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Adaptive Model Predictive Control for Mobile Robots with Localization Fluctuation Estimation.

Jie Meng^1,2, Hanbiao Xiao^1,2, Liyu Jiang³

¹Intelligent Transportation Systems Research Center, Wuhan University of Technology, 1178 Heping Avenue, Wuhan 430000, China.

Sensors (Basel, Switzerland)

|March 11, 2023

Summary

This summary is machine-generated.

This study introduces an adaptive model predictive control (MPC) for mobile robots, improving trajectory tracking by accurately assessing localization fluctuations. This enhanced control system reduces errors significantly compared to traditional methods.

Keywords:

fuzzy estimation localization fluctuations mobile robots model predictive control

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Area of Science:

Robotics
Control Systems Engineering
Artificial Intelligence

Background:

Mobile robots face localization fluctuations in dynamic environments, impacting autonomous task performance.
Existing controllers often neglect these fluctuations, leading to poor trajectory tracking and instability.
This necessitates advanced control strategies for robust mobile robot operation.

Purpose of the Study:

To propose an adaptive model predictive control (MPC) for mobile robots that accurately assesses and compensates for localization fluctuations.
To balance control precision with computational efficiency in dynamic scenarios.
To enhance the stability and tracking performance of mobile robots.

Main Methods:

Developed a fuzzy logic-based localization fluctuation estimation integrating variance and entropy.
Created a modified kinematics model using Taylor expansion for linearization, accounting for disturbance.
Implemented an improved MPC with adaptive predictive step size adjustment based on fluctuation levels.

Main Results:

The proposed adaptive MPC significantly enhances localization fluctuation assessment accuracy.
The modified model and adaptive step size reduce computational burden and improve system stability.
Experimental results show a 74.3% decrease in tracking distance error and 95.3% in angle error compared to PID control.

Conclusions:

The adaptive MPC effectively addresses localization fluctuations in mobile robots.
The method offers superior performance in terms of accuracy and stability over traditional PID control.
This approach provides a robust solution for mobile robot control in dynamic environments.