Model-based fault detection and isolation for a powered wheelchair

This paper presents a model-based fault detection and isolation (FDI) for a powered wheelchair handling faults of both the internal sensors (two wheel-resolvers and a gyro) and the external sensor (a forward-looking laser range sensor), as well as actuators (two wheel motors). Hard faults of the internal sensors and actuators are diagnosed based on mode probability estimated with interacting multi-model estimator. Soft faults, which appear in the internal sensors as change of sensor gains, are diagnosed based on estimating the robot velocity with the fault-free external sensor via scan matching method. Faults of the external sensor are detected based on errors related to the scan matching. Experimental results in indoor environments show the performance of our FDI method.