RBFNN‐Based Identification and Compensation Mechanism for Disturbance‐Like Parametric Friction with Application to Tractor‐Trailer Vehicles

Abstract

Abstract This paper develops an effective identification and compensation mechanism for the disturbance‐like parametric friction of a typical underactuated tractor‐trailer vehicle system. To begin with, a parametric friction model is proposed to describe various friction effects associated with the system velocity, and then a disturbance‐like parametric friction concept is introduced by considering the motion characteristics of tractor‐trailer vehicle. Next, the radial basis function neural network (RBFNN) is employed to identify the friction due to its high convergence rate, superior approximation precision and local‐minima avoidance ability. Afterwards, a sliding mode control (SMC) is utilized to compensate the identified friction due to its numerous merits, such as strong robustness and fast convergence. On the basis of the effective combination of identification and compensation mechanism, a favorable transient performance can be achieved during the desired velocity tracking process. Lastly, the simulation results confirm that the RBFNN‐based disturbance‐like parametric friction identification and compensation mechanism can effectively improve the trajectory tracking performance of tractor‐trailer vehicle.