The Model Reference Adaptive Fuzzy Control for the Vehicle Semi-Active Suspension

The LQG control system is employed as vehicle suspension's optimal target system, which has an adaptive ability to the road conditions and vehicle speed in a limited bandwidth. In order to keep the optimal performances when the suspension parameters change, a model reference adaptive fuzzy control (MRAFC) strategy is presented. The LQG control system serves as the reference model in the MRAFC system. The simulation results indicate that the presented MRAFC system can adapt to the parameters variation of vehicle suspension and track the optimality of the LQG control system, the presented vehicle suspension MRAFC system has the ability to adapt to road conditions and suspension parameters change.

Fuzzy Control of Model Travel Tracking for Vehicle Semi-Active Suspension

The control strategy of the model travel tracking for the vehicle suspension sys tem is presented based on analyzing the responses of the vehicle suspension tra vel. A fuzzy control system of vehicle suspension is designed, in which the sus pension travel output of the adaptive LQG control system is taken as the tracking objective. The simulation results prove that the suspension travel and vertical acceleration can be tracked simultaneously with the simple fuzzy controller, and the tracking effect of fuzzy control is better than that of the PID controller.

Fuzzy Logic Control for Semi-Active Suspension System of Tracked Vehicle

The model of half a tracked vehicle semi-active suspension is established. The fuzzy logic controller of the semi-active suspension system is constructed. The acceleration of driver's seat and its time derivative are used as the inputs of the fuzzy logic controller, and the fuzzy logic controller output determines the semi-active suspension controllable damping force. The fuzzy logic controller is to minimize the mean square root of acceleration of the driver's seat. The control forces of controllable dampers behind the first road wheel are obtained by time delay, and the delay times are determined by the vehicle speed and axles distances. The simulation results show that this control method can decrease the acceleration of driver's seat and the suspension travel of the first road wheel, the ride quality is improved obviously.

A tunable fuzzy logic controller for the vehicle semi-active suspension system

On the basis of analyzing the system constitution of vehicle semi-active suspension, a 4-DOF (degree of freedom) dynamic model is established. A tunable fuzzy logic controller is designed by using without quantification method and taking into account the uncertainty, nonlinearity and complexity of parameters for a vehicle suspension system. Simulation to test the performance of this controller is performed under random excitations and definite disturbances of a C grade road, and the effects of time delay and changes of system parameters on the vehicle suspension system are researched. The numerical simulation shows that the performance of the designed tunable fuzzy logic controller is effective, stable and reliable.

HYBRID FUZZY CONTROL FOR ELECTRO-HYDRAULIC ACTIVE DAMPING SUSPENSION

A new control scheme, the hybrid fuzzy control method, for active dampingsuspension system is presented. The scheme is the result of effective combination of the statisticaloptimal control method based on the statistical property of suspension system, with the bang-bangcontrol method based on the real-time characteristics of suspension system. Computer simulations areperformed to compare the effectiveness of hybrid fuzzy control scheme with that of optimal dampingcontrol, bang-bang control, and passive suspension. It takes the effects of time-variant factorsinto full account. The superiority of the proposed hybrid fuzzy control scheme for active dampingsuspension to the passive suspension is verified in the experiment study.

Skyhook Surface Sliding Mode Control on Semi-Active Vehicle Suspension System for Ride Comfort Enhancement

A skyhook surface sliding mode control method was proposed and applied to the control on the semi-active vehicle suspension system for its ride comfort enhancement. A two degree of freedom dynamic model of a vehicle semi-active suspension system was given, which focused on the passenger’s ride comfort perform-ance. A simulation with the given initial conditions has been devised in MATLAB/SIMULINK. The simula-tion results were showing that there was an enhanced level of ride comfort for the vehicle semi-active sus-pension system with the skyhook surface sliding mode controller.

Applying machine learning for cars’semi-active air suspension under soft and rigid roads

To improve the ride quality and enhance the control efficiency of cars’semi-active air suspensions(SASs)under various surfaces of soft and rigid roads,a machine learning(ML)method is proposed based on the optimized rules of the fuzzy control(FC)method and car dynamic model for application in SASs.The root-mean-square(RMS)acceleration of the driver’s seat and car’s pitch angle are chosen as the objective functions.The results indicate that a soft surface obviously influences a car’s ride quality,particularly when it is traveling at a high-velocity range of over 72 km/h.Using the ML method,the car’s ride quality is improved as compared to those of FC and without control under different simulation conditions.In particular,compared with those cars without control,the RMS acceleration of the driver’s seat and car’s pitch angle using the ML method are respectively reduced by 30.20% and 19.95% on the soft road and 34.36% and 21.66% on the rigid road.In addition,to optimize the ML efficiency,its learning data need to be updated under all various operating conditions of cars.

Research on Suspension Gravity Compensation System of Lunar Rover with Magnetic Levitation Servo

In order to overcome the shortcomings of the traditional sling suspension method,such as complex structure of suspension truss,large running resistance,and low precision of position servo system,a gravity compensation method of lunar rover based on the combination of active suspension and active position following of magnetic levitation is proposed,and the overall design is carried out.The dynamic model of the suspension module of microgravity compensation system was established,and the decoupling control between the constant force component and the position servo component was analyzed and verified.The constant tension control was achieved by using hybrid force/position control.The position following control was realized by using fuzzy adaptive PID(proportional⁃integral⁃differential)control.The stable suspension control was realized based on the principle of force balance.The simulation results show that the compensation accuracy of constant tension could reach more than 95%,the position deviation was less than 5 mm,the position deviation angle was less than 0.025°,and the air gap recovered stability within 0.1 s.The gravity compensation system has excellent dynamic performance and can meet the requirements of microgravity simulation experiment of lunar rover.

Review:Advances in the control of mechatronic suspension systems

The suspension system is a key element in motor vehicles. Advancements in electronics and microprocessor technology have led to the realization of mechatronic suspensions. Since its introduction in some production motorcars in the 1980 s, it has remained an area which sees active research and development, and this will likely continue for many years to come. With the aim of identifying current trends and future focus areas, this paper presents a review on the state-of-the-art of mechatronic suspensions. First, some commonly used classifications of mechatronic suspensions are presented. This is followed by a discussion on some of the actuating mechanisms used to provide control action. A survey is then reported on the many types of control approaches, including look-ahead preview, predictive, fuzzy logic, proportional–integral–derivative(PID), optimal, robust, adaptive, robust adaptive,and switching control. In conclusion, hydraulic actuators are most commonly used, but they impose high power requirements, limiting practical realizations of active suspensions. Electromagnetic actuators are seen to hold the promise of lower power requirements, and rigorous research and development should be conducted to make them commercially usable. Current focus on control methods that are robust to suspension parameter variations also seems to produce limited performance improvements, and future control approaches should be adaptive to the changeable driving conditions.

车辆MR半主动悬架的Fuzzy-PID控制研究

模糊控制的优点是不需要掌握受控对象的精确数学模型,而车辆悬架系统是典型的时变、非线性系统,其建模困难并具有不确定性,因此模糊控制策略广泛地应用于车辆半主动悬架的控制系统。本文分析比较模糊与模糊PID复合控制策略对车辆MR半主动悬架的控制效果,得出采用在线分别针对调节PID控制器3个参数的模糊自适应PID控制策略的悬挂系统具有鲁棒性强,超调量小,抗干扰,调整时间短等特点。

结合改进ANFIS的车辆半主动悬架振动控制

为改善MR阻尼器半主动悬架的减振效果,提出一种基于改进自适应神经模糊推理系统(ANFIS)的半主动控制方法。首先,针对MR阻尼器的逆向动力学模型难以精确确定的问题,采用改进乌鸦搜索算法(MCSA)对ANFIS进行优化,即分别用MCSA和最小二乘法对ANFIS的前件参数和后件参数进行寻优,以克服标准ANFIS易于陷入局部最优解的缺陷。为了提高标准乌鸦搜索算法(CSA)的搜索精度,采用三角概率分布策略选择目标乌鸦,并对更新后的解实施反转变异操作。然后,根据悬架响应设计LQR控制器以计算理想控制力,并与改进逆向模型相结合,实现理想控制力与MR阻尼器输入控制信号之间的转化,从而调节阻尼力,实现车辆半主动悬架系统的振动控制。仿真结果表明:相较于GA-ANFIS和PSO-ANFIS,所提出的MCSA-ANFIS逆向建模方法具有更高的预测精度,使MR阻尼器的输入信号和阻尼力的预测精度分别提高17.49%和30.62%;以随机路面信号作为半主动悬架的激励,相较于被动控制和LQR-COC半主动控制,所提出的LQR-MCSA-ANFIS控制策略能使簧载质量加速度、悬架动行程和轮胎动载荷的均方根值分别下降12.37%、37.63%、30.70%以及6.64%、14.89%、17.27%。该半主动控制策略可为MR阻尼器悬架系统的减振研究提供参考。

汽车半主动座椅悬架自适应模糊神经滑模控制

针对含有人体模型的5自由度座椅悬架系统,设计一种基于自适应模糊神经网络(Adaptive Neuro-Fuzzy Inference System,ANFIS)的滑模控制器(ANFIS-SMC)。首先,设计一种时变滑模面,通过调整其斜率使系统状态点快速到达滑模面,从而提升系统控制速度;其次,通过ANFIS对切换增益在线调节,用切换项消除干扰,实现控制器在复杂多源干扰下精确控制;最后,仿真验证采用双曲正切函数代替切换项中的符号函数,使得输出更连续,有效降低抖振。仿真结果表明:该控制器能有效提高系统的跟踪性能和响应速度,对不确定性干扰具有较好的鲁棒性,带有该控制器的座椅悬架乘坐舒适性明显改善。

基于ANFIS的空气悬架执行器故障诊断研究

悬架作为汽车底盘不可或缺的一部分,一旦发生故障会对汽车甚至驾驶员产生恶劣影响。为了找到悬架发生故障的位置,短时间内减轻对车辆结构的进一步损伤,并保障驾驶员及乘客的安全,针对电控空气悬架执行器故障,提出了一种自适应模糊神经网络系统(ANFIS)的故障诊断与隔离方法。设计ANFIS观测器组,通过传感器的输出值为真实数据,ANFIS观测器判断发生故障的位置进行诊断。利用AMESim软件搭建电控空气悬架系统物理模型与Simulink搭建PID/PWM车身高度控制器模型、故障模型、随机路面模型与故障诊断模型,最后通过AMESim-Simulink联合仿真进行验证。仿真结果得出不同故障下输出值与其对应的诊断结果的预期输出值差异较小,整个悬架系统的平均诊断误差为0.048,准确率为95%,验证了此方法的有效性与准确性。

车辆半主动悬架模糊变权重因子自适应控制研究

针对传统悬架控制策略中权重因子固定不可变的问题,提出了车辆半主动悬架模糊变权重因子自适应控制系统,该控制系统由3个模糊控制回路组成,3个模糊控制器分别控制影响车辆动态性能的3个悬架响应参数。α模糊控制器、β模糊控制器和γ模糊控制器根据路面条件分别控制簧载质量加速度控制函数权重因子、动载荷控制函数权重因子和动挠度控制函数权重因子,分别建立了各模糊控制器的控制规则和输入输出变量的函数关系。车辆半主动悬架模糊变权重因子自适应控制系统的控制原理是在以A级、B级路面为代表的高等级路面上增大簧载质量加速度控制权重,以提高车辆的舒适性;在以C级路面为代表的中等级路面上增大动挠度控制权重,以提高车辆综合性能;在以D级路面为代表的低等级路面上增大动载荷控制权重,以提高车辆安全性。凸块路面激励仿真结果表明,相比于被动控制和定权重因子控制,模糊变权重因子自适应控制的簧载质量加速度峰值分别降低了40.2%和28.6%,动挠度峰值分别降低了41.5%和21.0%,动载荷峰值分别降低了40.1%和31.8%,各响应的衰减速度几乎没有减小,衰减时间基本保持一致,在衰减过程中并没有明显的振荡现象。随机路面激励仿真结果表明,相对于被动控制和定权重因子控制,模糊变权重因子自适应控制的簧载质量加速度均方根值分别降低了48.8%和23.5%,动挠度均方根值分别降低了23.6%和13.4%,动载荷均方根值分别降低了11.9%和9.7%,悬架性能得到了大幅改善。在低等级路面激励输入时,安全性指标改善量较大;在高等级路面激励输入时,舒适性指标改善量较大;在中等级路面激励输入时,综合性指标改善量较大,仿真结果证明了所提控制系统的有效性和正确性。

汽车半主动空气悬架自适应模糊神经网络控制

考虑空气悬架弹簧刚度可调的特性,建立了车辆5自由度的半主动悬架非线性动力学模型.提出了一种基于自适应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空气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,对其进行了仿真分析.研究结果表明:该控制方法能够使人体垂直加速度、车身垂直加速度和俯仰角加速度都得到很大的衰减,可在一定程度上减少路面对车身的振动冲击,提高汽车的行驶平顺性.

汽车磁流变半主动悬架自适应模糊控制研究

针对汽车磁流变半主动悬架存在非线性及不确定性等因素而难以控制的问题,提出采用自适应模糊控制策略并进行了研究。在分析磁流变减振器输入输出特性的基础上,针对1/4车辆悬架模型设计了自适应模糊控制器并进行了仿真分析。以某微型车为试验用车,搭建了平顺性道路试验系统,进行了不同车速、不同控制策略(自适应模糊控制和天棚控制)下的随机路面试验,试验结果与仿真结果相吻合,说明将自适应模糊策略应用于半主动控制是可行的,能够抑制车身的垂直振动,提高乘坐的舒适性,且控制效果要优于天棚控制。

车辆半主动悬架自适应模糊控制

给出了车辆半主动悬架自适应模糊控制方法 ,设计了自适应模糊控制器 ,将模糊系统辨识和模糊控制结合起来 ,对自适应模糊控制规则进行了修正 ,使自适应模糊控制的复杂性得以简化 ,提高了半主动悬架控制的实时性 ,从而达到最佳的控制效果。与被动悬架的对比分析表明 。

车辆半主动悬架的模糊控制

建立了履带式车辆1/2车半主动悬架控制的状态方程模型。提出一种了半主动悬架模糊控制规则的产生方法。设计了1/2车半主动悬架的模糊控制系统。仿真分析表明,所提出的模糊控制规则无论对随机路面输入还是确定性路面输入都具有良好的适应性。

基于神经网络的半主动悬架自适应模糊控制

提出了基于神经网络的自适应模糊控制策略。模糊控制主要用来对付系统的非线性 ;神经网络根据振动响应的方差递推结果来辨识车体的振动情况实时调节模糊控制器的量化因子 ,使模糊控制器对路面的变化具有自适应的能力。在半主动悬挂 1/ 4车非线性模型的基础上进行了仿真研究。

电磁型混合磁极直接自适应模糊悬浮控制方案的研究

电磁型电磁永磁混合悬浮磁悬浮列车的悬浮系统是一个开环不稳定、强非线性时变系统。该文在分析了混合悬浮系统的工作原理的基础上,给出了一种电磁型磁悬浮系统直接自适应模糊控制器的设计方法。为了使控制算法在气隙变化的情况下能更快地收敛,提出了一种新的复合自适应算法。该算法能更加充分利用已有的先验知识,从而使系统的动态响应速度更快,跟踪误差更小。计算机仿真和实验结果都验证了理论分析的正确性。