scholarly journals Review on Motion Control of Autonomous Vehicles

2020 ◽  
Vol 56 (10) ◽  
pp. 127
Author(s):  
XIONG Lu ◽  
YANG Xing ◽  
ZHUO Guirong ◽  
LENG Bo ◽  
ZHANG Renxie
Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 297
Author(s):  
Ali Marzoughi ◽  
Andrey V. Savkin

We study problems of intercepting single and multiple invasive intruders on a boundary of a planar region by employing a team of autonomous unmanned surface vehicles. First, the problem of intercepting a single intruder has been studied and then the proposed strategy has been applied to intercepting multiple intruders on the region boundary. Based on the proposed decentralised motion control algorithm and decision making strategy, each autonomous vehicle intercepts any intruder, which tends to leave the region by detecting the most vulnerable point of the boundary. An efficient and simple mathematical rules based control algorithm for navigating the autonomous vehicles on the boundary of the see region is developed. The proposed algorithm is computationally simple and easily implementable in real life intruder interception applications. In this paper, we obtain necessary and sufficient conditions for the existence of a real-time solution to the considered problem of intruder interception. The effectiveness of the proposed method is confirmed by computer simulations with both single and multiple intruders.


2019 ◽  
Vol 18 (6) ◽  
pp. 1510-1517
Author(s):  
Hongyang Xia ◽  
Jiqing Chen ◽  
Fengchong Lan ◽  
Zhaolin Liu

PAMM ◽  
2012 ◽  
Vol 12 (1) ◽  
pp. 733-734 ◽  
Author(s):  
Axel Hackbarth ◽  
Edwin Kreuzer ◽  
Andrew Gray

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6052
Author(s):  
Xing Yang ◽  
Lu Xiong ◽  
Bo Leng ◽  
Dequan Zeng ◽  
Guirong Zhuo

As one of the core issues of autonomous vehicles, vehicle motion control directly affects vehicle safety and user experience. Therefore, it is expected to design a simple, reliable, and robust path following the controller that can handle complex situations. To deal with the longitudinal motion control problem, a speed tracking controller based on sliding mode control with nonlinear conditional integrator is proposed, and its stability is proved by the Lyapunov theory. Then, a linear parameter varying model predictive control (LPV-MPC) based lateral controller is formulated that the optimization problem is solved by CVXGEN. The nonlinear active disturbance rejection control (ADRC) method is applied to the second lateral controller that is easy to be implemented and robust to parametric uncertainties and disturbances, and the pure pursuit algorithm serves as a benchmark. Simulation results in different scenarios demonstrate the effectiveness of the proposed control schemes, and a comparison is made to highlight the advantages and drawbacks. It can be concluded that the LPV-MPC has some trouble to handle uncertainties while the nonlinear ADRC performs slight worse tracking but has strong robustness. With the parallel development of the control theory and computing power, robust MPC may be the future direction.


Author(s):  
Bruno Arnaldi ◽  
Rémi Cozot ◽  
Stéphane Donikian ◽  
Michel Parent

The Praxitele project was charged with designing a new kind of transportation in an urban environment, which consisted of a fleet of electric public cars. These public cars are capable of autonomous motion on certain displacements between stations. The realization of such a project requires experimentation regarding the behaviors of autonomous vehicles in the urban environment. Because of the danger connected with these kinds of experiments at a real site, it was necessary to design a virtual urban environment in which simulations could be done. To perform an authentic simulation of a real environment composed of a large set of vehicles (some of which are autonomous and others of which are controlled by the user or by some specific control law), different models need to be implemented: geometric modeling of the environment, mechanical simulation, motion control models, driver models, sensor models, and visualization algorithms. To implement these different models into a unique system, a new simulator system was designed. This simulator takes into account real-time synchronization and communication between cooperative processes implementing the models mentioned earlier. First, the aims and goals of the Praxitele project are presented. The motion control algorithm for automatic platooning of autonomous vehicles is then briefly presented. The focus is on the simulation of a virtual urban environment that includes Praxitele vehicles. The implementation of all of these models is described. Finally, results of a simulation of cooperative driving of the Praxitele vehicles in a virtual urban environment are given.


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