scholarly journals Nonlinear Steering Wheel Angle Control Using Self-Aligning Torque with Torque and Angle Sensors for Electrical Power Steering of Lateral Control System in Autonomous Vehicles

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4384 ◽  
Author(s):  
Wonhee Kim ◽  
Chang Kang ◽  
Young-Seop Son ◽  
Chung Chung
Keyword(s):  
Control System ◽  
Autonomous Vehicles ◽  
Tracking Error ◽  
The Self ◽  
Steering Wheel ◽  
Measured Signal ◽  
Control Input ◽  
Nonlinear Controller ◽  
Lateral Control ◽  
Power Steering

The development of sensor technology enabled the use of composite sensors to measure the torque and angle of steering wheels at gradually decreasing costs while maintaining the required safety. The electric power steering (EPS) is vital to the safety of the car, therefore it is not worth sacrificing safety to save cost and the SWA control with angle sensor gradually becomes the mainstream. Existing methods to control steering wheel angle (SWA) for EPS consider the self-aligning torque as a disturbance that should be rejected. However, this torque is useful to return the SWA from an outward to the center position. Hence, we propose a nonlinear control of SWA using the self-aligning torque for EPS in the lateral control system of autonomous vehicles. The proposed method consists of a high-gain disturbance observer and a backstepping controller, where the former aims to estimate the self-aligning torque, and an auxiliary state variable prevents using the derivative of the measured signal. The nonlinear controller is designed via backstepping to bound the SWA tracking error. The self-aligning torque provides damping that can improve the controller tracking when following the same direction of the input torque on the steering wheel control. In this case, the control input can be reduced by the damping effect of the self-aligning torque. The performance of the proposed method is validated through EPS hardware-in-the-loop simulation.

Synthesis, Stability Analysis, and Experimental Implementation of a Multirate Repetitive Learning Controller

2002 ◽  
Vol 124 (4) ◽  
pp. 668-674 ◽  
Author(s):  
Nader Sadegh ◽  
Ai-Ping Hu ◽  
Courtney James
Keyword(s):  
Control System ◽  
Stability Analysis ◽  
Tracking Error ◽  
Sampling Rate ◽  
Weighted Averaging ◽  
System Control ◽  
Control Input ◽  
Experimental Implementation ◽  
Repetitive Learning ◽  
Averaging Filter

This paper describes a multirate repetitive learning controller with an adjustable sampling rate that may be used as an “add-on” module to enhance the tracking performance of a feedback control system. The sampling rate of the multirate controller is slower than the remainder of the control system, and is selected by the user to achieve the required system performance based on a trade-off between the accuracy and the complexity of the controller. The multirate controller learns the system control input based on the tracking error down-sampled using a weighted averaging filter. The output of the multirate controller is up-sampled through an arbitrary hold mechanism determined by the user. This paper extends the existing stability results for single-rate repetitive learning controllers to the proposed multirate scheme. It provides an explicit procedure for its design and stability analysis. In addition, the proposed multirate repetitive learning controller is implemented on a mechanical system performing a non-colocated control task, where its effectiveness in reducing tracking errors while following periodic reference trajectories is shown experimentally.

scholarly journals Robust Prescribed Performance Control of Uncertain Vehicle Lateral System under State Constraints

2021 ◽  
Vol 2083 (3) ◽  
pp. 032029
Author(s):  
Jing Yu
Keyword(s):  
Neural Network ◽  
Control System ◽  
Lyapunov Function ◽  
Finite Time ◽  
Tracking Control ◽  
State Constraints ◽  
Tracking Error ◽  
Lateral Control ◽  
Barrier Lyapunov Function ◽  
The Neural Network

Abstract In the study of the zero-error tracking control problem for vehicle lateral control systems under full-state constraints and nonparametric uncertainties, the zero-error tracking control problem is presented in this paper. A neural adaptive tracking control scheme is proposed by combining the error transformation of the vehicle lateral control system with the barrier Lyapunov function, which realizes that the tracking error of the vehicle lateral control converges to a prescribed compact set at a controllable or specified convergence rate in a specified finite time. The scheme has the following significant characteristics: 1) Based on the Nussbaum gain, the preset new energy finite-time control algorithm, the tracking error of the vehicle lateral control system with non-parametric uncertainty and external disturbance decreases to zero with t → ∞. In addition, it also has the control ability to cope with the presence or even unknown moment of inertia of the system. 2) Barrier Lyapunov function (BLF) ensures the bounded input of the neural network during the whole system envelope, and ensures the stable learning and approximation of the neural network. Furthermore, the bounded stability of the closed-loop system is proved by Lyapunov analysis. Finally, the effectiveness and superiority of the proposed control method are verified by simulation.

scholarly journals Intelligent Vehicle Lateral Control Method Based on Feedforward + Predictive LQR Algorithm

Actuators ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 228
Author(s):  
Tao Yang ◽  
Ziwen Bai ◽  
Zhiqiang Li ◽  
Nenglian Feng ◽  
Liqing Chen
Keyword(s):  
Control System ◽  
Vehicle Dynamics ◽  
Error Control ◽  
Control Method ◽  
Tracking Error ◽  
Error Model ◽  
Intelligent Vehicle ◽  
Lateral Control ◽  
Perception System ◽  
Lqr Controller

Aiming at the problems of control stability of the intelligent vehicle lateral control method, single test conditions, etc., a lateral control method with feedforward + predictive LQR is proposed, which can better adapt to the problem of intelligent vehicle lateral tracking control under complex working conditions. Firstly, the vehicle dynamics tracking error model is built by using the two degree of freedom vehicle dynamics model, then the feedforward controller, predictive controller and LQR controller are designed separately based on the path tracking error model, and the lateral control system is built. Secondly, based on the YOLO-v3 algorithm, the environment perception system under the urban roads is established, and the road information is collected, the path equation is fitted and sent to the control system. Finally, the joint simulation is carried out based on CarSim software and a Matlab/Simulink control model, and tested combined with hardware in the loop test platform. The results of simulation and hardware-in-loop test show that the transverse controller with feedforward + predictive LQR can effectively improve the accuracy of distance error control and course error control compared with the transverse controller with feedforward + LQR control, LQR controller and MPC controller on the premise that the vehicle can track the path in real time.

scholarly journals The Self-Driving Car: A Media Machine for Posthumans?

Artnodes ◽  
2020 ◽  
Author(s):  
Erkki Huhtamo
Keyword(s):  
Autonomous Vehicles ◽  
Active Role ◽  
The Self ◽  
Media Culture ◽  
Steering Wheel ◽  
Data Networks ◽  
Automated Highways ◽  
Car Culture ◽  
Multiple Levels ◽  
Self Driving Cars

This article discusses the self-driving car as a media machine, thinking about its character and broader implications from media archaeological and posthumanist perspectives. Self-driving or autonomous vehicles challenge traditional ideas about agency. Car culture has usually been considered human-centered. While there have been concerns about the “human factor” and the consequences of poor and distracted driving, the human behind the steering wheel has also been considered a guarantee of safety. The introduction of the self-driving car displaces the human from an active role as an agent and introduces forms of material agency as a replacement. This shift has huge consequences, which will be explored from various perspectives. The study will also situate the self-driving car historically within plans about automated highways, also discussing their discursive manifestations within popular media culture. The study introduces the idea of “traffic dispositive”, which it applies on multiple levels. One of the basic points underlying the discussion is that the autonomous car can never be fully autonomous. It is linked with data networks and other frameworks of factors that affect its uses and also its potential passengers. We must ask: How will the potential adoption of self-driving cars affect the human/posthuman relationship?

scholarly journals Control System Design for a Ducted-Fan Unmanned Aerial Vehicle Using Linear Quadratic Tracker

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Junho Jeong ◽  
Seungkeun Kim ◽  
Jinyoung Suk
Keyword(s):  
Control System ◽  
Steady State ◽  
Unmanned Aerial Vehicle ◽  
Tracking Error ◽  
Control Input ◽  
System Matrix ◽  
Linear Quadratic ◽  
Ducted Fan ◽  
Navigation Simulation ◽  
Aerial Vehicle

Tracking control system based on linear quadratic (LQ) tracker is designed for a ducted-fan unmanned aerial vehicle (UAV) under full flight envelope including hover, transition, and cruise modes. To design the LQ tracker, a system matrix is augmented with a tracking error term. Then the control input can be calculated to solve a single Riccati equation, but the steady-state errors might still remain in this control system. In order to reduce the steady-state errors, a linear quadratic tracker with integrator (LQTI) is designed to add an integral term of tracking state in the state vector. Then the performance of the proposed controller is verified through waypoint navigation simulation under wind disturbance.

A Supervisory Lateral Slip Prevention Controller for Autonomous Vehicles

2019 ◽  
Author(s):  
Nikunj Kumbhani ◽  
Saeid Bashash
Keyword(s):  
Control System ◽  
Autonomous Vehicles ◽  
Feedback Linearization ◽  
Integrated Control ◽  
Linearization Method ◽  
Steering Wheel ◽  
Vehicle Speed ◽  
Supervisory Controller ◽  
Simulated Environment ◽  
Predictive Controller

Abstract This paper analyzes the behavior of autonomous vehicles in simulated environment and develops an integrated control system for maneuvering and slip prevention in curvy roads. First, a longitudinal and lateral control system is designed for the vehicle using the feedback linearization method. The longitudinal controller enforces tracking the desired velocity, while the lateral controller steers the vehicle toward, and maintains it on a desired lane. A two-level supervisory controller is then developed to prevent lateral slip while driving on curvy roads. On the lower level, the steering wheel angle is actively limited based on the vehicle speed to avoid oversteering. On the supervisory level, a predictive controller is integrated into the system to optimally slow down the vehicle ahead of a detected road curvature. Simulation results indicate the effectiveness of the proposed schemes in maintaining desirable maneuvering conditions and preventing lateral slips.

Applied nonlinear control of vehicle stability with control and state constraints

2019 ◽  
Vol 234 (1) ◽  
pp. 191-211 ◽  
Author(s):  
Amin Tahouni ◽  
Mehdi Mirzaei ◽  
Behrouz Najjari
Keyword(s):  
Control System ◽  
State Constraints ◽  
Tracking Error ◽  
Current Control ◽  
Control Signal ◽  
Slip Angle ◽  
Control Input ◽  
Yaw Rate ◽  
Prediction Approach ◽  
Yaw Moment

For the vehicle dynamic control system, to guarantee directional stability in risky maneuvers, the side-slip angle should be restricted to the admissible range when the yaw rate tracks the proposed desired response for enhanced steerability. Meanwhile, the control input of the external yaw moment produced by asymmetric braking forces should be calculated in the practical range according to the capacity of tire forces. In the present study, a novel constrained controller with input and state constraints is developed. To this aim, a cost function consisting of predicted continuous response of yaw rate tracking error is expanded in terms of current control signal. Concurrently, the state constraint of side slip is transformed to the equivalent constraint of control signal by a novel nonlinear prediction approach. After that, the expanded performance index is analytically minimized in the presence of all input constraints to obtain the control law. The computed yaw moment is optimally distributed to asymmetric braking forces by designing a wheel slip control system. Simulation studies are conducted to evaluate the performance of proposed constrained controller compared with the unconstrained controller and a conventional nonlinear model predictive controller developed in the recent papers using a 14-degree-of-freedom vehicle model which includes suspension system dynamics. The results show that the proposed controller is much faster and easy to solve and implement.

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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