Tracking of trajectory and fault estimation of MIABOT robot using an artificial neural network

This article deals with the tracking of states and faults for nonlinear systems modeled using the Takagi Sugeno approach. An artificial neural network with unknown inputs is used for the purpose of tracking the system state and faults affecting the system. Firstly, the problem of state tracking is considered. After that, the proposed approach is extended to estimate actuator faults. The proposed method is applied to a robot modeled using the Takagi-Sugeno framework. Indeed, we propose on this work to estimate state and actuator fault affecting a non linear system modeled using Takagi-Sugeno structure by its application to a model of mobile robot given in the form a Takagi-Sugeno structure with 16 local models.