Title: Intelligent control strategy for a heavy vehicle compaction system DYNAPAC CC6200

Authors: Hocine Chebi

Addresses: Faculty of Electrical Engineering, Djillali Liabes University, Sidi Bel Abbes 22000, Algeria

Abstract: This paper proposes a GA-LOS-PI controller-based four-level control system that accurately evaluates pavement compaction effect of DYNAPAC CC6200 autonomous articulated vehicle (AAV). In the evaluation layer, various performance indicators are evaluated, including stability, rapidity and accuracy when trajectory tracking, and the ratio of required compaction to actual compaction once and twice and compaction repeatability index when pavement compacting. In the decision and control layer, incremental PI controller is used as the main control strategy, line of sight (LOS) guidance is introduced to eliminate system control lag, and genetic algorithm (GA) is used for searching the best proportional. The comparative simulation results of no controller, the traditional incremental PI controller, LOS-PI controller with human driving experience initial control parameters (HDEICP), GA-LOS-PI controller with random initial control parameters (RICP) and GA-LOS-PI controller with HDEICP manifest evidently that the proposed GA-LOS-PI controller with HDEICP has almost no steady-state error.

Keywords: pavement compaction; four-level system; AAV; autonomous articulated vehicle; genetic algorithm; PI controller; line of sight guidance; trajectory tracking; DYNAPAC CC6200.

DOI: 10.1504/IJHVS.2022.127023

International Journal of Heavy Vehicle Systems, 2022 Vol.29 No.3, pp.227 - 244

Received: 16 Jun 2020
Accepted: 27 May 2021
Published online: 18 Nov 2022 *

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