A Novel Fuzzy Observer-Based Steering Control Approach for Path Tracking in Autonomous Vehicles

2018 ◽  
pp. 1-1 ◽  
Author(s):  
Changzhu Zhang ◽  
Jinfei Hu ◽  
Jianbin Qiu ◽  
Weilin Yang ◽  
Hong Sun ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Path Tracking ◽  
Steering Control ◽  
Control Approach ◽  
Fuzzy Observer

Deep reinforcement learning based path tracking controller for autonomous vehicle

2020 ◽  
pp. 095440702095459
Author(s):  
I-Ming Chen ◽  
Ching-Yao Chan
Keyword(s):  
Reinforcement Learning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Path Tracking ◽  
Steering Control ◽  
Alternative Approach ◽  
Control Command ◽  
Pure Pursuit ◽  
Driving Condition ◽  
Policy Optimization

Path tracking is an essential task for autonomous vehicles (AV), for which controllers are designed to issue commands so that the AV will follow the planned path properly to ensure operational safety, comfort, and efficiency. While solving the time-varying nonlinear vehicle dynamic problem is still challenging today, deep neural network (NN) methods, with their capability to deal with nonlinear systems, provide an alternative approach to tackle the difficulties. This study explores the potential of using deep reinforcement learning (DRL) for vehicle control and applies it to the path tracking task. In this study, proximal policy optimization (PPO) is selected as the DRL algorithm and is combined with the conventional pure pursuit (PP) method to structure the vehicle controller architecture. The PP method is used to generate a baseline steering control command, and the PPO is used to derive a correction command to mitigate the inaccuracy associated with the baseline from PP. The blend of the two controllers makes the overall operation more robust and adaptive and attains the optimality to improve tracking performance. In this paper, the structure, settings and training process of the PPO are described. Simulation experiments are carried out based on the proposed methodology, and the results show that the path tracking capability in a low-speed driving condition is significantly enhanced.

scholarly journals Preview Control with Dynamic Constraints for Autonomous Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5155
Author(s):  
Rui Li ◽  
Qi Ouyang ◽  
Yue Cui ◽  
Yang Jin
Keyword(s):  
Autonomous Vehicles ◽  
Lyapunov Theory ◽  
System Stability ◽  
System Response ◽  
Control Law ◽  
Steering Control ◽  
Tracking Accuracy ◽  
Closed Loop System ◽  
Control Approach ◽  
Dynamic Constraints

In this paper, a preview theory-based steering control approach considering vehicle dynamic constraints is presented. The constrained variables are predicted by an error states system and utilized to adjust the control law once the established dynamic constraints are violated. The simulated annealing optimization algorithm for preview length is conducted to improve the adaptability of the controller to varying velocities and road adhesion coefficients. The theoretical stability of a closed-loop system is guaranteed using Lyapunov theory, and further analysis of the system response in time domain and frequency domain is discussed. The results of simulations implemented on Carsim–Simulink demonstrate the favorable performance of the proposed control in tracking accuracy and system stability under extreme conditions.

scholarly journals A path-tracking algorithm using predictive Stanley lateral controller

2020 ◽  
Vol 17 (6) ◽  
pp. 172988142097485
Author(s):  
Ahmed AbdElmoniem ◽  
Ahmed Osama ◽  
Mohamed Abdelaziz ◽  
Shady A Maged
Keyword(s):  
Autonomous Vehicles ◽  
Controller Design ◽  
Nonholonomic Constraints ◽  
Path Tracking ◽  
Control Approach ◽  
Current Controller ◽  
Wide Range ◽  
Yaw Stability ◽  
The Stability ◽  
Lateral Error

Path tracking is one of the most important aspects of autonomous vehicles. The current research focuses on designing path-tracking controllers taking into account the stability of the yaw and the nonholonomic constraints of the vehicle. In most cases, the lateral controller design relies on identifying a path reference point, the one with the shortest distance to the vehicle giving the current state of the vehicle. That restricts the controller’s ability to handle sudden changes of the trajectory heading angle. The present article proposes a new approach that imitates human behavior while driving. It is based on a discrete prediction model that anticipates the future states of the vehicle, allowing the use of the control algorithm in future predicted states augmented with the current controller output. The performance of the proposed approach is verified through several simulations on V-REP simulator with different types of maneuvers (double lane change, hook road, S road, and curved road) and a wide range of velocities. Predictive Stanley controller was used compared to the original Stanley controller. The obtained results of the proposed control approach show the advantage and the performance of the technique in terms of minimizing the lateral error and ensuring yaw stability by an average of 53% and 22%, respectively.

A novel adaptive control approach for path tracking control of autonomous vehicles subject to uncertain dynamics

2020 ◽  
Vol 234 (8) ◽  
pp. 2115-2126
Author(s):  
Ardashir Mohammadzadeh ◽  
Hamid Taghavifar
Keyword(s):  
Autonomous Vehicles ◽  
Tracking Control ◽  
Control Method ◽  
Path Tracking ◽  
Operating Conditions ◽  
Ground Vehicles ◽  
Control Approach ◽  
Autonomous Ground Vehicles ◽  
Lane Changes ◽  
Uncertain Dynamics

Autonomous ground vehicles are constantly exposed to matched/mismatched uncertainties and disturbances and different operating conditions. Consequently, robustness to resist the undesirable effect of changes in the nominal parameters of the vehicle is a significant provision for satisfactory path-tracking control of these vehicles. The accomplishment of lateral path-tracking control is an essential task expectable from autonomous ground vehicles, particularly during critical maneuvers, abrupt cornering, and lane changes at high speeds. This paper presents a new control approach based on immersion and invariance control theorem. The asymptotic stability of the proposed method is ensured and the adaptation laws for the parameters are derived based on the I&I stability theorem. The effectiveness of the proposed control method is confirmed for autonomous ground vehicles systems while making a double-lane-change at various forward speeds. The robustness of the proposed control method is evaluated under parametric uncertainties related to the autonomous ground vehicle and different road conditions. The obtained results suggest that the proposed control method holds the capacity to be applied effectively to the path-tracking task of autonomous ground vehicles under a broad range of operating conditions, parametric uncertainness, and external disturbances.

A novel path tracking approach considering safety of the intended functionality for autonomous vehicles

2021 ◽  
pp. 095440702110205
Author(s):  
Huiran Wang ◽  
Qidong Wang ◽  
Wuwei Chen ◽  
Linfeng Zhao ◽  
Dongkui Tan
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Coordinated Control ◽  
Steering System ◽  
Front Wheel ◽  
Path Tracking ◽  
Hierarchical Architecture ◽  
Automatic Steering ◽  
The Stability ◽  
Control Layer

To reduce the adverse effect of the functional insufficiency of the steering system on the accuracy of path tracking, a path tracking approach considering safety of the intended functionality is proposed by coordinating automatic steering and differential braking in this paper. The proposed method adopts a hierarchical architecture consisting of a coordinated control layer and an execution control layer. In coordinated control layer, an extension controller considering functional insufficiency of the steering system, tire force characteristics and vehicle driving stability is proposed to determine the weight coefficients of automatic steering and the differential braking, and a model predictive controller is designed to calculate the desired front wheel angle and additional yaw moment. In execution control layer, a H∞ steering angle controller considering external disturbances and parameter uncertainty is designed to track desired front wheel angle, and a braking force distribution module is used to determine the wheel cylinder pressure of the controlled wheels. Both simulation and experiment results show that the proposed method can overcome the functional insufficiency of the steering system and improve the accuracy of path tracking while maintaining the stability of the autonomous vehicle.

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