Trajectory Tracking and Obstacle Avoidance for Wheeled Mobile Robots Based on EMPC With an Adaptive Prediction...

Area of Science:

• Robotics
• Control Systems Engineering
• Artificial Intelligence

Background:

• Wheeled mobile robots (WMRs) require robust control for navigation in dynamic environments.
• Existing model-predictive control (MPC) strategies can be computationally intensive, limiting real-time applications.
• Input constraints and external disturbances pose significant challenges for WMR trajectory tracking and obstacle avoidance.

Purpose of the Study:

• To develop an event-triggered model-predictive control (EMPC) strategy for WMRs.
• To address trajectory tracking and obstacle avoidance under input constraints and external disturbances.
• To reduce the computational burden associated with traditional MPC.

Main Methods:

• Implementation of an event-triggered mechanism to minimize computational load.
• Integration of a potential field in the cost function for smooth path generation.
• Utilization of an adaptive prediction horizon for further computational reduction.
• Analysis of recursive feasibility for the optimal control problem (OCP) and practical stability of the closed-loop system.

Main Results:

• The proposed EMPC strategy effectively achieves trajectory tracking and obstacle avoidance for WMRs.
• The event-triggered mechanism and adaptive horizon significantly reduce computational requirements.
• The system demonstrates practical stability and recursive feasibility under challenging conditions.
• Simulation results validate the superiority of the developed EMPC approach.

Conclusions:

• The developed EMPC strategy offers an efficient and effective solution for WMR navigation.
• The approach successfully balances performance, computational efficiency, and robustness.
• This work provides a valuable contribution to advanced mobile robot control systems.