Lateral Stability Control of Autonomous Ground Vehicles Considering Stability Margins and State Estimation Errors

2018 ◽  
Author(s):  
Yiwen Huang ◽  
Yan Chen
Keyword(s):  
State Estimation ◽  
Stability Control ◽  
Lateral Stability ◽  
Ground Vehicles ◽  
Estimation Errors ◽  
Stability Margins ◽  
Autonomous Ground Vehicles

Stability Regions of Vehicle Lateral Dynamics: Estimation and Analysis

2020 ◽  
Vol 143 (5) ◽  
Author(s):  
Yiwen Huang ◽  
Wei Liang ◽  
Yan Chen
Keyword(s):  
Load Transfer ◽  
Equilibrium Points ◽  
Longitudinal Velocity ◽  
Linearization Method ◽  
Stability Control ◽  
Stable Operation ◽  
Lateral Stability ◽  
Ground Vehicles ◽  
Stability Regions ◽  
Road Friction

Abstract A new method is proposed to estimate and analyze the vehicle lateral stability region, which provides a direct and intuitive demonstration for the safety and stability control of ground vehicles. Based on a four-wheel vehicle model and a nonlinear two-dimensional (2D) analytical LuGre tire model, a local linearization method is applied to estimate the vehicle lateral stability regions by analyzing the vehicle stability at each operation point on a phase plane, which includes but not limited to the equilibrium points. As the collections of all the locally stable operation points, the estimated stability regions are conservative because both vehicle and tire stability are simultaneously considered, which are especially important for characterizing the stability features of highly/fully automated ground vehicles (AGV). The obtained lateral stability regions can be well explained by the vehicle characteristics of oversteering and understeering in the context of vehicle handling stability. The impacts of vehicle lateral load transfer, longitudinal velocity, tire-road friction coefficient, and steering angle on the estimated stability regions are presented and discussed. To validate the correctness of the estimated stability regions, a case study by matlab/simulink and CarSim® co-simulation is presented and discussed.

Fuzzy Control for Autonomous Ground Vehicles

2000 ◽  
Author(s):  
Jeffrey S. Wit ◽  
Carl D. Crane ◽  
Armstrong III ◽  
II David G.
Keyword(s):  
Fuzzy Control ◽  
Ground Vehicles ◽  
Autonomous Ground Vehicles

scholarly journals Steering Angle Prediction Techniques for Autonomous Ground Vehicles: A Review

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Hajira Saleem ◽  
Faisal Riaz ◽  
Leonardo Mostarda ◽  
Muaz A. Niazi ◽  
Ammar Rafiq ◽  
...  
Keyword(s):  
Ground Vehicles ◽  
Steering Angle ◽  
Autonomous Ground Vehicles ◽  
Prediction Techniques

Multi-layer controller with state-constraint: Vehicle lateral stability control based on fuzzy logic

2021 ◽  
pp. 095440702110142
Author(s):  
Neng Wan ◽  
Guangping Zeng ◽  
Chunguang Zhang ◽  
Dingqi Pan ◽  
Songtao Cai
Keyword(s):  
Control System ◽  
Integrated Control ◽  
State Constraint ◽  
Stability Control ◽  
Lateral Stability ◽  
System A ◽  
Velocity Reduction ◽  
Vehicle Velocity ◽  
Allowable Range ◽  
Simulation Results

This paper deals with a new state-constrained control (SCC) system of vehicle, which includes a multi-layer controller, in order to ensure the vehicle’s lateral stability and steering performance under complex environment. In this system, a new constraint control strategy with input and state constraints is applied to calculate the steady-state yaw moment. It ensures the vehicle lateral stability by tracking the desired yaw rate value and limiting the allowable range of the side slip. Through the linkage of the three-layer controller, the tire load is optimized and achieve minimal vehicle velocity reduction. The seven-degree-of-freedom (7-DOF) simulation model was established and simulated in MATLAB to evaluate the effect of the proposed controller. Through the analysis of the simulation results, compared with the traditional ESC and integrated control, it not only solves the problem of obvious velocity reduction, but also solves the problem of high cost and high hardware requirements in integrated control. The simulation results show that designed control system has better performance of path tracking and driving state, which is closer to the desired value. Through hardware-in-the-loop (HIL) practical experiments in two typical driving conditions, the effectiveness of the above proposed control system is further verified, which can improve the lateral stability and maneuverability of the vehicle.

scholarly journals Model predictive control for autonomous ground vehicles: a review

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Shuyou Yu ◽  
Matthias Hirche ◽  
Yanjun Huang ◽  
Hong Chen ◽  
Frank Allgöwer
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
High Performance ◽  
Control Method ◽  
Theoretical Background ◽  
Future Research ◽  
Centralized Control ◽  
Ground Vehicles ◽  
Uncertain Environments ◽  
Autonomous Ground Vehicles

AbstractThis paper reviews model predictive control (MPC) and its wide applications to both single and multiple autonomous ground vehicles (AGVs). On one hand, MPC is a well-established optimal control method, which uses the predicted future information to optimize the control actions while explicitly considering constraints. On the other hand, AGVs are able to make forecasts and adapt their decisions in uncertain environments. Therefore, because of the nature of MPC and the requirements of AGVs, it is intuitive to apply MPC algorithms to AGVs. AGVs are interesting not only for considering them alone, which requires centralized control approaches, but also as groups of AGVs that interact and communicate with each other and have their own controller onboard. This calls for distributed control solutions. First, a short introduction into the basic theoretical background of centralized and distributed MPC is given. Then, it comprehensively reviews MPC applications for both single and multiple AGVs. Finally, the paper highlights existing issues and future research directions, which will promote the development of MPC schemes with high performance in AGVs.

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