Feedforward Neural Network Controller for Quadrotor in the Presence of Payload and Wind Disturbances

Abstract

Recently, Artificial Neural Networks (ANN) have been used in broad research fields such as control system engineering. Inspired by the human brain system, this algorithm uses the learning process to improve system performance using the training dataset. The ANN algorithm is based on a weighted connection between the neurons to deal with the controller design. These connection parameters represent one of the crucial elements necessary to apply the learning process efficiently. However, finding the connection parameters becomes problematic when the controlled system is complex, nonlinear, and subjected to external disturbances. This work uses the most available ANN architecture called the Feedforward Neural Network to stabilize the quadrotor system in reference trajectory tracking when subjected to model payload and Dryden turbulent wind. A Deterministic Algorithm (DA) trains the FNN weight and bias without initialization with another training method to improve the control performance. Besides, three simulation tests are performed to control the quadrotor system. The first simulation uses classical PID to collect the necessary training data for the learning process. Secondly, the DA algorithm trains the FNN parameters to design the controllers necessary to stabilize the quadrotor in-flight tests. The third simulation uses the most common training method: the Levenberg-Marquardt Backpropagation (LMB) algorithm to tune the FNN parameters. The final simulation tests these control methods' efficiency when the quadrotor system is subjected to the model payload and the Dryden turbulent wind. A comparison test between the proposed DA-based feedforward neural network, the classical PID, and the LMB-based feedforward neural network is performed in the nominal case (without disturbance) and in the case of adding disturbance. The test result is obtained using Matlab software and demonstrates much accuracy and efficacity of the proposed DA-based FNN controller compared to the other methods. This result considers the proposed control approach an additional innovative method-based neural network to stabilize the quadrotor's Unmanned Aerial Vehicles (UAV).