A Comparative Study of Two Differet MPC Controllers Integrated With MHE

Year 2025, Volume: 9 Issue: 4, 637 - 656, 31.12.2025

Mostafa Mohamed , Shady Maged Mohamed Abdelwahab

https://doi.org/10.30939/ijastech..1631384

Abstract

In Executing aggressive maneuvers, such as the Double Lane Change (DLC), presents a significant challenge for autonomous vehicle control, particularly when dealing with highly non-linear vehicle dynamics. This paper proposes a novel path-following and planning approach based on a comparative analysis of two distinct Nonlinear Model Predictive Control (NMPC) architectures. Developed using the CasADi framework within the MATLAB environment, the controllers are applied to a high-fidelity 8-Degree-of-Freedom (8-DOF) vehicle model, solved via the ode45 function. To ensure practical robustness against sensor noise, the control loop is integrated with a Moving Horizon Estimator (MHE), which significantly enhances the accuracy of state estimation during operation. The first control architecture implements a kinematic model, defining states by vehicle position and yaw angle to control wheel rotation speeds and steering. In comparison, the second architecture utilizes a comprehensive dynamic model that incorporates higher-order states, such as yaw rate and side-slip angle, with control inputs for independent front steering angles. We rigorously evaluated both controllers across a wide spectrum of longitudinal speeds and varying road surface conditions. The results assess the capability of each system to track predefined trajectories while strictly satisfying state and control constraints, ultimately clarifying the specific trade-offs between computational simplicity and the dynamic stability required for high-speed tracking tasks.

Keywords

Autonomous vehicle , Double lane change , Model predictive control , Moving Horizon Estimation , Path following , Robust control.

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