Learning Control of Fixed-Wing Unmanned Aerial Vehicles Using Fuzzy Neural Networks

Abstract

A learning control strategy is preferred for the control and guidance of a fixed-wing unmanned aerial vehicle to deal with lack of modeling and flight uncertainties. For learning the plant model as well as changing working conditions online, a fuzzy neural network (FNN) is used in parallel with a conventional P (proportional) controller. Among the learning algorithms in the literature, a derivative-free one, sliding mode control (SMC) theory-based learning algorithm, is preferred as it has been proved to be computationally efficient in real-time applications. Its proven robustness and finite time converging nature make the learning algorithm appropriate for controlling an unmanned aerial vehicle as the computational power is always limited in unmanned aerial vehicles (UAVs). The parameter update rules and stability conditions of the learning are derived, and the proof of the stability of the learning algorithm is shown by using a candidate Lyapunov function. Intensive simulations are performed to illustrate the applicability of the proposed controller which includes the tracking of a three-dimensional trajectory by the UAV subject to timevarying wind conditions. The simulation results show the efficiency of the proposed control algorithm, especially in real-time control systems because of its computational efficiency.

Adaptive Neuro-Fuzzy Fusion of Multi-Sensor Data for Monitoring a Pilot’s Workload Condition

“…For basic concepts about fuzzy neural networks, see References [24,25]. Meanwhile, refer to References [26,27,28,29] for performances of fuzzy neural networks in related studies such as flight control and fault diagnosis. Furthermore, they have also gained popularity in other areas of science such as pattern recognition, data classification, image processing, and robot control [30,31,32,33,34].…”

Optimal Sensor Placement Based on Eigenvalues Analysis for Sensing Deformation of Wing Frame Using iFEM

“…Deformation sensing in real-time is an essential technology for providing feedback to the actuation and control systems of smart structures, especially of next-generation aircrafts. Moreover, when the detailed state of structural deformation is known, other necessary response quantities, such as stress and failure criteria, can also be assessed [ 1 , 2 , 3 ]. A research of deformation sensing focuses on the utilization of in situ strain measurements, captured from a network of strain sensors, to estimate the structural deformation, which is commonly referred to as shape sensing.…”

Gust Perturbation Alleviation Control of Small Unmanned Aerial Vehicle Based on Pressure Sensor

“…Researchers and engineers propose hundreds of methods to improve an SUAV's flight performance [2][3][4]. Some researchers get their inspirations from the nature, such as flying creatures like birds and insects which are capable of high-performance flight.…”