@article {10.3844/jcssp.2021.657.669,
article_type = {journal},
title = {Fault-Tolerant Control of a Nonlinear Uncertain System: A Neural Network-Based Passive Approaches and Comparative Study with State-of-the-Art Control Approaches},
author = {Raval, Sejal K. and Patel, Himanshukumar Rajendrabhai and Shah, Vipul A.},
volume = {17},
number = {7},
year = {2021},
month = {Aug},
pages = {657-669},
doi = {10.3844/jcssp.2021.657.669},
url = {https://thescipub.com/abstract/jcssp.2021.657.669},
abstract = {This article suggests passive methods for designing Fault-Tolerant Control (FTC) for nonlinear uncertain systems with actuator and leak faults. To anticipate the Fault-Tolerant Control (FTC) action to overcome the actuator and leak faults, two-layer Feed-Forward Back-Propagation Neural Network (FFBPNN) and two-layer Cascade Forward Neural Network (CFNN) have been used, it will also tolerate external process additive disturbances. We employ the passive approach for fault-tolerant control using Proportional Integral Derivative (PID) control methodology to create a fault-tolerant controller without a fault detection mechanism. Further, we use the four residue signal features (i.e., mean, variance, skewness and normalize data of residue signal) to train the neural network in this study to tackle the issue originating from having less faults and uncertainty from residue signal. To show the efficacy of the suggested approach, simulations are run. The measurement of the residue signal was done using a healthy and a faulty uncertain non-linear system model. A comparison of findings utilizing a state of-the-art control methodology provided in (Dutta et al., 2014) was also presented to validate the proposed FTC methodology.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}