Intelligent Control for Unmanned Aerial Systems with System Uncertainties and Disturbances Using Artificial Neural Network

Stabilizing the Unmanned Aircraft Systems (UAS) under complex environment including system uncertainties, unknown noise and/or disturbance is so challenging. Therefore, this paper proposes an adaptive neural network based intelligent control method to overcome these challenges. Based on a class of artificial neural network, named Radial Basis Function (RBF) networks an adaptive neural network controller is designed. To handle the unknown dynamics and uncertainties in the system, firstly, we develop a neural network based identifier. Then, a neural network based controller is generated based on both the identified model of the system and the linear or nonlinear controller. To ensure the stability of the system during its online training phase, the linear or nonlinear controller is utilized. The learning capability of the proposed intelligent controller makes it a promising approach to take system uncertainties, noises and/or disturbances into account. The satisfactory performance of the proposed intelligent controller is validated based on the computer based simulation results of a benchmark UAS with system uncertainties and disturbances, such as wind gusts disturbance.

[1]  Seyyed Hamid Elyas,et al.  Optimal tuning of Brain Emotional Learning Based Intelligent Controller using Clonal Selection Algorithm , 2013, ICCKE 2013.

[2]  Shamik Sengupta,et al.  Adaptive Flocking Control of Multiple Unmanned Ground Vehicles by Using a UAV , 2015, ISVC.

[3]  Radoslaw Romuald Zakrzewski,et al.  Neural network control of nonlinear discrete time systems , 1994 .

[4]  Holger Voos,et al.  Controller design for quadrotor UAVs using reinforcement learning , 2010, 2010 IEEE International Conference on Control Applications.

[5]  Thorsten O. Zander,et al.  A Survey on Unmanned Aerial Vehicle Remote Control Using Brain–Computer Interface , 2018, IEEE Transactions on Human-Machine Systems.

[6]  S. Islam,et al.  Adaptive sliding mode control design for quadrotor unmanned aerial vehicle , 2015, 2015 International Conference on Unmanned Aircraft Systems (ICUAS).

[7]  George J. Vachtsevanos,et al.  Handbook of Unmanned Aerial Vehicles , 2014 .

[8]  Hao Xu,et al.  Brain Emotional Learning-Based Intelligent Controller for flocking of Multi-Agent Systems , 2017, 2017 American Control Conference (ACC).

[9]  Bin Xian,et al.  Nonlinear robust output feedback tracking control of a quadrotor UAV using quaternion representation , 2015 .

[10]  Martin T. Hagan,et al.  Neural networks for control , 1999, Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251).

[11]  Tammaso Bresciani,et al.  Modelling, Identification and Control of a Quadrotor Helicopter , 2008 .

[12]  Sergio Dominguez,et al.  L1 adaptive control for Wind gust rejection in quad-rotor UAV wind turbine inspection , 2017, 2017 International Conference on Unmanned Aircraft Systems (ICUAS).

[13]  Jooyoung Park,et al.  Universal Approximation Using Radial-Basis-Function Networks , 1991, Neural Computation.

[14]  Hao Xu,et al.  A neurobiologically-inspired intelligent trajectory tracking control for unmanned aircraft systems with uncertain system dynamics and disturbance , 2019 .

[15]  Yu Zhang,et al.  Adaptive Neural Control of a Quadrotor Helicopter with Extreme Learning Machine , 2015 .

[16]  Saeed Bagheri Shouraki,et al.  Attitude control of a Quadrotor using Brain Emotional Learning Based Intelligent Controller , 2013, 2013 13th Iranian Conference on Fuzzy Systems (IFSC).

[17]  Subodh Bhandari,et al.  Neural network based nonlinear model reference adaptive controller for an unmanned aerial vehicle , 2017, 2017 International Conference on Unmanned Aircraft Systems (ICUAS).

[18]  Mohammad Jafari.  On the Cooperative Control and Obstacle Avoidance of MultiVehicle Systems , 2015 .

[19]  Jianxiang Xi,et al.  Robust attitude controller design for miniature quadrotors , 2016 .

[20]  C. Cozaa,et al.  Adaptive fuzzy control for a quadrotor helicopter robust to wind buffeting , 2012 .

[21]  M. Teshnehlab,et al.  Speed control of a Digital Servo System using parallel distributed compensation controller and Neural Adaptive controller , 2013, 2013 13th Iranian Conference on Fuzzy Systems (IFSC).

[22]  Rogelio Lozano,et al.  Quad Rotorcraft Control: Vision-Based Hovering and Navigation , 2012 .

[23]  Isaac Chairez,et al.  Neuro-fuzzy controller for attitude-tracking stabilization of a multi-rotor unmanned aerial system , 2017, 2017 International Conference on Unmanned Aircraft Systems (ICUAS).