Comparative Analysis of MacAdam Model and PI Control in a Predictive Driver Model

2021 ◽  
Vol 13 (4) ◽  
Author(s):  
C. Dias ◽  
J. Landre ◽  
P. Americo ◽  
M. Campolina ◽  
L. Marino Marino ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Degrees Of Freedom ◽  
Control Method ◽  
Longitudinal Velocity ◽  
Pi Controller ◽  
Driver Model ◽  
Lateral Acceleration ◽  
Steering Wheel ◽  
Reference Path ◽  
Double Lane Change

Autonomous vehicles are the future of automotive engineering and understanding how this systems work is critical. In these vehicles, controller models are usually needed to generate signals that would normally be imposed by the driver e.g., steering angles, acceleration inputs and braking commands. Intuitively, each control method utilized has its peculiarities and presents different behaviours. In such situation, this paper aims to develop an error comparison between a car displacement and its reference path due the use of two different predictive driver controllers: The proportional-integrative and the MacAdam model. For this purpose, a 14 degrees of freedom vehicle model is used with the aid of MATLAB Simulink, whereas simulations were made using the double-lane change manoeuvre, a commonly used manoeuvre to analyse the vehicle dynamics performance. At the end of this paper, lateral acceleration, displacement and steering wheel angle analysis led the conclusion that the vehicle behaviour is smoother with the use of the proportional-integrative control regardless of longitudinal velocity. Nevertheless, the trajectory error is smaller for MacAdam model than PI controller is and therefore it is easier to follow the reference path with this one, although in aggressive maneuverers it can cause more discomfort and increase the risk of rolling when compared to the PI controller in a vehicle with the same body stiffness.

Stability of Controlled Road Vehicles: A Preliminary Fundamental Study

2015 ◽  
Author(s):  
Fabio della Rossa ◽  
Massimiliano Gobbi ◽  
Giampiero Mastinu ◽  
Carlo Piccardi ◽  
Giorgio Previati
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Preliminary Investigation ◽  
Basin Of Attraction ◽  
Lateral Acceleration ◽  
Steering Wheel ◽  
Vehicle Speed ◽  
Vehicle Model ◽  
The Stability ◽  
Positive Effect

A comparison of the lateral stability behaviour between an autonomous vehicle, a vehicle with driver and a vehicle without driver (fixed steering wheel) is made by introducing a simple mathematical model of a vehicle running on even road. The mechanical model of the vehicle has two degrees of freedom and the related equations of motion contain the nonlinear tyre characteristics. The driver is described by a well-known model proposed in the literature. The autonomous vehicle has a virtual driver (robot) that behaves substantially like a human, but with its proper reaction time and gain. The road vehicle model has been validated. The study of vehicle stability has to be based on bifurcation analysis and a preliminary investigation is proposed here. The accurate computation of steady-state equilibria is crucial to study the stability of the three kinds of vehicles here compared. The stability of the bare vehicle without driver (fixed steering wheel) is studied in a rather complete way referring to a number of combinations of tyre characteristics. The (known) conclusion is that the understeering vehicle is stable at each lateral acceleration level and at each vehicle speed. The additional (partially unknown) conclusion is that the vehicle (model) with degradated tyres may exhibit a huge number of different bifurcations. The driver has many effects on the stability of the vehicle. One positive effect is to eliminate the many possible different equilibria of the bare vehicle and keep active one single equilibrium only. Another positive effect is to broaden the basin of attraction of stable equilibria (at least at relatively low speed). A negative effect is that, even for straight running, the driver seem introducing a subcritical Hopf bifurcation which limits the maximum forward speed of some understeering vehicles (that could run faster with fixed steering wheel). Both the mentioned positive and negative effects appear to be applicable to autonomous vehicles as well. Further studies could be useful to overcome the limitations on the stability of current autonomous vehicles that have been identified in the present research.

Application of Error Analysis Method in the Complex Vehicle Model

2012 ◽  
Vol 591-593 ◽  
pp. 584-587
Author(s):  
Shui Rong Liao ◽  
Tao Yang
Keyword(s):  
Error Analysis ◽  
Degrees Of Freedom ◽  
Lateral Acceleration ◽  
Steering Wheel ◽  
Model Parameters ◽  
Vehicle Model ◽  
Two Degrees Of Freedom ◽  
Input Amplitude ◽  
Sinusoidal Input ◽  
Set Up

A two degree of freedom input vehicle model is set up. Based on driver modeling analytical method of error analysis, step signal is taken as the input of steering angle to complex vehicle model based on CarSim, vehicle lateral acceleration is taken as as output. Meanwhile, the same steering wheel angle is taken as input as equivalent two degrees of freedom vehicle model, vehicle model parameters are optimized based on the minimum objective function. The results show that, in the same kind of speed, for steering wheel angle step input and sinusoidal input , when the input amplitude increases, the equivalent accuracy of the complex vehicle model and two degrees of freedom vehicle model will be reduced.

scholarly journals Integrated Avoid Collision Control of Autonomous Vehicle Based on Trajectory Re-Planning and V2V Information Interaction

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1079 ◽  
Author(s):  
Fen Lin ◽  
Kaizheng Wang ◽  
Youqun Zhao ◽  
Shaobo Wang
Keyword(s):  
Autonomous Vehicles ◽  
Control Method ◽  
Integrated Control ◽  
Autonomous Vehicle ◽  
Longitudinal Velocity ◽  
Reference Trajectory ◽  
Threshold Method ◽  
Lane Changing ◽  
Information Interaction ◽  
Fitting In

An integrated longitudinal-lateral control method is proposed for autonomous vehicle trajectory tracking and dynamic collision avoidance. A method of obstacle trajectory prediction is proposed, in which the trajectory of the obstacle is predicted and the dynamic solution of the reference trajectory is realized. Aiming at the lane changing scene of autonomous vehicles driving in the same direction and adjacent lanes, a trajectory re-planning motion controller with the penalty function is designed. The reference trajectory parameterized output of local reprogramming is realized by using the method of curve fitting. In the framework of integrated control, Fuzzy adaptive (proportional-integral) PI controller is proposed for longitudinal velocity tracking. The selection and control of controller and velocity are realized by logical threshold method; A model predictive control (MPC) with vehicle-to-vehicle (V2V) information interaction modular and the driver characteristics is proposed for direction control. According to the control target, the objective function and constraints of the controller are designed. The proposed method’s performance in different scenarios is verified by simulation. The results show that the autonomous vehicles can avoid collision and have good stability.

scholarly journals APPLICATION OF COMPUTER PROGRAM CARSIM FOR MODELLING SINGLE AND DOUBLE LANE CHANGE MANOEUVRES / TAIKOMOSIOS PROGRAMOS „CARSIM“ PRITAIKYMAS, MODELIUOJANT VIENGUBĄ IR DVIGUBĄ JUDĖJIMO JUOSTOS PAKEITIMO MANEVRĄ

2012 ◽  
Vol 4 (4) ◽  
pp. 397-402
Author(s):  
Artūras Žukas
Keyword(s):  
Computer Program ◽  
Lateral Acceleration ◽  
Steering Wheel ◽  
Lane Change ◽  
Lateral Forces ◽  
Computer Aided ◽  
Double Lane Change ◽  
Dynamical Properties ◽  
The Given

The paper analyzes the possibilities of using computer aided modelling programs for developing new cars to achieve better dynamical properties of control over vehicles. The article shortly reviews the behaviour of young and experienced drivers and models describing it. The paper covers the process of turning car steering wheel, considers acceptable values of lateral acceleration comfortable for a car driver and all car passengers and presents computer aided modelling program CarSim used for displaying single and double lane change manoeuvres at various speeds on dry asphalt. The given charts, including data about steering wheel angle and lateral acceleration values indicate single and double lane change manoeuvres performed by a car. Also, the values of longitudinal and lateral forces of each wheel during the double lane change manoeuvre are provided. Santrauka Straipsnyje nagrinėjamos kompiuterinių modeliavimo programų pritaikymo galimybės automobilių konstrukcijoms tobulinti, dinaminėms savybėms gerinti. Trumpai minimi vairuotojo elgseną aprašantys mokslininkų jau sukurti modeliai. Aptariamas vairo rato pasukimo procesas, pateikiamos priimtinos vairuotojui ir automobilio keleiviams nesudarančios diskomforto skersinio pagreičio reikšmės. Atliekama trumpa taikomosios kompiuterinio modeliavimo programos „CarSim“ apžvalga. Šia programa atlikti viengubo ir dvigubo judėjimo juostos pakeitimo manevrai, esant skirtingiems automobilio judėjimo greičiams ant sauso asfalto. Pateikti vairo rato pasukimo, skersinių pagreičių reikšmių grafikai. Taip pat pateiktos visus ratus veikiančių išilginių ir skersinių jėgų bei ratų slydimo reikšmės esant 90 km/h greičiui.

Countersteering to Recover Straight Ahead Running After a Disturbance

2016 ◽  
Author(s):  
Fabio della Rossa ◽  
Massimiliano Gobbi ◽  
Giampiero Mastinu ◽  
Giorgio Previati
Keyword(s):  
Degrees Of Freedom ◽  
Rear Axle ◽  
External Disturbance ◽  
Equations Of Motion ◽  
Driver Model ◽  
Steering Wheel ◽  
Non Linear ◽  
Oscillating Motion ◽  
Straight Ahead ◽  
Lateral Excitation

The paper deals with the analysis of a manoeuvre occurring frequently before crashes. Due to an external disturbance the straight ahead running of a vehicle is degradated into an oscillating motion. The driver is required to countersteer to recover the straight ahead motion. The bifurcation analysis of a simple model describing a vehicle+driver running straight ahead is performed. The mechanical model of the car has two degrees of freedom and the related equations of motion contain the non linear tyre characteristics. The driver is described by a non linear model defined by three parameters, namely the gain (steering wheel angle per lateral deviation from desired path), the prevision distance, the reaction time delay. Unreferenced bifurcations are discovered for the understeering vehicle. A supercritical Hopf bifurcation may occur as forward speed is increased. Also tangent (fold) bifurcations occur as the speed (or disturbance) are further increased. The vehicle+driver model is validated by means of a number of tests performed in a track. The validation relies on the identification of driver’s parameters. The track is equipped with a plank sliding laterally when the vehicle rear axle passes on it. Such a lateral excitation applies a disturbance to the vehicle which initiates a spin to be counteracted by the driver. An analysis is performed on driver’s parameters identification. Such parameter identification seems a possible way to assess single driver’s ability to perform recovery manoeuvres.

A new vehicle path-following strategy of the steering driver model using general predictive control method

2016 ◽  
Vol 232 (24) ◽  
pp. 4578-4587 ◽  
Author(s):  
Yang Cao ◽  
Jianyong Cao ◽  
Fan Yu ◽  
Zhe Luo
Keyword(s):  
Predictive Control ◽  
Control Method ◽  
Path Following ◽  
Least Square ◽  
Driver Model ◽  
Steering Wheel ◽  
Active Steering ◽  
Predictive Controller ◽  
Vehicle Path ◽  
General Predictive Control

In order to build an accurate and effective model, simulation of driver behavior is absolutely essential for the development of advanced driver assistance systems and the current assessment of vehicle handling stability. The purpose of the proposed active steering control is to assist the driver to follow the desired path, especially in situations of the vehicle under strong external disturbance, distracted driver, or other unforeseen circumstances that can cause deviations. Based on the preview optimal simple artificial neural network driver model, an active steering system using general predictive control method is established. In order to improve the path-following capability of vehicles under disturbances, a general predictive controller, with the deviation between the vehicles’ actual and desired lateral positons as inputs and with the corrective steering wheel angle as outputs, is developed to follow the desired path. Meanwhile, adapting recursion least square method with the forgetting factor to estimate the parameters of the controlled auto-regressive and integrated moving average model of the vehicle is designed. The proposed vehicle path-following control system is evaluated in some typical conditions (e.g. under strong crosswind condition in standard double-lane-change, etc.). Simulation results and analysis have verified that this new vehicle path-following strategy, given by general predictive controller, is capable of capturing driver’s steering behavior and the amended driver steering angle can improve the dynamic performance of vehicle under some external disturbances.

scholarly journals Modeling and simulation of minimum time-handling inverse dynamics of a vehicle

2018 ◽  
Vol 10 (7) ◽  
pp. 168781401878608 ◽  
Author(s):  
Li-Xia Zhang ◽  
Fu-Quan Pan ◽  
Hui Zhang ◽  
Ting Feng
Keyword(s):  
Degrees Of Freedom ◽  
Load Transfer ◽  
Inverse Dynamics ◽  
Longitudinal Velocity ◽  
Minimum Time ◽  
Multiple Shooting ◽  
Steering Wheel ◽  
Dynamics Model ◽  
Vehicle Handling ◽  
Simulation Results

The performance of a vehicle in minimum time handling is highly important for the safety of the vehicle. In this study, a vehicle motion state equation with 3 degrees of freedom was established on the basis of the lateral, yaw, and longitudinal motions of the vehicle. Equations on the linear tire and motion trajectory were established with consideration of longitudinal load transfer to establish the vehicle-handling dynamics model. Steering-wheel angle, driving force equation set, and yaw angle equation had been introduced to convert the vehicle-handling dynamics model into the vehicle-handling inverse dynamics model. By introducing performance index, control set, and several constraint conditions, an optimal control model of the vehicle minimum time handling was established, which was solved by improved direct multiple-shooting nonlinear programming method. A comparison of the simulation results of ADAMS/Car and MATLAB showed that both of the optimal routes input were in tangent with the road boundary. We can observe through the longitudinal velocity that the MATLAB simulation results are more similar to a straight line than that of the ADAMS/Car simulation results, which meet the psychological expectation of a driver. Thus, the inverse dynamics model on minimum time handling of the vehicle is reasonable and feasible.

scholarly journals Robust trajectory tracking control for autonomous vehicle subject to velocity-varying and uncertain lateral disturbance

2021 ◽  
Vol 57 (1) ◽  
pp. 7-23
Author(s):  
Yuqiong Wang ◽  
Song Gao ◽  
Yuhai Wang ◽  
Pengwei Wang ◽  
Yingchao Zhou ◽  
...  
Keyword(s):  
Trajectory Tracking ◽  
Autonomous Vehicles ◽  
Tracking Control ◽  
Autonomous Vehicle ◽  
Longitudinal Velocity ◽  
Lane Change ◽  
Trajectory Tracking Control ◽  
Velocity Planning ◽  
Double Lane Change ◽  
Lateral Disturbance

Autonomous vehicles are the most advanced intelligent vehicles and will play an important role in reducing traffic accidents, saving energy and reducing emission. Motion control for trajectory tracking is one of the core issues in the field of autonomous vehicle research. According to the characteristics of strong nonlinearity, uncertainty and chang-ing longitudinal velocity for autonomous vehicles at high speed steering condition, the robust trajectory tracking control is studied. Firstly, the vehicle system models are established and the novel target longitudinal velocity planning is carried out. This velocity planning method can not only ensure that the autonomous vehicle operates in a strong nonlinear coupling state in bend, but also easy to be constructed. Then, taking the lateral location deviation minimiz-ing to zero as the lateral control objective, a robust active disturbance rejection control path tracking controller is designed along with an extended state observer which can deal with the varying velocity and uncertain lateral dis-turbance effectively. Additionally, the feedforward-feedback control method is adopted to control the total tire torque, which is distributed according to the steering characteristics of the vehicle for additional yaw moment to enhance vehicle handing stability. Finally, the robustness of the proposed controller is evaluated under velocity-varying condi-tion and sudden lateral disturbance. The single-lane change maneuver and double-lane change maneuver under vary longitudinal velocity and different road adhesions are both simulated. The simulation results based on Matlab/Simulink show that the proposed controller can accurately observe the external disturbances and have good performance in trajectory tracking and handing stability. The maximum lateral error reduces by 0.18 meters compared with a vehicle that controlled by a feedback-feedforward path tracking controller in the single-lane change maneuver. The lateral deviation is still very small even in the double lane change case of abrupt curvature. It should be noted that our proposed control algorithm is simple and robust, thus provide great potential for engineering application.

scholarly journals A Model Predictive Control with Preview-Follower Theory Algorithm for Trajectory Tracking Control in Autonomous Vehicles

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 381
Author(s):  
Ying Xu ◽  
Wentao Tang ◽  
Biyun Chen ◽  
Li Qiu ◽  
Rong Yang
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Trajectory Tracking ◽  
Autonomous Vehicles ◽  
Linear Time ◽  
Effective Length ◽  
Lateral Acceleration ◽  
Trajectory Tracking Control ◽  
Yaw Rate ◽  
Reference Path

Research on trajectory tracking is crucial for the development of autonomous vehicles. This paper presents a trajectory tracking scheme by utilizing model predictive control (MPC) and preview-follower theory (PFT), which includes a reference generation module and a MPC controller. The reference generation module could calculate reference lateral acceleration at the preview point by PFT to update state variables, and generate a reference yaw rate in each prediction point. Since the preview range is increased, PFT makes the calculation of yaw rate more accurate. Through physical constraints, the MPC controller can achieve the best tracking of the reference path. The MPC problem is formulated as a linear time-varying (LTV) MPC controller to achieve a predictive model from nonlinear vehicle dynamics to continuous online linearization. The MPC-PFT controller method performs well by increasing the effective length of the reference path. Compared with MPC and PFT controllers, the effectiveness and robustness of the proposed method are proved by simulations of two typical working conditions.

scholarly journals Establishment and tracking control of trapezoidal steering wheel angle model for autonomous vehicles

2020 ◽  
Vol 17 (6) ◽  
pp. 172988142098278
Author(s):  
Haobin Jiang ◽  
Aoxue Li ◽  
Xinchen Zhou ◽  
Yue Yu
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Steering System ◽  
Free Play ◽  
Driver Model ◽  
Steering Wheel ◽  
Feature Points ◽  
Steering Control ◽  
Power Steering ◽  
Nonlinear Vehicle Model

Human drivers have rich and diverse driving characteristics on curved roads. Finding the characteristic quantities of the experienced drivers during curve driving and applying them to the steering control of autonomous vehicles is the research goal of this article. We first recruited 10 taxi drivers, 5 bus drivers, and 5 driving instructors as the representatives of experienced drivers and conducted a real car field experiment on six curves with different lengths and curvatures. After processing the collected driving data in the Frenet frame and considering the free play of a real car’s steering system, it was interesting to observe that the shape enclosed by steering wheel angles and the coordinate axis was a trapezoid. Then, we defined four feature points, four feature distances, and one feature steering wheel angle, and the trapezoidal steering wheel angle (TSWA) model was developed by backpropagation neural network with the inputs were vehicle speeds at four feature points, and road curvature and the outputs were feature distances and feature steering wheel angle. The comparisons between TSWA model and experienced drivers, model predictive control, and preview-based driver model showed that the proposed TSWA model can best reflect the steering features of experienced drivers. What is more, the concise expression and human-like characteristic of TSWA model make it easy to realize human-like steering control for autonomous vehicles. Lastly, an autonomous vehicle composed of a nonlinear vehicle model and electric power steering (EPS) system was established in Simulink, the steering wheel angles generated by TSWA model were tracked by EPS motor directly, and the results showed that the EPS system can track the steering angles with high accuracy at different vehicle speeds.

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