Path tracking of vehicles based on forward iterative MPC and multi-step signal time delay compensation

As a path tracking control (PTC) method for autonomous vehicles, nonlinear model predictive control (NMPC) offers high accuracy but limited real-time performance. Moreover, signal time delay (STD) reduces performance and may destabilize the control system. To address these issues, a forward iterative model predictive control (Bai-FIMPC) method is proposed and integrated with a multi-step STD compensation scheme. Bai-FIMPC replaces conventional global optimization with a weighted summation of multi-step feasible solutions, significantly reducing computation time while maintaining high accuracy. The multi-step STD compensation predicts states under delayed execution and feeds them into the PTC controller, thereby mitigating error accumulation. Simulation results under high-dynamic scenarios demonstrate that Bai-FIMPC achieves accuracy comparable to NMPC and LMPC, while reducing average computation time to the sub-millisecond level, close to that of real-time controllers such as proportional–integral–derivative and Stanley. Multi-step STD compensation proves broadly applicable, enabling both Bai-FIMPC and NMPC to maintain effective tracking under delay conditions. Hardware-in-the-loop and field experiments further validate that the integrated Bai-FIMPC with multi-step STD compensation completes PTC tasks with high precision and deterministic real-time performance. These results suggest that the proposed method provides a practical, scalable control framework for autonomous vehicles operating in highly dynamic, STD-affected environments.