Fault-Tolerant Control of a Nonlinear Uncertain System: A Neural Network-Based Passive Approaches and Comparative Study with State-of-the-Art Control Approaches

Sejal K Raval,Vipul A Shah,Himanshukumar Rajendrabhai Patel

doi:10.3844/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.

Highlights

Fault Tolerant Control (FTC) is a research area for industrial processes that aims to preserve satisfactory control performance and system stability under faulty conditions (Patel and Shah, 2018a)
Because the Feed-Forward Back-Propagation Neural Network (FFBPNN) is a supervised machine learning technique, it’s best to divide the data into precise training and testing ratios
We employed two alternative neural networks in the proposed work: One configuration is FFBPNN with four inputs, two hidden layers with 10 and 5 neurons and one output neurons, while the other configuration is Cascade Forward Neural Network (CFNN) with four inputs, one output neurons and three cascade layers

Summary

Introduction

Fault Tolerant Control (FTC) is a research area for industrial processes that aims to preserve satisfactory control performance and system stability under faulty conditions (Patel and Shah, 2018a). For FTC, there are two primary structured approaches: Active FTC and passive FTC (Patel and Shah, 2018d) Passive FTC has designed a robust controller for the system based on predetermined conditions and magnitude of system faults. In an active FTC approach, Fault Detection and Diagnosis (FDD) is a key component required. FDD has three important functions that begin with the detection of faults in the process, isolation and identification (Patel and Shah, 2018b; 2019d). The second major reason is that the operational principles are understood and reasonably simple to explain to practitioners, which appears to be a significant factor in the introduction of a new control scheme in industry

Methods

Results

Conclusion