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

• Published in: IEEE access Vol. 9; pp. 150120 - 150127
• Main Authors: Herve, Samba Aime, Aurelien, Yeremou Tamtsia, Leandre, Nneme Nneme, Som, Idellette Judith Hermine, Alphonse, Houwe
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Delays; Estimation; fault estimation; Heuristic algorithms; Iterative algorithms; Iterative learning algorithm; Iterative methods; Machine learning; Markov chains; Packet loss; quantiser measurements; Quantization (signal); random packet losses; Servomotors; Software; Wireless communication
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3123118

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 <inline-formula> <tex-math notation="LaTeX">H_{\infty } </tex-math></inline-formula> 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.