具有非线性电液作动器的车辆悬架鲁棒PID控制

依据悬架实验装置，建立了电液作动器的非线性动力学方程，随后得到线性对象模型。作动器的非线性效果通过线性化模型的参数不确定性来体现。应用Matlab的非线性控制系统工具箱，设计了对象模型的鲁棒PID控制器。仿真和实验研究表明：该控制器对系统的不确定性具有较强的鲁棒性，提高了车辆悬架系统的性能。     

基于三自由度的智能汽车模糊滑模横向运动控制研究

针对智能汽车运动过程中存在的车身姿态变化问题以及运动控制精度问题，设计了一种基于非线性3自由度动力学模型的模糊滑模横向运动控制器。建立了包括侧倾运动的3自由度动力学模型，进行了模型线性化；对基于线性化处理后的动力学模型进行了滑模控制器设计，通过控制前轮转角实现了路径跟踪横向控制，并引入了模糊控制提高控制效果，本控制系统能够在跟踪过程中对车身姿态变化进行观察。仿真结果表明，搭建的基于3自由度动力学模型的模糊滑模控制器能够在考虑侧倾运动的基础上，实现路径跟踪，且构建的模糊滑模控制系统相较于传统滑模控制其横向偏差与方向偏差分别降低了7.28%和1.50%，同时模糊控制也减弱了滑模控制固有的抖振影响。     

燃料电池空气系统非奇异滑模控制

为了提高燃料电池的效率和使用寿命，必须对其多个状态进行精确的控制。首先，建立了面向控制的燃料电池空气系统四阶非线性方程，并进行模型有效性验证。然后，针对空气供给系统压力和空气流量的非线性和强耦合性等特点，提出了一种基于全局反馈线性化理论的非奇异终端滑模控制策略。反馈线性化通过对空压机转速和背压阀开度的协同控制，将非线性模型转化为线性模型，实现对阴极压力和空气流量的解耦；考虑空气系统在复杂环境下受到不确定性扰动，设计比例积分观测器对扰动进行观测以减少环境影响；在此基础上，设计非奇异滑模控制器。仿真结果表明：非奇异滑模控制的阴极压力和过氧比各误差积分均小于传统滑模和反馈线性化控制，可显著提高燃料电池空气供给控制系统精度和鲁棒性，对今后研发高精度燃料电池阴极空气供给控制系统提供参考。     

纯电动汽车制动避撞系统的建模与分析

针对纯电动汽车制动避撞系统,提出了基于反馈线性化的跟车距离、速度跟踪误差的滑模控制方法;考虑了模型非线性、系统参数不确定性和外部干扰的因素,建立车辆纵向动力学模型;采用指数趋近律的控制方法,设计了一种双输入双输出的汽车避撞系统控制器;并进行了跟车场景下制动避撞控制器的仿真。结果表明:该控制器避撞控制效果明显,在保证汽车行驶的舒适性的同时,跟车过程的跟踪误差小。     

模糊滑模变结构控制在DCT电控离合器上的应用

针对电控离合器系统的强非线性、难以建立精确的数学模型等特点,设计了滑模变结构控制器。利用模糊控制器来调整滑模趋近律参数,从而削弱了滑模控制的抖振现象。建立了无刷直流电机的数学模型,用该控制方法在MATLAB软件里对双离合器自动变速器起步、换挡进行仿真,并与传统PID控制器进行比较。仿真结果表明,该控制器跟踪指令信号性能良好,抗干扰性能强于PID控制器。     

纯电动汽车制动避撞系统的建模与分析EI北大核心CSCD

针对纯电动汽车制动避撞系统,提出了基于反馈线性化的跟车距离、速度跟踪误差的滑模控制方法;考虑了模型非线性、系统参数不确定性和外部干扰的因素,建立车辆纵向动力学模型;采用指数趋近律的控制方法,设计了一种双输入双输出的汽车避撞系统控制器;并进行了跟车场景下制动避撞控制器的仿真。结果表明:该控制器避撞控制效果明显,在保证汽车行驶的舒适性的同时,跟车过程的跟踪误差小。     

The Design of Feedback Controller for the Suspension System with Saturated Uncertain Magneto-theological Damper

首先分析了磁流变阻尼器悬架系统的闭环稳定性.应用饱和模型描述了磁流变阻尼器的主要物理特性,这一模型弱化了磁流变阻尼器Bouc-Wen模型的强非线性.接着利用凸包技术对悬架系统状态方程时变参数矩阵和非线性饱和项进行线性化处理.再根据相关引理和Lyapunov函数稳定性理论,将反馈控制器求解问题转化为若干LMI约束条件下的凸优化问题.最后通过求解LMI凸优化问题的最优解,设计出悬架系统的闭环反馈控制器,并给出了最大稳定吸引域.仿真结果表明,磁流变阻尼器的减振效果明显地优于传统阻尼器.     

基于模型预测控制的智能车轨迹跟踪控制研究

为了完成智能车的轨迹跟踪,提出一种基于模型预测控制的轨迹跟踪方法,利用将运动学模型这个非线性系统线性化的方案,来获得必须的线性时变系统,采取模型预测控制的三要素来设计控制器。并且基于MPC在控制过程中能增加多种约束的优点,建立基于车辆运动学模型的约束做轨迹跟踪仿真实验,最后,基于山东理工大学智能车平台上GPS提供的定位信息,在校园中采集路线并对前提规划好的的轨迹进行实车验证。实验结果表明:基于MPC算法所设计的控制器能快速且稳定地跟踪期望轨迹。     

应用于车辆纵向控制的无模型自适应滑模预测控制方法

鉴于汽车纵向动力学系统为典型的参数时变不定、多扰动的非线性离散系统,基于精确数学模型的控制算法较难取得理想效果,本文中采用无需模型、基于输入/输出数据的控制算法.首先,基于紧格式动态线性化数据模型,将无模型自适应控制(MFAC)算法、滑模控制(SMC)算法和模型预测控制(MPC)算法相结合,设计了无模型自适应控制器.接...     

基于微分几何的汽车主动悬架模糊PID控制研究

文章以汽车主动悬架为研究对象,结合二自由度非线性汽车主动悬架动力学模型,利用微分几何理论将非线性模型精确线性化后,首先设计PID控制器,为抑制干扰因素影响而引起系统参数或结构改变,提高悬架系统应对复杂工况下的自适应能力,然后设计了利用模糊控制原理对PID参数进行在线整定的自适应模糊PID控制器。仿真结果表明:相比于参数固定的被动悬架系统,采用该控制方法的主动悬架能够轻松应对各种工况,不仅在保证改善汽车乘坐的舒适性的情况下,同时进一步改善了车辆的行驶平顺性和行驶安全性,为汽车主动悬架系统控制策略设计提供实用性参考。     

Nonlinear feedforward–feedback control of clutch-to-clutch shift technique

To improve the shift quality of the vehicle with clutch-to-clutch gear shifts, a nonlinear feedforward–feedback control scheme is proposed for clutch slip control during the shift inertia phase. The feedforward control is designed based on flatness in consideration of the system nonlinearities, and the linear feedback control is given to accommodate the model errors and the disturbances. Lookup tables, which are widely used to represent complex nonlinear characteristics of powertrain systems, appear in their original form in the designed feedforward controller, while the linear feedback controller is calculated through linear matrix inequalities such that the control system is robust against the parameter uncertainties. Finally, the designed controller is tested on an AMESim powertrain simulation model, which contains a time-variant model of clutch actuators.     

汽车AMT自动离合器的改进模糊滑模控制

提出改进的模糊滑模控制器(MFSMC)实现离合器的自动控制。MFSMC采用饱和函数和模糊自适应系统来缓解抖振现象并提高其鲁棒性;采用局部线性化技术减少MFSMC的模糊规则数满足自动离合器的实时性要求。模拟结果显示所提出的控制器具有快速跟踪预定轨迹、对参数时变和外部扰动具有鲁棒性等特点。     

主动汽车悬架的非线性控制

本文采用１／４车模型对天棚阻尼器和主动悬架的动力学性能进行分析，针对执行器的非线性特性，探讨了微分几何法和反馈法线必互法在主动悬架控制中的应用，在系统控制设计中采用了离散滑模法，仿真结果显示非线性控制律能有效地改善主动悬架的隔振特性。     

Automatic Cruise Control of a Mechatronically Steered Vehicle Convoy

In this paper a convoy of two vehicles is considered where the second one is mechatronically operated. The convoy model used for simulation and controller design is derived by the method of multibody systems. A nonlinear cruise controller based on the concept of flat outputs in connection with exact state linearization is derived. A nonlinear local observer is also implemented. It is shown that such a system responds properly to arbitrary maneuvers performed by the driver of the leading vehicle.     

Automatic Cruise Control of a Mechatronically Steered Vehicle Convoy

In this paper a convoy of two vehicles is considered where the second one is mechatronically operated. The convoy model used for simulation and controller design is derived by the method of multibody systems. A nonlinear cruise controller based on the concept of flat outputs in connection with exact state linearization is derived. A nonlinear local observer is also implemented. It is shown that such a system responds properly to arbitrary maneuvers performed by the driver of the leading vehicle.     

汽车自动离合器的动态滑模控制

针对自动离合器的非线性特性、易受外部干扰和参数不确定性的特点,采用动态滑模控制器实现离合器的有效控制,该控制器能有效缓解抖振现象并提高自动离合器的鲁棒性.为验证所采用控制器的有效性和鲁棒性,建造了基于dSPACE的自动离合器快速控制原型试验平台并进行试验.结果表明,与PID控制器和常规滑模控制器相比,所采用的控制器具有更好的控制性能.     

Predictive control of a vehicle trajectory using a coupled vector with vehicle velocity and sideslip angle

In this paper, a predictive algorithm for vehicle trajectory control using the vehicle velocity and sideslip angle is proposed. Since the driving state of a vehicle generates nonholonomic constraint equations, it is difficult to control the trajectory with a conventional control algorithm. Furthermore, control vectors such as vehicle velocity and sideslip angle are coupled together; hence, a separate control for each variable is not suitable. In this study, a coupled control vector that combines the velocity and sideslip angle is proposed for the predictive control of vehicle trajectory. Since the coupled control vector is derived from the status of the vehicle’s motion, it is easy to generate a feedback control vector for the predictive controller. The coupled vector cannot be directly used as input to the vehicle systems; therefore, the vehicle input vector should be calculated from the control vector using a nonlinear function. Since nonlinear functions are not inserted in the control loop, they are calculated by the controller. Therefore, this method does not require a linearization process in the control logic, which enhances the stability and accuracy of the predictive controller.     

Almost disturbance decoupling control of mimo nonlinear system subject to feedback linearization and a feedforward neural network: Application to half-car active suspension system

A novel tracking and almost disturbance decoupling problem of multi-input, multi-output (MIMO) nonlinear systems based on feedback linearization and a multi-layered feedforward neural network approach has been proposed. The feedback linearization and neural network controller guarantees exponentially global uniform ultimate bounded stability and almost disturbance decoupling performance without using any learning or adaptive algorithms. The new approach renders the system to be stable with the almost disturbance decoupling property at each step when selecting weights to enhance the performance if the proposed sufficient conditions are maintained. One example, which cannot be solved by the existing approach of the almost disturbance decoupling problem because it requires the sufficient conditions that the nonlinearities that multiply the disturbances satisfy structural triangular conditions, is proposed to exploit the fact that the tracking and the almost disturbance decoupling performances are easily achieved by the proposed approach. In order to demonstrate the practical applicability, a famous half-car active suspension system is investigated.     

Second-order sliding mode controller with model reference adaptation for automatic train operation

In this paper, a new approach to model reference based adaptive second-order sliding mode control together with adaptive state feedback is presented to control the longitudinal dynamic motion of a high speed train for automatic train operation with the objective of minimal jerk travel by the passengers. The nonlinear dynamic model for the longitudinal motion of the train comprises of a locomotive and coach subsystems is constructed using multiple point-mass model by considering the forces acting on the vehicle. An adaptation scheme using Lyapunov criterion is derived to tune the controller gains by considering a linear, stable reference model that ensures the stability of the system in closed loop. The effectiveness of the controller tracking performance is tested under uncertain passenger load, coupler-draft gear parameters, propulsion resistance coefficients variations and environmental disturbances due to side wind and wet rail conditions. The results demonstrate improved tracking performance of the proposed control scheme with a least jerk under maximum parameter uncertainties when compared to constant gain second-order sliding mode control.