A Neural Network Based Teleoperation for Remote Controlled Vehicles

2026-08-10

AI Summary

This study addresses control instability in teleoperated vehicles caused by communication delays, unmodeled environmental disturbances, and highly nonlinear tire–road dynamics. To this end, a unilateral teleoperation framework is proposed, integrating wave-variable (WV)-based passivity for guaranteed stability under time delays and a lightweight, model-free adaptive radial basis function network (RBFN) to compensate for longitudinal and lateral dynamic uncertainties via decoupled adaptation laws. Compared to conventional bilateral WV–neural network architectures, the proposed approach offers superior computational efficiency and structural simplicity. Simulations demonstrate that the RBFN outperforms PID, LQR, MPC, and NMPC controllers in disturbance rejection while achieving execution times several orders of magnitude lower. Hardware-in-the-loop experiments on a 1:10-scale vehicle over a real-world 4G teleoperation platform confirm robust and safe trajectory tracking under realistic network conditions.

Abstract

Direct teleoperation of vehicles faces critical technical bottlenecks: communication latency and the operator's inability to physically perceive unmodeled environmental disturbances (e.g., aerodynamic drag, bank angles) coupled with highly nonlinear tire-road dynamics. To address these challenges, we propose a tailored unilateral teleoperation framework. The system integrates the Wave Variable (WV) approach to passively guarantee stability under stochastic delays, and an adaptive Radial Basis Function Network (RBFN) to actively compensate for vehicle-specific uncertainties. Unlike existing WV-neural network architectures designed for bilateral robotic arms, our framework features decoupled adaptive laws specifically designed for vehicle longitudinal and lateral dynamics. Furthermore, compared to model-heavy predictive controllers, the model-free RBFN offers rapid online adaptation without heavy computational overhead. Building upon our preliminary theoretical formulation, this brief paper presents comprehensive comparative analyses and real-world hardware validations. Simulation benchmarks against PID, LQR, MPC, and NMPC demonstrate that the RBFN achieves superior robustness against unmodeled disturbances while requiring orders of magnitude less execution time than MPC and NMPC, making it ideal for resource-constrained vehicle edge computing. Finally, hardware-in-the-loop experiments using a 1/10th scale vehicle over a 4G network validate the system's practical feasibility, safety, and robust trajectory tracking under physical road uncertainties.

Problem

• teleoperation
• communication latency
• unmodeled disturbances
• nonlinear tire-road dynamics
• remote controlled vehicles

Innovation

• Wave Variable
• Radial Basis Function Network
• Unilateral Teleoperation
• Model-free Adaptation
• Vehicle Dynamics Compensation