Trajectory tracking control considering the transmission backlash of the dual-motor autonomous steering system

Abstract

In order to solve the tracking error caused by transmission backlash in the dual-motor autonomous steering system, and thus improve the trajectory tracking accuracy of autonomous vehicles, a novel steering control strategy combining sliding model control (SMC) with variable weights and linear quadratic regulator (LQR) is proposed in this paper. Firstly, the vehicle dynamics model is built and the steering system model with contact and backlash modes is established by combining it with the simplified gear backlash system. Secondly, the front wheel angle control based on SMC is designed and the weights of contact mode and backlash mode are optimized by Particle Swarm Optimization (PSO) algorithm. Finally, the results of simulation under typical conditions show that the proposed strategy has better accuracy of angle tracking and stronger adaptability to the change of system load. Furthermore, it is also proved by simulation and Hardware-in-Loop (HiL) test under different trajectory tracking conditions that the LQR controller with proposed strategy has better accuracy of trajectory tracking for autonomous vehicles.

Input-Output Feedback Linearization Associates with Linear Quadratic Regulator for Stabilization Control of Furuta Pendulum System

“…Many researches have been completed and shown to scientific community related to topic of combining control approaches. Diversity of combination of controllers have been presented such as a combination of sliding mode control (SMC) and LQR for stabilization control of Quadcopter [1], combining SMC with variable weights and LQR for trajectory tracking control problem for dual-motor autonomous steering system [2], sliding mode -disturbance observer (SMC-DO) combines with LQR technique for controlling flexible manipulators robot [3], controlling 3D overhead crane systems by using PID-SMC [4], adaptive back-stepping sliding mode [5], optimum fuzzy combination of decoupled SMC (DSMC) [6], a feed-forward controller combines with feedback controller for tracking control problem [7], adaptive radical basis function neural networks associates with proportional derivative-SMC method [8], adaptive fuzzy logic back-stepping methodology [9], combination of state feedback controller with RBF [10], SMC integrates with partial feedback linearization for a spatial ballbot [11]. In addition, combination of control strategies for swing-up problem have been studied.…”