Comparative Analysis of Control Algorithms for Mobile Robots under Noise Conditions

Abstract

The control of mobile robots in dynamic environments poses unique challenges compared to traditional manipulators, particularly due to the influence of wheel configuration on robot dynamics. Despite extensive studies on pathfollowing algorithms, the inclusion of noise in simulations is often overlooked, which is critical for real-world applications. To address this gap, this paper provides a comprehensive evaluation of four popular control algorithms under various noise conditions, i.e., proportional-integral control, feedback linearization, Lyapunov-based control (LBC), and model predictive control (MPC). The algorithms were tested using a circular trajectory to ensure consistent and challenging conditions, and their performances were measured using the integral absolute error and mean squared error metrics. The results show that LBC and MPC offer superior robustness to noise, making them suitable for practical applications. This study contributes to the existing literature by highlighting the importance of considering noise in control algorithm evaluations and provides recommendations regarding the selection of appropriate controllers for mobile robots in noisy environments.

Keywords

• autonomous robots
• control theory
• mobile robots