Fast warm-start of F-MPC strategy for automotive cruise control with mode switching

Jiaqi Liu, Shiying Dong, Qifang Liu, Bingzhao Gao, Taketoshi Kawabe, Hong Chen

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

A fast iterative algorithm of nonlinear model predictive control is proposed to solve the warm-start problem of the fast model predictive control (F-MPC) method for real-time application with mode switching. Because an incorrect initial guess of F-MPC creates problems such as excessive iterations or even solution failures, the proposed iterative algorithm aims to provide the initial value of F-MPC quickly and efficiently. The idea is to decompose the original nonlinear system into a main linear part and a nonlinear part, which is regarded as measurable disturbance. Then the explicit optimal solution of the “disturbed” system is derived, the obtained series of control inputs are applied to the system, the system state and consequently the “disturbance” are updated, and finally the process is repeated until convergence. The calculated iterative result can be used as the initial solution for F-MPC especially for maneuver mode switching. Automotive cruise control is used as an example for validation, and it is shown that the control strategy has superior adaptability for mode switching, guaranteeing the given safety constraints as well as significantly reducing the computational load.

Original languageEnglish
Article number105344
JournalControl Engineering Practice
Volume128
DOIs
Publication statusPublished - Nov 2022

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

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