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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

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Published on: October 14, 2017

New control model for autonomous vehicles using integration of Model Predictive and Stanley based controllers.

Mustafa Hamid Al-Jumaili^1,2, Yasa Ekşioğlu Özok³

¹Electrical and Computer Engineering Department, Altinbas University, Istanbul, Turkey. malju2@unh.newhaven.edu.

Scientific Reports

|August 27, 2024

Summary

A new Model Predictive and Stanley based controller (MPS) enhances autonomous vehicle control. This hybrid system integrates two controllers to improve performance and reduce errors in various driving scenarios.

Keywords:

Autonomous vehicle MPC MPS Model predictive control Path tracking Stanley control

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Area of Science:

Robotics and Control Systems
Autonomous Vehicle Technology

Background:

Autonomous vehicles require robust control systems for safe and efficient operation.
Existing control methods, such as model predictive control and Stanley control, have individual limitations.

Purpose of the Study:

To introduce a novel hybrid control system, the Model Predictive and Stanley based controller (MPS), for autonomous vehicle applications.
To leverage the strengths of both model predictive and Stanley controllers to overcome individual drawbacks.

Main Methods:

Development of a hybrid control system integrating model predictive and Stanley controllers.
Testing the MPS controller for path-following and overall vehicle control in diverse scenarios.
Evaluation on both straight and curved road segments.

Main Results:

The MPS controller demonstrated high performance and flexibility in various autonomous driving scenarios.
The hybrid approach effectively combined the advantages of the constituent controllers.
Comparative analysis showed the MPS system outperformed existing control methods.

Conclusions:

The proposed MPS controller offers a robust and high-performance solution for autonomous vehicle control.
This hybrid strategy represents a significant advancement in autonomous driving control systems.
The MPS controller's adaptability makes it suitable for complex and varied driving conditions.