Adaptive Control of Vehicle Yaw Rate with Active Steering System and Extreme Learning Machine - A Pilot Study

The active steering system can enhance the vehicle yaw stability, which is essential to road safety. However, control of vehicle yaw rate is very challenging due to the presence of nonlinearity and uncertainties in the vehicle dynamics. To address the problems, an extreme-learning-machine (ELM)-based adaptive control algorithm is proposed, and a vehicle dynamic model is also developed to identify the necessary exogenous variables for control system inputs. To validate the performance of the proposed controller, simulation is conducted with an industry software. Simulation result indicates that the proposed controller achieves superior performance in tracking nominal vehicle yaw rate. A comparison is also carried out with fuzzy logic control. The pilot result shows that the proposed controller outperforms the fuzzy logic control.

[1]  İkbal Eski,et al.  Design of neural network-based control systems for active steering system , 2013, Nonlinear Dynamics.

[2]  Pak Kin Wong,et al.  Modeling and optimization of biodiesel engine performance using kernel-based extreme learning machine and cuckoo search , 2015 .

[3]  Hans B. Pacejka,et al.  A New Tire Model with an Application in Vehicle Dynamics Studies , 1989 .

[4]  Tao Zhang,et al.  Stable Adaptive Neural Network Control , 2001, The Springer International Series on Asian Studies in Computer and Information Science.

[5]  S. Narayanan,et al.  Fuzzy logic based yaw stability control for active front steering of a vehicle , 2014 .

[6]  Vadim I. Utkin,et al.  SLIDING MODE CONTROL FOR ACTIVE STEERING OF CARS , 1995 .

[7]  Yong Zhang,et al.  Controller design for vehicle stability enhancement , 2006 .

[8]  Jingang Yi,et al.  Adaptive emergency braking control in automated highway systems , 1999, Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304).

[9]  Vadim I. Utkin,et al.  Chattering Problem in Sliding Mode Control Systems , 2006, International Workshop on Variable Structure Systems, 2006. VSS'06..

[10]  Wei He,et al.  Adaptive Neural Network Control of an Uncertain Robot With Full-State Constraints , 2016, IEEE Transactions on Cybernetics.

[11]  Avesta Goodarzi,et al.  Optimal yaw moment control law for improved vehicle handling , 2003 .

[12]  Dianhui Wang,et al.  Extreme learning machines: a survey , 2011, Int. J. Mach. Learn. Cybern..

[13]  Shui-Chun Lin,et al.  Adaptive Neural Network Control of a Self-Balancing Two-Wheeled Scooter , 2010, IEEE Transactions on Industrial Electronics.

[14]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[15]  Norhazimi Hamzah,et al.  A Review of Active Yaw Control System for Vehicle Handling and Stability Enhancement , 2014 .