Matrix Inequalities Based Robust Model Predictive Control for Vehicle Considering Model Uncertainties, External...

Area of Science:

• Control Engineering
• Robotics
• Automotive Systems

Background:

• Vehicle control systems are susceptible to model uncertainties, external disturbances, and time delays.
• Ensuring robust stability and accurate tracking in dynamic vehicle environments is a significant challenge.

Purpose of the Study:

• To design a robust model predictive control (MPC) controller for vehicles.
• To address bounded model uncertainties, external disturbances, and time-varying delays in vehicle systems.

Main Methods:

• Utilized a Lyapunov-Razumikhin function (LRF) to guarantee system state entry into a robust positively invariant (RPI) set.
• Employed a quadratic cost function for the stage cost, establishing an upper bound for the infinite horizon cost.
• Designed a Lyapunov-Krasovskii function (LKF) candidate incorporating matrix inequality technology to bound and minimize the infinite horizon cost.

Main Results:

• The developed robust MPC controller effectively steers vehicle states to a small region around the reference tracking signal.
• Demonstrated successful performance even with the presence of external disturbances, model uncertainties, and time-varying delays.
• Simulation results validate the controller's capability in maintaining robust performance.

Conclusions:

• The proposed robust MPC controller offers a reliable solution for vehicle control under challenging conditions.
• The controller enhances tracking accuracy and stability in uncertain and dynamic environments.
• The methodology provides a framework for robust control design in automotive applications.