Integration of Fault Diagnosis and Fault-Tolerant Control for Health Monitoring of a Class of MIMO Intelligent Autonomous Vehicles

This paper deals with the integration of robust bondgraph-model-based fault diagnosis (FD) with structural recoverability analysis and fault-tolerant control (FTC) of an intelligent heavy-size and autonomous vehicle, used for loading and routing 20- and 40-ft containers inside port terminals. The overactuated vehicle is an omnidirectional mobile platform with redundant actuators such as four independent driven wheels, four independent braking wheels, and four-wheel steering systems. The supervision system is able to monitor the health condition of the vehicle and to study the fault recoverability possibilities to reconfigure the control input when a fault occurs. For FD, analytical redundancy relations (ARRs) are derived from the bond graph model. The latter is constraint relations describing the nominal system behavior and is written in terms of the measured system variables. To perform robust FD, adaptive thresholds are generated. They consider modeling and measurement uncertainties. Once a fault is detected, the structural recoverability algorithm analyzes the redundancy presented on the system, and an appropriate control strategy is applied. The designed procedure of FD and FTC is validated by considering a multiple-fault scenario on the vehicle and comparing its results with the nominal case.