基于SIMULINK的车辆主动悬架LQG控制仿真研究

通过建立1／4车辆模型,应用最优控制理论进行了车辆主动悬架的LQG(Linear Quadratic Gaussian)控制器的设计,并在Matlab／Simulink环境中建立系统模型并进行仿真,将仿真结果与被动悬架仿真结果进行对比分析。仿真结果表明,具有LQG控制器的主动悬架对车辆行驶平顺性和乘坐舒适性的改善有良好的效果。     

基于Simulink的车辆主动悬架LQG控制器的设计

建立了二自由度1/4车体的数学模型,并利用线性最优化控制理论进行了汽车主动悬架的LQG控制器设计,并在Matlab/Simulink环境下进行仿真,结果表明具有LQG控制器的主动悬架对车辆行驶平稳性和乘坐舒适性有了很大的改善。     

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.     

基于单神经元的汽车方向自适应PID控制

针对汽车方向动力学控制存在的非线性和参数时变不确定性问题，提出了一种新的基于单神经元的汽车方向自适应PID控制算法。该算法利用了神经网络的自学习和自适应能力，实现了方向PID控制器的参数在线自整定，从而避免了传统的自适应PID控制必须在线辨识被控系统的参考模型参数而带来的计算工作量大的问题。仿真计算和场地试验验证表明该控制算法可有效地控制汽车按照预期给定的轨迹行驶，且保证了汽车方向闭环控制系统具有较强的适应性和鲁棒性。     

Powertrain Control for Active Damping of Driveline Oscillations

When a vehicle is subjected to acceleration or disturbances, the elasticity of the various components in the driveline may cause torsional vibrations which can result in an oscillating vehicle speed. These driveline oscillations are also known as shuffle and are low frequency oscillations corresponding to the first resonance frequency of the driveline. The oscillations give rise to, apart from material stress, noticeable lessened driveability. In this work, different ways to actively damp the oscillations are investigated. The idea is to use the engine as an actuator in order to achieve active damping, so-called active engine control. Different linear controllers are investigated and evaluated. The paper includes driveline modelling, control design and verifications by simulations, and tests in real vehicle. Implementation issues such as limited amount of available engine torque and parameter identifications are also discussed. A Linear-Quadratic-Gaussion (LQG) controller has been implemented and tested on a heavy duty truck. Results show that the LQG controller works well and active damping is achieved.     

Powertrain Control for Active Damping of Driveline Oscillations

When a vehicle is subjected to acceleration or disturbances, the elasticity of the various components in the driveline may cause torsional vibrations which can result in an oscillating vehicle speed. These driveline oscillations are also known as shuffle and are low frequency oscillations corresponding to the first resonance frequency of the driveline. The oscillations give rise to, apart from material stress, noticeable lessened driveability. In this work, different ways to actively damp the oscillations are investigated. The idea is to use the engine as an actuator in order to achieve active damping, so-called active engine control. Different linear controllers are investigated and evaluated. The paper includes driveline modelling, control design and verifications by simulations, and tests in real vehicle. Implementation issues such as limited amount of available engine torque and parameter identifications are also discussed. A Linear-Quadratic-Gaussion (LQG) controller has been implemented and tested on a heavy duty truck. Results show that the LQG controller works well and active damping is achieved.     

Robust control for 4WS vehicles considering a varying tire-road friction coefficient

A µ-synthesis for four-wheel steering (4WS) problems is proposed. Applying this method, model uncertainties can be taken into consideration, and a µ-synthesis robust controller is designed with optimized weighting functions to attenuate the external disturbances. In addition, an optimal controller is designed using the well-known optimal control theory. Two different versions of control laws are considered here. In evaluations of vehicle performance with the robust controller, the proposed controller performs adequately with different maneuvers (i.e., J-turn and Fishhook) and on different road conditions (i.e., icy, wet, and dry). The numerical simulation shows that the designed µ-synthesis robust controller can improve the performance of a closed-loop 4WS vehicle, and this controller has good maneuverability, sufficiently robust stability, and good performance robustness against serious disturbances.     

Model-independent self-tuning fault-tolerant control method for 4WID EV

To solve the problem of the existing fault-tolerant control system of four-wheel independent drive (4WID) electric vehicles (EV), which relies on fault diagnosis information and has limited response to failure modes, a modelindependent self-tuning fault-tolerant control method is proposed. The method applies model-independent adaptive control theory for the self-tuning active fault-tolerant control of a vehicle system. With the nonlinear properties of the adaptive control, the complex and nonlinear issues of a vehicle system model can be solved. Besides, using the online parameter identification properties, the requirement of accurate diagnosis information is relaxed. No detailed model is required for the controller, thereby simplifying the development of the controller. The system robustness is improved by the error based method, and the error convergence and input-output bounds are proved via stability analysis. The simulation and experimental results demonstrate that the proposed fault-tolerant control method can improve the vehicle safety and enhance the longitudinal and lateral tracking ability under different failure conditions.     

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.     

汽车转向与悬架系统的集成控制研究

在建立了汽车转向与悬架系统的综合模型的基础上,运用一种具有扩展的调节器结构LQG控制方法,设计了 主动悬架控制器,实现对车身横摆角速度、车身垂直加速度、车身侧倾角和俯仰角的集成控制,从而显著提高汽车的 平顺性、操纵稳定性和安全性。     

A Comparison of Adaptive LQG and Nonlinear Controllers for Vehicle Suspension Systems

A design feature of many computer-controlled suspension systems, is their ability to adapt control law parameters to suit prevailing road conditions. Here, for systems employing high bandwidth actuators and state variable feedback control, the benefits of such adaptation are shown to be at best marginal. An optimal adaptive LQG system is compared with a fixed structure nonlinear feedback controller in the context of a simple quarter-vehicle suspension model. Performance comparisons are made, and trends considered under more realistic conditions. In consequence the overall usefulness of this type of adaptation is called into question.     

Improved optimal controller for start-up of amt trucks in consideration of driver’s intention

A linear-quadratic optimal controller is proposed for vehicle start-up, which is designed as a linear feedback form of the states and the measured (estimated) disturbances. The requirements of less friction loss and less driveline shock are represented by the weighting matrices of the cost function. The driver’s intension is also considered and the controller gains are adjusted on-line accordingly. The designed control strategy is tested on a complete powertrain simulation model. Through large amount of simulations, it is verified that the system is robust to the variations of driving conditions, such as variation of vehicle mass and road grade. It is also shown that the control performance is influenced greatly by the estimation error of engine torque and clutch torque, and the acceptable level of mean estimation error is about ±10%.     

Cooperative regenerative braking control for front-wheel-drive hybrid electric vehicle based on adaptive regenerative brake torque optimization using under-steer index

In this study, cooperative regenerative braking control of front-wheel-drive hybrid electric vehicle is proposed to recover optimal braking energy while guaranteeing the vehicle lateral stability. In front-wheel-drive hybrid electric vehicle, excessive regenerative braking for recuperation of the maximum braking energy can cause under-steer problem. This is due to the fact that the resultant lateral force on front tire saturates and starts to decrease. Therefore, cost function with constraints is newly defined to determine optimum distribution of brake torques including the regenerative brake torque for improving the braking energy recovery as well as the vehicle lateral stability. This cost function includes trade-off relation of two objectives. The physical meaning of first objective of cost function is to maximize the regenerative brake torque for improving the fuel economy and that of second objective is to increase the mechanical-friction brake torques at rear wheels rather than regenerative brake torque at front wheels for preventing front tire saturation. And weighting factor in cost function is also proposed as a function of under-steer index representing current state of the vehicle lateral motion in order to generalize the constrained optimization problem including both normal and severe cornering situation. For example, as the vehicle approaches its handling limits, adaptation of weighting factor is possible to prioritize front tire saturation over increasing the recuperation of braking energy for driver safety and vehicle lateral stability. Finally, computer simulation of closed loop driver-vehicle system based on Carsim? performed to verify the effectiveness of adaptation method in proposed controller and the vehicle performance of the proposed controller in comparison with the conventional controller for only considering the vehicle lateral stability. Simulation results indicate that the proposed controller improved the performance of braking energy recovery as well as guaranteed the vehicle lateral stability similar to the conventional controller.     

Inverse neuro-fuzzy MR damper model and its application in vibration control of vehicle suspension system

In this paper, a magneto-rheological (MR) damper-based semi-active controller for vehicle suspension is developed. This system consists of a linear quadratic Gauss (LQG) controller as the system controller and an adaptive neuro-fuzzy inference system (ANFIS) inverse model as the damper controller. First, a modified Bouc–Wen model is proposed to characterise the forward dynamic characteristics of the MR damper based on the experimental data. Then, an inverse MR damper model is built using ANFIS technique to determine the input current so as to gain the desired damping force. Finally, a quarter-car suspension model together with the MR damper is set up, and a semi-active controller composed of the LQG controller and the ANFIS inverse model is designed. Simulation results demonstrate that the desired force can be accurately tracked using the ANFIS technique and the semi-active controller can achieve competitive performance as that of active suspension.     

车辆悬架的最优自适应与自校正控制

本文研究了车辆主动悬架自适应与自校正控制的策略与算法。     

Active Roll Control of Single Unit Heavy Road Vehicles

Summary Strategies are investigated for controlling active anti-roll systems in single unit heavy road vehicles, so as to maximise roll stability. The achievable roll stability improvements that can be obtained by applying active anti-roll torques to truck suspensions are discussed. Active roll control strategies are developed, based on linear quadratic controllers. It is shown that an effective controller can be designed using the LQG approach, combined with the loop transfer recovery method to ensure adequate stability margins. A roll controller is designed for a torsionally flexible single unit vehicle, and the vehicle response to steady-state and transient cornering manoeuvres is simulated. It is concluded that roll stability can be improved by between 26% and 46% depending on the manoeuvre. Handling stability is also improved significantly.     

Self-tuning control algorithm design for vehicle adaptive cruise control system through real-time estimation of vehicle parameters and road grade

The main purpose of this paper is to design a self-tuning control algorithm for an adaptive cruise control (ACC) system that can adapt its behaviour to variations of vehicle dynamics and uncertain road grade. To this aim, short-time linear quadratic form (STLQF) estimation technique is developed so as to track simultaneously the trend of the time-varying parameters of vehicle longitudinal dynamics with a small delay. These parameters are vehicle mass, road grade and aerodynamic drag-area coefficient. Next, the values of estimated parameters are used to tune the throttle and brake control inputs and to regulate the throttle/brake switching logic that governs the throttle and brake switching. The performance of the designed STLQF-based self-tuning control (STLQF-STC) algorithm for ACC system is compared with the conventional method based on fixed control structure regarding the speed/distance tracking control modes. Simulation results show that the proposed control algorithm improves the performance of throttle and brake controllers, providing more comfort while travelling, enhancing driving safety and giving a satisfactory performance in the presence of different payloads and road grade variations.     

基于混合遗传算法的主动悬架集成优化研究

作者提出的主动悬架的集成优化方法是以主动悬架的结构参数与LQG控制器为优化对象,以主动悬架系统输出的车身垂直加速度、悬架动位移、轮胎动位移和主动控制力的加权和为优化性能指标。同时提出了一种混合优化算法,它利用梯度算法每次迭代得到的结果来改进遗传算法的群体,而用遗传算法的最优个体与梯度算法的迭代解相比较,选择其中的最优点作为梯度算法下一步迭代的初始点。运用该混合遗传算法进行主动悬架系统的集成优化控制能有效地提高汽车行驶平顺性和安全性。     

基于遗传算法和LQG控制的汽车半主动悬架结构和控制参数的集成优化研究

传统半主动悬架的设计是先设计其结构参数后设计其控制器参数，这样易造成系统失去全局最优性能。针对这一问题，本文提出一种基于遗传算法和LQG控制的集成优化半主动悬架结构参数和控制参数的方法，通过理论分析和仿真结果表明此方法与传统优化方法相比，对改善汽车行驶平顺性和提高汽车行驶安全性具有较优的效果。     

模糊PID控制的电动汽车再生制动系统变换器的研究

提出了利用超级电容作为储能元件实现电动汽车再生制动的能量回收方案,分析了电动汽车控制系统的双向DC/DC变换器和电机驱动器的驱动降压电路、制动升压电路,设计了该控制系统的模糊自整定PID控制器。通过仿真研究表明,在车辆驱动降压变换时,模糊自整定PID控制的超级电容器在150 A左右的大电流放电情况下,超级电容仍能维持2.5 s的指定电压输出,车辆在额定功率下工作,通过降压变换,超级电容储存的能量迅速供给电机,有效提高了驱动电流,改善了起动及加速性能,有效增加了续驶里程。在制动升压变换时,模糊自整定PID控制的超级电容器电流基本跟随指令值上下波动,超级电容电压从120 V不断上升,使得该电容器的储能能力得到充分利用,实现了高水平的能量回收。