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Parallel Binary Equilibrium Optimization-Based Incremental Dominance Model Predictive Control for AGV Trajectory Tracking
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DOI：10.1016/j.jfranklin.2026.108710.png)
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Abstract
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This paper presents an improved incremental dominance model predictive control (IIDMPC) to overcome the key limitations of conventional model predictive control (MPC) in real-time applications for automatic guided vehicles (AGVs), in which the high computational burden degrades real-time performance, particularly when handling complex constraints and long optimization horizons. To address this challenge, the study begins by establishing an MPC framework based on the kinematic model of AGVs. Subsequently, it introduces the concept of incremental dominance relations and defines an incremental dominance vector, which consists of three distinct components: dominated increments, undominated increments, and increments with undetermined dominance relationships. To optimize the computational process, a novel method for constructing the incremental dominance matrix is proposed and subsequently integrated into the conventional MPC framework. Furthermore, a new parallel binary equilibrium optimization (PBiEO) algorithm is introduced to optimize uncertain dominance relationships within the incremental dominance matrix. Comprehensive simulation experiments demonstrate that the proposed PBiEO algorithm exhibits rapid convergence and significantly reduces the likelihood of getting trapped in local optima. More importantly, the results show that while the tracking precision is maintained at a level comparable to conventional MPC, the computational efficiency is significantly enhanced.
Keywords:
Incremental Dominance
Model Predictive Control
Automatic Guided Vehicles
Parallel Binary Equilibrium Optimization
Trajectory Tracking
Journal
J
IF:
4.2
Papers:
812
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0
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