外文翻譯--汽車路徑跟蹤的綜合控制器（英文）

1、Multibody Syst Dyn (2015) 33:207–228 DOI 10.1007/s11044-014-9409-8Integrated robust controller for vehicle path followingBehrooz Mashadi · Pouyan Ahmadizadeh · Majid Majidi · Mehdi Mahmoodi-KaleybarReceive

2、d: 8 March 2013 / Accepted: 8 January 2014 / Published online: 5 February 2014 © Springer Science+Business Media Dordrecht 2014Abstract The design of an integrated 4WS + DYC control system to guide a vehicle on a de

3、sired path is presented. The lateral dynamics of the path follower vehicle is formulated by considering important parameters. To reduce the effect of uncertainties in vehicle parame- ters, a robust controller is designed

4、 based on a μ-synthesis approach. Numerical simulations are performed using a nonlinear vehicle model in MATLAB environment in order to in- vestigate the effectiveness of the designed controller. Results of simulations s

5、how that the controller has a profound ability to making the vehicle track the desired path in the presence of uncertainties.Keywords Vehicle path following · 4WS · DYC · Robust control · μ-SynthesisN

6、otationAlphabetic A State-space matrixB Input matrixC Output matrixC Set of complex numbersCαf (Cαr) Cornering stiffness of front (rear) tiresd Output from perturbation blockD Constant scaling matrixE Disturbance matrixc

7、.g. Center of gravityB. Mashadi · P. Ahmadizadeh (B ) · M. MajidiSchool of Automotive Engineering, Iran University of Science and Technology, Tehran, Iran e-mail: p_ahmadizadeh@iust.ac.irM. Majidi e-mail: m_maj

8、idi@iust.ac.irM. Mahmoodi-Kaleybar School of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran e-mail: m_mahmoodi_k@iust.ac.irIntegrated robust controller for vehicle path following 2091 Int

9、roductionWith the population growth, the numbers of vehicles and passengers have been increased in the streets and highways leading to more traffic problems. Several measures such as building new highways and roads have

10、been considered in order to reduce traffic congestion and increasing safety, but such measures do not always suffice because of environmental and cost constraints. One way to improve the safety of roads is to remove the

11、human element errors during driving. This can lead to automatic driving technology, which is the fundamental of intelligent transportation systems (ITS) and is being studied by many researchers during recent years. The p

12、rimary task of automatic driving is to make an autonomous vehicle to follow a reference path automatically. During this path following, therefore, several issues should be considered to have an acceptable path control. V

13、ehicle controllers that meet these requirements are called path followers. The goal of a path-following controller is to minimize the lateral distance between the vehicle and a defined path, to minimize the difference in

14、 the vehicle and the path head- ings and to limit the control input to smoothen the motions while maintaining the stability. Several studies have been carried out regarding the path-following problem. El Hajjaji et al. [

15、1] focused on the design of a stabilizing fuzzy controller for the path-following prob- lem of vehicles using a nonlinear dynamics model. The vehicle model is approximated by a set of linear models interpolated by fuzzy

16、membership functions, and then a model-based fuzzy controller is developed to stabilize the model. Then the outcome of the path-following problem is parameterized in terms of a linear matrix inequality (LMI) problem. The

17、 LMI problem is solved by a convex optimization technique to complete the fuzzy path-following control design for vehicles. Consolini et al. [2] considered a special path-following task so that a given front point of a c

18、ar-like vehicle, which is within the look-ahead range of a stereo vision system, to follow a prespecified Cartesian path. A solution to this path-following problem was provided by a feedback/feedforward control strategy

19、where the feedforward was determined by a dynamic generator based on exact dynamic inversion over the nominal vehicle model and the feedback was mainly issued by correcting terms proportional to the tangential and normal

20、 errors determined with respect to the vehicle’s ideal trajectory. Bal- luchi et al. [3] considered a kinematic model of a nonholonomic wheeled vehicle to follow a path. They assumed that the current distance and the hea

21、ding angle error with respect to the closest point on the reference path can be measured but only the sign of the path cur- vature is detected. They used a hybrid system formalism to model the problem based on optimal co

22、ntrol theory, as the feedback information was both continuous and discrete. Hell- ström et al. [4] propose a path-tracking algorithm called Follow the Past, in which a human driver drives the path once, while the co

23、mputer records the position, velocity, orientation, and steering angle. Then this piece of information is used to control the vehicle each time it autonomously travels along the path. If the vehicle gets off the course,

24、for example, as a result of avoiding an obstacle or because of noise in positioning sensors, the Follow the Past algorithm steers like the driver, plus an additional angle, based on the distance to the path. Heredia et a

25、l. [5] present a method to analyze the stability of an autonomous vehicle path-following algorithm taking into account explicitly the computation and communica- tion delays in the control loop. These pure delays are pres

26、ent in autonomous vehicles due to position estimation. The problem is analyzed by solving directly the transcendental char- acteristic equation that appears when the time delay is considered. The analysis is carried out

27、for straight paths and paths of constant curvature, and the method is applied to the pure pursuit path-tracking algorithm. Goodarzi et al. [6] treated this problem by the application of a linear quadratic regulator (LQR)