Neural network autoregressive with exogenous input assisted multi-constraint nonlinear predictive control of autonomous vehicles

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4 Citations (Scopus)
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Abstract

This paper focuses on the controller design for path-tracking problem of autonomous ground vehicles (AGVs) by employing a multi-constraint nonlinear predictive control (NMPC) schema. It is aimed to improve the transient performance of the vehicle and to consider a rollover prevention criterion in the proposed method. The path-tracking problem is transformed into the yaw stabilization issue, and a feedback control law with input saturation is developed to decrease the steady-state errors. Furthermore, the yaw-rate signal is generated for the desired path-tracking performance. The major contributions of the present paper are, first, developing a neural network autoregressive with exogenous input system to assist in obtaining an accurate and explicit model in order to contribute to the control of the system over the prediction horizon; second, describing a Frenet-Serret differential geometry based path-following agenda and developing AGV dynamic model by incorporating the vehicle vertical mode of motion to prevent vehicle rollover during critical maneuvers, and finally, achieving an enhanced yaw stabilization and transient tracking performance considering saturated input signal by employing the proposed system identification algorithm. The effectiveness of the proposed control system is verified by comparing with the traditional NMPC method by employing a high fidelity CarSim/MATLAB framework.
Original languageEnglish
Article number8703063
Pages (from-to)6293-6304
Number of pages12
JournalIEEE Transactions on Vehicular Technology
Volume68
Issue number7
Early online date30 Apr 2019
DOIs
Publication statusPublished - Jul 2019
Externally publishedYes

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Keywords

  • Wheels
  • Predictive control
  • Control systems
  • Vehicle dynamics
  • Force
  • Neural networks
  • Autonomous vehicles
  • rollover prevention
  • path-following
  • predictive control

ASJC Scopus subject areas

  • Aerospace Engineering
  • Applied Mathematics
  • Electrical and Electronic Engineering
  • Automotive Engineering

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