AutoDyn: robust model predictive control design for trajectory tracking under varying vehicle dynamics
Kumlachew Yeneneh1, Gadisa Sufe2
1Department of Motor vehicle Engineering, College of Engineering, Ethiopian Defence University, P.O. Box 1041, Bishoftu, Ethiopia. kumynnh.coe@etdu.edu.et.
Scientific Reports
|June 2, 2026
Summary
This study introduces a Robust Model Predictive Control (RMPC) framework for enhanced trajectory tracking in autonomous vehicles. The RMPC significantly improves accuracy, stability, and real-time performance under uncertain conditions.
Area of Science:
- Control Systems Engineering
- Robotics
- Automotive Engineering
Background:
- Autonomous vehicles require precise trajectory tracking despite complex dynamics and parameter uncertainties.
- Ensuring safety, comfort, and stability is critical in varying road conditions and vehicle states.
Purpose of the Study:
- To develop a Robust Model Predictive Control (RMPC) framework for accurate and stable trajectory tracking.
- To address parameter uncertainty and external disturbances in vehicle dynamics.
- To ensure computational efficiency for real-time automotive applications.
Main Methods:
- Developed a nonlinear vehicle model including lateral, yaw, and load-transfer dynamics.
- Integrated multi-parametric predictive modeling and robust optimization with constraint tightening.
- Benchmarked RMPC against Proportional Integral Derivative (PID) and linear predictive control (LPC) strategies.
Main Results:
- Achieved up to 78% reduction in lateral tracking error and 71% enhancement in yaw stability.
- Demonstrated 68% reduction in settling time and 61% smoother steering actuation.
- Showcased 57% improvement in disturbance rejection with an average computation time of 6.3 ms.
Conclusions:
- The proposed RMPC framework significantly enhances accuracy, stability, and computational efficiency.
- It offers improved smoothness and explicit constraint handling compared to Sliding Mode Control.
- Provides a practical foundation for next-generation intelligent vehicle control systems.
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