A novel fault classification‐based fault‐tolerant control for two degree of freedom helicopter systems. (9th June 2020)

• Record Type: Journal Article
• Authors: Singh, Rupam; Bhushan, Bharat
• Is Part Of: International journal of adaptive control and signal processing. Volume 34:Number 8(2020)
• Page Start: 1080
• Page End: 1104
• Publication Date: 2020-06-09
• Subjects: 2DoF helicopter -- discrete wavelet transform -- fault detection and diagnosis -- fuzzy‐proportional integral derivative -- multilayer perceptron neural network; Adaptive control systems -- Periodicals; Adaptive signal processing -- Periodicals; 629.836
• DOI: 10.1002/acs.3121
• Languages: English
• ISSNs: 0890-6327

Abstract

Summary: Fault detection and diagnosis (FDD) plays an essential role in identifying and isolating various faults in a system. In general, fault detection is attained by monitoring the extent of matching between the actual operating condition and an analytical model prediction. This process aids in achieving enhanced performance, and for operating the system within the acceptable bounds. In this article, a neural network‐based classification method and fuzzy‐based control strategy are adapted to perform FDD on a two degree of freedom (2DoF) helicopter system. The operating voltage, pitch, and yaw outputs of the 2DoF helicopter system were considered for developing the algorithm. The signal processing properties of the discrete wavelet transform and pattern recognition properties of a multilayer perceptron neural network are adapted to design the classification algorithm. The developed algorithm improves training and testing efficiency. In order to reinstate the normal operation of the system, the classifier output is integrated with a hybrid fuzzy‐proportional integral derivative controller. This control technique enhances the 2DoF helicopter response as the time taken by the pitch and yaw angle to settle trajectory is reduced. The results depicted validate the efficiency of the projected approach.