Adaptive optimal control allocation using Lagrangian neural networks for stability control of a 4WS–4WD electric vehicle

2013 ◽  
Vol 35 (8) ◽  
pp. 1139-1151 ◽  
Author(s):  
Murat Demirci ◽  
Metin Gokasan
2019 ◽  
Vol 2019 ◽  
pp. 1-21
Author(s):  
Yu Zhao ◽  
Chengning Zhang

An electronic stability control (ESC) based on torque distribution is proposed for an eight in-wheel motor-independent drive electric vehicle (8WIDEV). The proposed ESC is extremely suitable for the independent driving vehicle to enhance its handling stability performance. The vehicle model is established based on a prototype 8WIDEV. A hierarchical control strategy, which includes a reference state generation controller, an upper-level vehicle controller, and a lower-level optimal control allocation controller, is utilized in the ESC. The reference state generation controller is used to obtain the ideal reference vehicle state. The upper-level vehicle controller is structured based on sliding mode control, which obtains the generalized objective force during 8WIDEV movement, therein considering the side slip angle and yaw rate. The lower-level optimal control allocation controller attempts to allocate the vehicle’s objective force in each motor optimally and reasonably. The model is validated by field measurement results under the step input condition and snake input condition. Simulation results from a hardware-in-the-loop (HIL) simulation platform indicate that the ESC based on the optimized allocation proposed for 8WIDEV achieves better stability performance compared with direct yaw moment control (DYC).


IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 4824-4833 ◽  
Author(s):  
Houhua Jing ◽  
Fengjiao Jia ◽  
Zhiyuan Liu

2013 ◽  
Vol 437 ◽  
pp. 669-673 ◽  
Author(s):  
Peng Fei Yang ◽  
Lu Xiong ◽  
Zhuo Ping Yu

Design the stability control strategy of four in-wheel-motors drive electric vehicle (EV) based on control allocation. Two kinds of control allocation methods are designed in this paper, one is the quadratic programming (QP), and the other is a simplified optimization method (SOM). Comparing and evaluating the control strategies through the co-simulation with MATLAB software and CARSIM software. The results of the simulation show: both strategies could stabilize the vehicle posture well under critical condition. QP has more accuracy than SOM, and could rebuild the system automatically when the motor fails. But the SOM doesn’t need iteration, could be possible to use in the real vehicle.


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