Citation

Luan Z, Zhang J, Zhao W, Wang C. IEEE Trans. Vehicular Tech. 2020; 69(8): 8140-8150.

Copyright

(Copyright © 2020, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/TVT.2020.2995408

PMID

unavailable

Abstract

Random network delay will introduce uncertainty into trajectory tracking model of the autonomous vehicle, which seriously deteriorates the vehicle's control system stability and trajectory tracking accuracy. In this paper, considering steering angle oscillation caused by random network delay, trajectory tracking system robustness and stability is analyzed and a linear uncertain time-delay system is established. Comprehensively considering control system accuracy, robustness, and computational efficiency in the rolling optimization of Model Predictive Control (MPC), Adaptive Model Predictive Control for Uncertain model (UM-AMPC) algorithm is proposed to predict control variables for the next sampling time and alleviate the target angle discontinuity. This is achieved by operating target angle signal and augmented state variables, which are received by the lower nodes during the period from the current sampling time to network delay upper bound. The hardware-in-the-loop simulation results show that the proposed algorithm can effectively guarantee system stability and tracking accuracy of the autonomous vehicle under random network delay.

Language: en

Keywords

adaptive model predictive control; autonomous vehicle; Autonomous vehicle; Autonomous vehicles; control system accuracy; control system synthesis; control variables; delay systems; delays; Delays; linear systems; linear uncertain time-delay system; model predictive control; nonlinear control systems; Prediction algorithms; predictive control; Predictive control; random network delay; remotely operated vehicles; robust control; stability; Stability analysis; system stability; trajectory tracking; Trajectory tracking; trajectory tracking accuracy; trajectory tracking control; trajectory tracking model; trajectory tracking system robustness; uncertain model algorithm; uncertain systems; Uncertainty