Sensor Fault Diagnosis and Fault Tolerant Control of Quadrotor UAV Based on Genetic Algorithm

Abstract

The quadrotor drone is small in size and light in weight, the mechanical structure is simple, and the requirements for the working environment are low. The development of a quadrotor UAV technology is also the focus of the current technical personnel. The author proposes a four-rotor UAV sensor fault diagnosis and fault-tolerant control based on genetic algorithm and introduces common sensor failures, and based on the improved BP neural network of the GA algorithm, the genetic algorithm is improved. This paper uses a classical BP algorithm, a classical GA-BP algorithm, and an improved GA-BP algorithm for training. Using a total of 150 sets of training data and training function using LevenbregMarquardt (trainlm), MeanSquaredError (performance function using mse), in the same noise background, the improved GA-BP algorithm has the highest detection rate, the classical GA-BP algorithm followed, and classical BP algorithm is the worst. Therefore, using the improved GA-BP algorithm, various errors of the sensor can be detected quickly and accurately.

UAV sensor failures dataset: Biomisa arducopter sensory critique (BASiC)

“…However, Hua et al. [7] introduced a fault diagnosis and fault-tolerant control system for UAV sensors based on a back-propagation neural network and genetic algorithm.…”

Analysis and System Design of Mechanical Fault Diagnosis Based on Deep Neural Network

“…ere is close cooperation between di erent parts and components of electromechanical equipment, forming an organic whole [1][2][3][4][5]. Generally, the operation environment of electromechanical equipment is complex, and it is under high-intensity working conditions for a long time [6][7][8][9][10][11]. At least, the equipment is prone to failure, and at worst, it will cause casualties and serious social harm.…”