基于弹性梁弯曲振动理论和模态分析法建立了多轴汽车平顺性分析的柔性模型。按照汽车行驶平顺性评价方法，运用建立的柔性模型，分析了车速、路面等级、悬挂质量的分布、车架刚度以及悬架系统的刚度和阻尼对多轴汽车平顺性的影响。分析结果表明：悬挂质量的弯曲振动是影响多轴汽车行驶平顺性的一个不可忽略的重要因素；常用的刚体模型不能准确地描述多轴汽车的平顺性，不适合用于多轴汽车平顺性的分析。     

Using the lead vehicle as preview sensor in convoy vehicle active suspension control

Both ride quality and roadholding of actively suspended vehicles can be improved by sensing the road ahead of the vehicle and using this information in a preview controller. Previous applications have used look-ahead sensors mounted on the front bumper to measure terrain beneath. Such sensors are vulnerable, potentially confused by water, snow, or other soft obstacles and offer a fixed preview time. For convoy vehicle applications, this paper proposes using the overall response of the preceding vehicle(s) to generate preview controller information for follower vehicles. A robust observer is used to estimate the states of a quarter-car vehicle model, from which road profile is estimated and passed on to the follower vehicle(s) to generate a preview function. The preview-active suspension, implemented in discrete time using a shift register approach to improve simulation time, reduces sprung mass acceleration and dynamic tyre deflection peaks by more than 50% and 40%, respectively. Terrain can change from one vehicle to the next if a loose obstacle is dislodged, or if the vehicle paths are sufficiently different so that one vehicle misses a discrete road event. The resulting spurious preview information can give suspension performance worse than that of a passive or conventional active system. In this paper, each vehicle can effectively estimate the road profile based on its own state trajectory. By comparing its own road estimate with the preview information, preview errors can be detected and suspension control quickly switched from preview to conventional active control to preserve performance improvements compared to passive suspensions.     

Designing H ∞/GH 2 static-output feedback controller for vehicle suspensions using linear matrix inequalities and genetic algorithms

This paper presents an approach to design the H _∞/GH ₂ static-output feedback controller for vehicle suspensions by using linear matrix inequalities (LMIs) and genetic algorithms (GAs). Three main performance requirements for an advanced vehicle suspension are considered in this paper. Among these requirements, the ride-comfort performance is optimized by minimizing the H _∞ norm of the transfer function from the road disturbance to the sprung mass acceleration, while the road-holding performance and the suspension deflection limitation are guaranteed by constraining the generalized H ₂ (GH ₂) norms of the transfer functions from the road disturbance to the dynamic tyre load and the suspension deflection to be less than their hard limits, respectively. At the same time, the controller saturation problem is considered by constraining its peak response output to be less than a given limit using the GH ₂ norm as well. A four-degree-of-freedom half-car model with active suspension system is applied in this paper. Several kinds of H _∞/GH ₂ static-output feedback controllers, which use the available sprung mass velocities or the suspension deflections as feedback signals, are obtained by using the GAs to search for the possible control gain matrices and then resolving the LMIs together with the minimization optimization problem. These designed H _∞/GH ₂ static-output feedback controllers are validated by numerical simulations on both the bump and the random road responses which show that the designed H _∞/GH ₂ static-output feedback controllers can achieve similar or even better active suspension performances compared with the state-feedback control case in spite of their simplicities.     

汽车非线性半主动悬架的模糊神经网络控制

考虑磁流变减振器阻尼力和悬架弹性元件非线性特性，建立车辆6自由度的半主动悬架非线性动力学模型。提出了一种基于模糊神经网络系统结构的模型参考自适应控制方法来研究汽车半主动悬架的非线性控制问题，并考虑半车模型前后悬架的输入时滞，对其进行了仿真研究。研究结果表明：运用模糊神经网络非线性控制方法能够使人体和车身垂直加速度、俯仰角加速度都得到很大的衰减，证实这种模糊神经网络控制方法可大大减少路面对车身的振动冲击，提高汽车行驶平顺性。