Online Tuning of Koopman Operator for Fault-Tolerant Control: A Case Study of Mobile Robot Localising on Minimal Sensor Information - Ravi Kiran Akumalla; Tushar Jain (Machines)
Self-localisation is a critical concept in the context of autonomous navigation and control of mobile robots. The most prevalent method for localisation is sensor fusion. Nevertheless, there are certain situations where the robots are compelled to localise on minimal sensor information. Furthermore, the key challenge is determining how to localise if this minimal sensor information fails. This paper proposes a data-driven analytical redundancy technique to address this challenge in wheeled mobile robots. Initially, the localisation of the robot is performed using the encoder information alone to create a minimalistic approach. In such a situation, a fault or failure in the encoders makes the robot behave in an undesirable way. To mitigate this, we are proposing a method to use the information from the analytical models when a fault is detected. Specifically, we obtain the analytical models through data-driven techniques. By a step response experiment, the input voltage and output angular velocity data of the motor are collected. We then use the System Identification toolbox in MATLAB® (ver R2025a) and the Koopman framework to obtain different analytical models using the same data. We observe that these models experience errors at different input voltages of the motor, affecting the proposed method for handling the encoder fault. So, in this work, we use online tuning of the Koopman operator and experimentally demonstrate its effectiveness in handling the sensor fault on a mobile robot localising with minimal information.