Design and Experimental Validation of an Adaptive Multi-Layer Neural Network Observer-Based Fast Terminal Sliding Mode Control for Quadrotor System

• Published in: Aerospace 2024-10, Vol.11 (10), p.788
• Main Authors: Akhtar, Zainab, Naqvi, Syed Abbas Zilqurnain, Khan, Yasir Ali, Hamayun, Mirza Tariq, Ijaz, Salman
• Format: Article
• Language: English
• Subjects: Accuracy; Adaptive algorithms; Adaptive control; Algorithms; Back propagation; Back propagation networks; Computer simulation; Control algorithms; Control stability; Controllers; Design; Drone aircraft; Dynamic inversion; Dynamical systems; Effectiveness; fast terminal sliding mode controller; Flight control systems; flight controller; Global positioning systems; GPS; Kalman filters; Kinematics; Methods; multi-layer neural network; Multilayers; Neural networks; Noise propagation; Nonlinear control; Nonlinear dynamics; Numerical simulations; Parameter modification; Parameter uncertainty; Sensors; Sliding mode control; State estimation; State observers; Tracking control; Unmanned aerial vehicles; Unmanned helicopters; Velocity
• ISSN: 2226-4310; 2226-4310
• DOI: 10.3390/aerospace11100788

This study considers the numerical design and practical implementation of a new multi-layer neural network observer-based control design technique for unmanned aerial vehicles systems. Initially, an adaptive multi-layer neural network-based Luenberger observer is designed for state estimation by employing a modified back-propagation algorithm. The proposed observer’s adaptive nature aids in mitigating the impact of noise, disturbance, and parameter variations, which are usually not considered by conventional observers. Based on the observed states, a nonlinear dynamic inversion-based fast terminal sliding mode controller is designed to attain the desired attitude and position tracking control. This is done by employing a two-loop control structure. Numerical simulations are conducted to demonstrate the effectiveness of the proposed scheme in the presence of disturbance, parameter uncertainty, and noise. The numerical results are compared with current approaches, demonstrating the superiority of the proposed method. In order to assess the practical effectiveness of the proposed method, hardware-in-loop simulations are conducted by utilizing a Pixhawk 6X flight controller that interfaces with the mission planner software. Finally, experiments are conducted on a real F450 quadrotor in a secured laboratory environment, demonstrating stability and good tracking performance of the proposed MLNN observer-based SMC control scheme.