Parallel hybrid interval type-2 fuzzy NARX network for robust line-following control of autonomous guided vehicles

Abstract This study introduces a parallel hybrid control architecture that integrates an interval type-2 fuzzy system (IT2FS) with a recurrent nonlinear autoregressive network with exogenous inputs (NARX) to enhance the line-following capability of autonomous guided vehicles (AGVs) under uncertainty and operational disturbances. In the proposed framework, the IT2FS explicitly models and compensates for system uncertainties, while the recurrent NARX structure contributes dynamic learning and improved temporal representation. Unlike conventional series fuzzy-neural hybrids, the proposed parallel fusion executes uncertainty-aware fuzzy reasoning and recurrent dynamic compensation concurrently, enabling robust real-time AGV control on a PLC platform. The parallel combination of the two components increases control flexibility, enabling faster adaptation and more stable behavior as operating conditions vary. The controller is developed, trained, and evaluated in a real-time environment, demonstrating accurate trajectory tracking and rapid responses to path variations. The results highlight the potential of type-2 fuzzy-NARX hybrid architectures for intelligent mobile-robot control, particularly in industrial autonomous navigation tasks.