An Integrated System for UAV Control Using a Neural Network Implemented in a Prototyping Board

Abstract

Modern aerospace vehicles are expected to have non-conventional flight envelopes and then, in order to operate in uncertain environments, they must guarantee a high level of robustness and adaptability. A Neural Network (NN) controller, with real-time learning capability, can be used in applications with manned or unmanned aerial vehicles. In this paper a novel real-time control system, based on a NN model, in order to control the trajectories of a hexacopter is proposed. The proposed NN is optimized by the analytical calculation of the embedding parameters. The paper shows a performance evaluation, through a real experimental testbed, of the proposed approach in terms of error measures and computation of the angular velocities of the hexacopter.

Neural network-based flight control systems: Present and future

“…As a traditional model-free approach, the entire controller was modeled by a single NN [43,44], where the error back-propagation technique was typically utilized to train the NN. Although such a control scheme, in some cases, could provide an acceptable response even under severe external disturbances [45], the stability of the closed-loop system could not be mathematically analyzed [46].…”

Control Strategies and Novel Techniques for Autonomous Rotorcraft Unmanned Aerial Vehicles: A Review

“…The suggested control scheme produced an excellent performance and has the ability to implement in any obscured and non-linear dynamic framework. Artale et al [171] also carried out experimentally, a new real-time control strategy based on ANN to stabilize and track the reference trajectories of a hexacopter model. The results are seemingly and adequately promising for the planned control technique with regard to error measures and recreation of the hexacopter dynamics through its angular velocities.…”

Neural-Networks Control for Hover to High-Speed-Level-Flight Transition of Ducted Fan UAV With Provable Stability

“…where x = x(t) ∈ R n , u = u(t) ∈ R m (with n ≥ m), respectively denote the state vector, and the control input vector; Functions f : R n → R n , g : R n → R n×m , are continuously differentiable. Taking consideration of unknown system dynamics and parameter uncertainties, we divide system (14) into the following two parts: (15) wheref(x) andḡ(x) are completely known or measurable whilef(x) andg(x) contain all the uncertainties and measurement errors. Correspondingly, the control input is divided into a nominal termū and an adaptive termũ.…”

“…Thus, for the transition control of a ducted fan UAV, it is essential to deal with the nonlinearity and uncertainty of the aircraft system. Neural networks (NNs) have been widely employed to aircraft control for its excellent performance in nonlinearity matching and uncertainty compensation [14]- [18]. In [19], NNs are introduced to learn the uncertainties online and control a small quadrotor.…”