Design and Experimental Development of Wireless Iterative Learning Fault Estimation Algorithm With Quantization and Packet Losses

In this paper, a wireless iterative learning fault estimation algorithm (WILFEA) is proposed and validated experimentally with the aim to achieve perfect tracking of a prescribed reference trajectory for systems with packet losses and quantizer measurements that operate repetitively. First, state variables, Markov chain process of random packet losses, and a logarithmic quantizer are considered to establish an extended-state-space system model. Next, based on this model, sufficient conditions for linear repetitive processes are developed with the Lyapunov-Krasovskii technique and
$H_{\infty }$
approach is applied to calculate the observer gain and the learning gain. Then, WILFEA based fault estimation is constructed. Compared with the existing methods, the proposed WILFEA improves the fault estimation performance in the current iteration by consider both state error and fault estimation error. Finally, the simulation and experimental results are used for DC-Servomotor system to illustrate the effectiveness of the proposed approach using Matlab/simulink software, LabVIEW Software, ZigBee Xbee and Arduino board.