Adaptive neural control of vehicle yaw stability with active front steering using an improved random projection neural network

ABSTRACT

Active front steering (AFS) can enhance the vehicle yaw stability. However, the control of vehicle yaw rate is very challenging due to (1) the unmodelled nonlinearity and uncertainties in vehicle dynamics; (2) timely response in control scheme. These two issues can be simultaneously alleviated through a random projection neural network (RPNN) for its high model generalisation and fast computational speed. However, typical RPNN cannot be directly applied to adaptive control applications. Therefore, a new RPNN-based adaptive neural control method is proposed, which is equipped with a newly designed adaptation law based on the theorem of Lyapunov stability. To test the performance of the proposed control method, simulations were carried out using a validated vehicle model. The simulation results show that, compared to conventional backpropagation neural network (BPNN) based controller, the proposed RPNN-based adaptive controller can reduce the response time and attenuate oscillatory steering in the case of cornering manoeuvre under fast variant vehicle speed. The results also demonstrate that the proposed RPNN-based adaptive controller outperforms the state-of-the-art fuzzy logic controller and the error feedback controller in multiple aspects including tracking nominal vehicle yaw rate, desired sideslip angle and intended path, showing its significance in vehicle yaw stability control.

KEYWORDS:

• Active front steering
• yaw rate control
• lateral stability
• adaptive neural control
• random projection neural network

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research is supported by the research grants of the University of Macau [grant numbers MYRG2016-00212-FST, MYRG2017-00135-FST, MYRG2018-00138-FST, and MYRG2019-00028-FST]. This project is also supported by National Natural Science Foundation of China [grant number 51705084] and the Opening Project of Guangdong Provincial Key Laboratory of Technique and Equipment for Macromolecular Advanced Manufacturing, South China University of Technology, P. R. China [grant number 2019kfkt06]; Natural Science Foundation of Guangdong Province of China [grant number 2018A030313999].