Optimum Control for Path Tracking Problem of Vehicle Handling Inverse Dynamics

Area of Science:

• Control Theory
• Robotics
• Computational Mathematics

Background:

• Vehicle path tracking is crucial for maneuverability and autonomous vehicle research.
• The problem is typically addressed by transforming it into an optimal control problem.
• Existing methods may have limitations in accuracy and applicability.

Purpose of the Study:

• To propose an efficient and accurate method for solving nonlinear optimal control problems in vehicle path tracking.
• To introduce a novel Symplectic Pseudospectral Method (SPM) for enhanced vehicle control.
• To compare the performance of SPM against established methods like the Gauss Pseudospectral Method (GPM).

Main Methods:

• Development of a Symplectic Pseudospectral Method (SPM) utilizing third-generation symplectic theory.
• Application of pseudospectral discretization for efficient problem-solving.
• Comparative analysis of SPM with the Gauss Pseudospectral Method (GPM) through simulations.

Main Results:

• The proposed SPM effectively solves nonlinear optimal control problems for vehicle path tracking.
• Simulations demonstrate that SPM achieves higher accuracy in path tracking compared to GPM.
• The SPM shows greater applicability and improved vehicle maneuverability.

Conclusions:

• The Symplectic Pseudospectral Method (SPM) is a highly effective algorithm for vehicle path tracking.
• SPM offers superior accuracy and applicability, advancing autonomous vehicle control capabilities.
• This method provides a robust solution for complex nonlinear optimal control challenges in robotics.