Intermittent Predictive Steering Control as an Automobile Driver Model

2008 ◽  
Vol 131 (1) ◽  
Author(s):  
Rene Roy ◽  
Philippe Micheau ◽  
Paul Bourassa
Keyword(s):  
Control Algorithm ◽  
Driver Model ◽  
Steering Wheel ◽  
Intermittent Control ◽  
Steering Control ◽  
Period Range ◽  
The Road ◽  
Wide Range ◽  
Safety Systems ◽  
General Predictive Control

The originality of this paper is the evaluation of intermittent control as a viable candidate to represent an automobile driver in a path tracking scenario. The control algorithm is based on general predictive control where the road curvature is considered known for a horizon in front of the automobile. The computed steering wheel command is used in an intermittent fashion, the intermittence period being one of the system parameter to study. Simulations are carried out and parameters of the driver, the automobile, and the road are varied. An intermittence period range giving satisfactory performances is observed. A comparison is made with actual car/driver behavior measurements for a lane change maneuver. It is concluded that, according to this driver model, there is a wide range of intermittence period that the automobile driver may be operating. Moreover, it is suggested to consider the intermittency of information as an important parameter for vehicle safety systems.

scholarly journals Model predictive control–based steering control algorithm for steering efficiency of a human driver in all-terrain cranes

2019 ◽  
Vol 11 (6) ◽  
pp. 168781401985978
Author(s):  
Ja-Ho Seo ◽  
Kwang-Seok Oh ◽  
Hong-Jun Noh
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Algorithm ◽  
Steering System ◽  
Driver Model ◽  
Control Technique ◽  
Change Scenario ◽  
Steering Control ◽  
Human Driver ◽  
Steering Effort

All-terrain cranes with multi-axles have large inertia and long distances between the axles that lead to a slower dynamic response than normal vehicles. This has a significant effect on the dynamic behavior and steering performance of the crane. Therefore, the purpose of this study is to develop an optimal steering control algorithm with a reduced driver steering effort for an all-terrain crane and to evaluate the performance of the algorithm. For this, a model predictive control technique was applied to an all-terrain crane, and a steering control algorithm for the crane was proposed that could reduce the driver’s steering effort. The steering performances of the existing steering system and the steering system applied with the newly developed algorithm were compared using MATLAB/Simulink and ADAMS with a human driver model for reasonable performance evaluation. The simulation was performed with both a double lane change scenario and a curved-path scenario that are expected to happen in road-steering mode.

A New Control Strategy to Reduce Steering Torque Without Perceptible Vibration for Vehicles Equipped With Electric Power Steering

2010 ◽  
Vol 132 (5) ◽  
Author(s):  
Masahiko Kurishige ◽  
Osamu Nishihara ◽  
Hiromitsu Kumamoto
Keyword(s):  
Angular Velocity ◽  
Electric Power ◽  
Control Strategy ◽  
Steering Wheel ◽  
Velocity Control ◽  
Steering Control ◽  
Electric Power Steering ◽  
Power Steering ◽  
Wide Range ◽  
Steering Torque

This paper proposes a new electric power steering control strategy, which significantly reduces the effort needed to change the steering direction of stationary vehicles. Previous attempts to reduce undesirable steering vibration have failed to reduce the steering torque because high-assist gains tend to produce oscillation or increase noise sensitivity. Herein, to eliminate this vibration, a new control strategy was developed based on pinion angular velocity control using a newly developed observer based on a simplified steering model. Tests yielded excellent estimations of the pinion angular velocity, and this made it possible to eliminate vibration at all steering wheel rotation speeds. Experiments with a test vehicle confirmed significant steering torque reduction, over a wide range of steering wheel speeds, without vibration transmission to the driver. The proposed control strategy allowed use of an assist gain more than three times higher than is conventional. Additionally, the proposed control strategy does not require supplemental sensors.

Decision Analysis of Lane Change Based on Fuzzy Logic

2013 ◽  
Vol 419 ◽  
pp. 790-794 ◽  
Author(s):  
Wen Shi ◽  
Ya Ping Zhang
Keyword(s):  
Fuzzy Logic ◽  
Change Process ◽  
Road Surface ◽  
Driver Model ◽  
Steering Wheel ◽  
Surface Model ◽  
Lane Change ◽  
The Road ◽  
Fuzzy Logic Analysis ◽  
Test Result

Aiming at the complexity of lane change process, fuzzy logic analysis method was proposed to analyzing this behavior. By assaying the multi lane change scene that the drivers may choose, influencing factors were quantified. Each indicator factor after quantified was treated as model input. PID models of driver, vehicle and road surface were established in Simulink condition. The road surface model controls whether the lane change process will be conducted, and the driver model will export angle of steering wheel to deciding the efficiency of lane change process. Real road test was conducted and the test result shows that information between human and vehicle can be fused sufficiently.

scholarly journals Modeling and Recognizing Driver Behavior Based on Driving Data: A Survey

2014 ◽  
Vol 2014 ◽  
pp. 1-20 ◽  
Author(s):  
Wenshuo Wang ◽  
Junqiang Xi ◽  
Huiyan Chen
Keyword(s):  
Driver Behavior ◽  
Control Point ◽  
Point Of View ◽  
Driver Model ◽  
Transport Systems ◽  
Steering Wheel ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Wide Range ◽  
Vehicle Systems

In recent years, modeling and recognizing driver behavior have become crucial to understanding intelligence transport systems, human-vehicle systems, and intelligent vehicle systems. A wide range of both mathematical identification methods and modeling methods of driver behavior are presented from the control point of view in this paper based on the driving data, such as the brake/throttle pedal position and the steering wheel angle, among others. Subsequently, the driver’s characteristics derived from the driver model are embedded into the advanced driver assistance systems, and the evaluation and verification of vehicle systems based on the driver model are described.

Artificial Intelligence Control Algorithm for the Steering Motion of Wheeled Soccer Robot

2019 ◽  
Vol 33 (10) ◽  
pp. 1959034 ◽  
Author(s):  
Xiaowei Xiong
Keyword(s):  
Artificial Intelligence ◽  
Control Algorithm ◽  
Steering Wheel ◽  
Dynamic Feedback ◽  
Steering Control ◽  
Soccer Robot ◽  
Artificial Neurons ◽  
Intelligence Control ◽  
Intelligent Model ◽  
Soccer Robots

In this paper, the artificial intelligence control algorithm for steering robot of steering wheel is studied. The steering movement of wheeled soccer robot is controlled by artificial intelligence control algorithm, and the steering movement is modeled and simulated. Firstly, the characteristics of artificial neurons are simulated and a similar control model is constructed to complete the simulation of football. The artificial intelligence control algorithm has a dynamic feedback item compared with the traditional intelligent model, which has a better effect on the steering control of the wheeled soccer robot. In this paper, artificial intelligence control algorithm is used to optimize the parameters of artificial intelligence control algorithm, and the output of control signal of each steering part of wheeled soccer robot is simulated in the experiment, and the control of the steering action of wheeled soccer robot by artificial intelligence control algorithm is verified by experiments. Then the artificial intelligence control algorithm forms the connection structure. This method provides a good reference for steering control of wheeled soccer robots.

The Active Front Steering Control Strategy Based on Driver Behavior

2013 ◽  
Vol 373-375 ◽  
pp. 1277-1282
Author(s):  
Jian Zhao ◽  
Yun Fu Su ◽  
Bing Zhu ◽  
Peng Fei Wang
Keyword(s):  
Driver Behavior ◽  
Driver Model ◽  
Steering Wheel ◽  
Steering Control ◽  
Active Steering ◽  
Active Front Steering ◽  
Reference Vehicle ◽  
The Stability ◽  
Prediction Control ◽  
Two Degree Of Freedom

Active Front Steering (AFS) is an important application to improve the stability of the vehicle, and the driver characteristic is also an important factor for the vehicle stability. In this article, a driver-behavior-based prediction control algorithm for AFS is proposed. According to the informed road trajectory, the ideal preview driver model is introduced to predict the future steering wheel angle. Based on this, a two-degree-of-freedom (2DOF) reference vehicle model and a PID controller are used to generate active steering control. The algorithm is verified by Carsim and Matlab/Simulink co-simulation, and the results show that trajectory tracking of the vehicle can be guarantee and driver manipulation duty can be reduced.

A Mathematical Model of Driver Steering Control Including Neuromuscular Dynamics

2008 ◽  
Vol 130 (3) ◽  
Author(s):  
Andrew J. Pick ◽  
David J. Cole
Keyword(s):  
Path Following ◽  
Linear Quadratic Regulator ◽  
Driver Model ◽  
Steering Wheel ◽  
Steering Control ◽  
Linear Quadratic ◽  
Reflex Action ◽  
Path Following Control ◽  
Steering Torque ◽  
Neuromuscular Dynamics

A mathematical driver model is introduced in order to explain the driver steering behavior observed during successive double lane-change maneuvers. The model consists of a linear quadratic regulator path-following controller coupled to a neuromuscular system (NMS). The NMS generates the steering wheel angle demanded by the path-following controller. The model demonstrates that reflex action and muscle cocontraction improve the steer angle control and thus increase the path-following accuracy. Muscle cocontraction does not have the destabilizing effect of reflex action, but there is an energy cost. A cost function is used to calculate optimum values of cocontraction that are similar to those observed in the experiments. The observed reduction in cocontraction with experience of the vehicle is explained by the driver learning to predict the steering torque feedback. The observed robustness of the path-following control to unexpected changes in steering torque feedback arises from the reflex action and cocontraction stiffness of the NMS. The findings contribute to the understanding of driver-vehicle dynamic interaction. Further work is planned to improve the model; the aim is to enable the optimum design of steering feedback early in the vehicle development process.

scholarly journals Research on Control of Intelligent Vehicle Human-Simulated Steering System Based on HSIC

2019 ◽  
Vol 9 (5) ◽  
pp. 905 ◽  
Author(s):  
Haobin Jiang ◽  
Huan Tian ◽  
Yiding Hua ◽  
Bin Tang
Keyword(s):  
Intelligent Control ◽  
Control Algorithm ◽  
Rapid Prototype ◽  
Steering System ◽  
Steering Wheel ◽  
Intelligent Vehicle ◽  
System Control ◽  
Steering Control ◽  
Nonlinear Fitting ◽  
Resistance Torque

The experienced drivers with good driving skills are used as objects of learning, and road steering test data of skilled drivers are collected in this article. First, a nonlinear fitting was made to the driving trajectory of skilled driver in order to achieve human-simulated control. The segmental polynomial expression was solved for two typical steering conditions of normal right-steering and U-turn, and the hp adaptive pseudo-spectral method was used to solve the connection problem of the vehicle segmental driving trajectory. Second, a new Electric Power Steering (EPS) system was proposed, and the intelligent vehicle human-simulated steering system control model based on human simulated intelligent control (HSIC) was established in Simulink/Carsim joint simulation environment to simulate and analyze. Finally, in order to further verify the effectiveness of the proposed algorithm in this article, an intelligent vehicle steering system test bench with a steering resistance torque simulation device was built, and the dSPACE rapid prototype controller was used to realize human-simulated intelligent control law. The results show that the human-simulated steering control algorithm is superior to the traditional proportion integration differentiation (PID) control in the tracking effect of the steering characteristic parameters and passenger comfort. The steering wheel angle and torque can better track the angle and torque variation curve of real vehicle steering experiment of the skilled driver, and the effectiveness of the intelligent vehicle human-simulated steering control algorithm based on HSIC proposed in this article is verified.

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.

Research on Fidelity of Driving Simulators

2011 ◽  
Vol 308-310 ◽  
pp. 1880-1884 ◽  
Author(s):  
Pei Xin Li ◽  
Yan Ding Wei ◽  
Xiao Jun Zhou ◽  
Chun Yu Wei ◽  
Ming Xiang Xie ◽  
...  
Keyword(s):  
Control Algorithm ◽  
Driving Simulator ◽  
Steering Wheel ◽  
Dynamics Modeling ◽  
Driving Simulators ◽  
Factors Affecting ◽  
The Road ◽  
Body Dynamics ◽  
Wheel Torque ◽  
Multi Body

Through analyzing the specialty and limitation of the current driving simulators, the main factors affecting fidelity of driving simulators are summarized. Then, a new driving simulator of high fidelity based on the multi-body dynamics is proposed, with focus on the dynamics modeling and the road feel. Furthermore, a control algorithm of the road feel is designed and by the means of co-simulations in MATLAB/Simulink and ADAMS environment, the measuring steering wheel torque proves the control algorithm of road feel is reasonable. The control algorithm has been put into practice and got satisfactory results.

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