A robust fault detection scheme with an application to mobile robots by using adaptive thresholds generated with locally linear models

In a fault detection system, generating residuals is the first step in detecting faults. However, residuals are not the only element of a dependable fault detection system. A fault detection system is reliable when an appropriate residual evaluation criterion is used along with a suitable residual generation technique. In this paper, a new method for an adaptive threshold generation is proposed to improve evaluation of the residuals with application to a trajectory following of an unmanned mobile robot. The proposed solution is useful when local linear models are utilized as observers for residual generation. For this purpose, locally linear model tree algorithm equipped with an external dynamics is applied as a powerful nonlinear identifier scheme to model the system. To demonstrate the capability of our proposed concept a complete model of a two wheeled mobile robot which is capable of implementing most possible faults in the system is developed. Detailed simulation results demonstrate the feasibility of our proposed methodology.