Grammatical-Evolution-based parameterized Model Predictive Control for urban traffic networks

Joost Jeschke, Dingshan Sun*, Anahita Jamshidnejad, Bart De Schutter

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

4 Citations (Scopus)
35 Downloads (Pure)

Abstract

While Model Predictive Control (MPC) is a promising approach for network-wide control of urban traffic, the computational complexity of the, often nonlinear, online optimization procedure is too high for real-time implementations. In order to make MPC computationally efficient, this paper introduces a parameterized MPC (PMPC) approach for urban traffic networks that uses Grammatical Evolution to construct continuous parameterized control laws using an effective simulation-based training framework. Furthermore, a projection-based method is proposed to remove the nonlinear constraints that are imposed on the parameters of the parameterized control laws and to guarantee the feasibility of the solution of the MPC optimization problem. The performance and computational efficiency of the constructed parameterized control laws are compared to those of a conventional MPC controller in an extensive simulation-based case study. The results show that the parameterized control laws, which are automatically constructed using Grammatical Evolution, decrease the computational complexity of the online optimization problem by more than 80% with a decrease in performance by less than 10%.

Original languageEnglish
Article number105431
Number of pages13
JournalControl Engineering Practice
Volume132
DOIs
Publication statusPublished - 2023

Keywords

  • Grammatical Evolution
  • Model Predictive Control
  • Parameterized controller
  • Urban traffic control

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