Lateral Control Strategy Based on MPC for Autonomous Vehicle under Extreme Road Adhesion Conditions

2021 ◽  
Author(s):  
Hanbing Wei ◽  
Dawei Gong
Keyword(s):  
Control Strategy ◽  
Autonomous Vehicle ◽  
Lateral Control

Lateral control strategy based on head movement responses for motion sickness mitigation in autonomous vehicle

2020 ◽  
Vol 42 (5) ◽  
Author(s):  
Sarah ‘Atifah Saruchi ◽  
Mohd Hatta Mohammed Ariff ◽  
Hairi Zamzuri ◽  
Noor Hafizah Amer ◽  
Nurbaiti Wahid ◽  
...  
Keyword(s):  
Motion Sickness ◽  
Control Strategy ◽  
Head Movement ◽  
Autonomous Vehicle ◽  
Lateral Control

Longitudinal and lateral collision avoidance control strategy for intelligent vehicles

2021 ◽  
pp. 095440702110240
Author(s):  
Ziyu Zhang ◽  
Chunyan Wang ◽  
Wanzhong Zhao ◽  
Jian Feng
Keyword(s):  
Real Time ◽  
Collision Avoidance ◽  
Decentralized Control ◽  
Control Strategy ◽  
Centralized Control ◽  
Lateral Control ◽  
Time Performance ◽  
Collision Avoidance Control ◽  
Avoidance Control ◽  
Control Accuracy

In order to solve the problems of longitudinal and lateral control coupling, low accuracy and poor real-time of existing control strategy in the process of active collision avoidance, a longitudinal and lateral collision avoidance control strategy of intelligent vehicle based on model predictive control is proposed in this paper. Firstly, the vehicle nonlinear coupling dynamics model is established. Secondly, considering the accuracy and real-time requirements of intelligent vehicle motion control in pedestrian crossing scene, and combining the advantages of centralized control and decentralized control, an integrated unidirectional decoupling compensation motion control strategy is proposed. The proposed strategy uses two pairs of unidirectional decoupling compensation controllers to realize the mutual integration and decoupling in both longitudinal and lateral directions. Compared with centralized control, it simplifies the design of controller, retains the advantages of centralized control, and improves the real-time performance of control. Compared with the decentralized control, it considers the influence of longitudinal and lateral control, retains the advantages of decentralized control, and improves the control accuracy. Finally, the proposed control strategy is simulated and analyzed in six working conditions, and compared with the existing control strategy. The results show that the proposed control strategy is obviously better than the existing control strategy in terms of control accuracy and real-time performance, and can effectively improve vehicle safety and stability.

SVM Based Lateral Control for Autonomous Vehicle

2005 ◽  
pp. 185-191
Author(s):  
Hanqing Zhao ◽  
Tao Wu ◽  
Daxue Liu ◽  
Yang Chen ◽  
Hangen He
Keyword(s):  
Autonomous Vehicle ◽  
Lateral Control

scholarly journals Lateral control of an autonomous vehicle using integrated backstepping and sliding mode controller

2018 ◽  
Vol 233 (1) ◽  
pp. 141-151 ◽  
Author(s):  
Armin Norouzi ◽  
Milad Masoumi ◽  
Ali Barari ◽  
Saina Farrokhpour Sani
Keyword(s):  
Optimization Algorithm ◽  
Sliding Mode ◽  
Autonomous Vehicle ◽  
Lane Change ◽  
Lateral Control ◽  
Lane Changing ◽  
Dynamic Constraints ◽  
Bicycle Model ◽  
Double Lane Change ◽  
Backstepping Controller

In this paper, a novel Lyapunov-based robust controller by using meta-heuristic optimization algorithm has been proposed for lateral control of an autonomous vehicle. In the first step, double lane change path has been designed using a fifth-degree polynomial (quantic) function and dynamic constraints. A lane changing path planning method has been used to design the double lane change manoeuvre. In the next step, position and orientation errors have been extracted based on the two-degree-of-freedom vehicle bicycle model. A combination of sliding mode and backstepping controllers has been used to control the steering in this paper. Overall stability of the combined controller has been analytically proved by defining a Lyapunov function and based on Lyapunov stability theorem. The proposed controller includes some constant parameters which have effects on controller performance; therefore, particle swarm optimization algorithm has been used for finding optimum values of these parameters. The comparing result of the proposed controller with backstepping controller illustrated the better performance of the proposed controller, especially in the low road frictions. Simulation of designed controllers has been conducted by linking CarSim software with Matlab/Simulink which provides a nonlinear full vehicle model. The simulation was performed for manoeuvres with different durations and road frictions. The proposed controller has outperformed the backstepping controller, especially in low frictions.

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