Fault Tolerant Control of Mecanum Wheeled Mobile Robots

2025-12-06

AI Summary

Mecanum-wheeled mobile robots (MWMRs) are highly susceptible to both complete actuator failures (e.g., motor stall) and partial failures (e.g., torque degradation), yet existing fault-tolerant control (FTC) methods struggle to address both simultaneously. To bridge this gap, this paper proposes a posterior-probability-based dynamic weighted FTC strategy. The method online estimates fault parameters and adaptively fuses predefined control laws according to the posterior probabilities computed over multiple fault modes—enabling, for the first time, unified cooperative fault tolerance for both complete and partial failures in omnidirectional wheeled robots. Unlike conventional FTC approaches, it eliminates the need for explicit fault isolation, thereby significantly enhancing control robustness and mission reliability. Comprehensive simulations under diverse hybrid fault scenarios demonstrate that the closed-loop system maintains stable motion and achieves high task success rates.

Technology Category

• Intelligent Robots: State Estimation
• Multiagent Systems: Multiagent Systems under Uncertainty
• Machine Learning: Adversarial Learning & Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

Mecanum wheeled mobile robots (MWMRs) are highly susceptible to actuator faults that degrade performance and risk mission failure. Current fault tolerant control (FTC) schemes for MWMRs target complete actuator failures like motor stall, ignoring partial faults e.g., in torque degradation. We propose an FTC strategy handling both fault types, where we adopt posterior probability to learn real-time fault parameters. We derive the FTC law by aggregating probability-weighed control laws corresponding to predefined faults. This ensures the robustness and safety of MWMR control despite varying levels of fault occurrence. Simulation results demonstrate the effectiveness of our FTC under diverse scenarios.

Problem

• Develops fault-tolerant control for Mecanum wheel robots
• Addresses both complete and partial actuator faults
• Uses probability-based learning for real-time fault adaptation

Innovation

• Handles both complete and partial actuator faults
• Uses posterior probability for real-time fault learning
• Aggregates probability-weighed control laws for robustness