Neural Network Control System of UAV Altitude Dynamics and Its Comparison with the PID Control System

This article proposes a comparative method to assess the performance of artificial neural network’s direct inverse control (DIC-ANN) with the PID control system. The comparison served as an analysis tool to assess the advantages of DIC-ANN over conventional control method for a UAV attitude controller. The development of ANN method for UAV control purposes arises due to the limitations of the conventional control method, which is the mathematical based model, involving complex expression, and most of them are difficult to be solved directly into analytic solution. Although the linearization simplified the solving process for such mathematical based model, omitting the nonlinear and the coupling terms is unsuitable for the dynamics of the multirotor vehicle. Thus, the DIC-ANN perform learning mechanism to overcome the limitation of PID tuning. Therefore, the proposed comparative method is developed to obtain conclusive results of DIC-ANN advantages over the linear method in UAV attitude control. Better achievement in the altitude dynamics was attained by the DIC-ANN compared to PID control method.

[1]  Wahidah Hashim,et al.  Dynamic Spectrum Allocation Scheme for Heterogeneous Network: BER Analysis , 2017 .

[2]  Yuvraj V. Parkale.  Comparison of ANN Controller and PID Controller for Industrial Water Bath Temperature Control System using MATLAB Environment , 2012 .

[3]  Benyamin Kusumoputro,et al.  Identification of Industrial Furnace Temperature for Sintering Process in Nuclear Fuel Fabrication Using NARX Neural Networks , 2014 .

[4]  M. Azizur Rahman,et al.  Online self-tuning ANN-based speed control of a PM DC motor , 1997 .

[5]  Frank L. Lewis,et al.  Dynamic inversion with zero-dynamics stabilisation for quadrotor control , 2009 .

[6]  Cheng Siong Chin,et al.  Robust and decoupled cascaded control system of underwater robotic vehicle for stabilization and pipeline tracking , 2008 .

[7]  Mohammad Behshad Shafii,et al.  Simulation and optimization of a pulsating heat pipe using artificial neural network and genetic algorithm , 2016 .

[8]  T. Thyagarajan,et al.  Modelling and control of greenhouse system using neural networks , 2018, Trans. Inst. Meas. Control.

[9]  Guang-Hong Yang,et al.  Adaptive Actor–Critic Design-Based Integral Sliding-Mode Control for Partially Unknown Nonlinear Systems With Input Disturbances , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[10]  Hyondong Oh,et al.  Indoor UAV Control Using Multi-Camera Visual Feedback , 2011, J. Intell. Robotic Syst..

[11]  Mohd Azlan Hussain,et al.  A comparative study between Neural Networks (NN)-based and adaptive-PID controllers for the optimal bio-hydrogen gas production in microbial electrolysis cell reactor , 2015 .

[12]  Aidan O'Dwyer,et al.  An Overview of Tuning Rules for the PI and PID Continuous-Time Control of Time-Delayed Single-Input, Single-Output (SISO) Processes , 2012 .

[13]  Benyamin Kusumoputro,et al.  Attitude and altitude control of a quadcopter using neural network based direct inverse control scheme , 2017 .

[14]  Eldon R. Rene,et al.  Back Propagation Neural Network Model for Predicting the Performance of Immobilized Cell Biofilters Handling Gas-Phase Hydrogen Sulphide and Ammonia , 2013, BioMed research international.

[15]  Xiaodong Zhang,et al.  A Survey of Modelling and Identification of Quadrotor Robot , 2014 .

[16]  Wang Jian,et al.  Backstepping-Based Inverse Optimal Attitude Control of Quadrotor , 2013 .

[17]  Karlisa Priandana,et al.  System identification and control of pressure process rig® system using backpropagation neural networks , 2015 .

[18]  C S Chin,et al.  Supervisory cascaded controller design: experiment test on a remotely operated vehicle , 2011 .

[19]  Jingwen Wang,et al.  Integral backstepping based nonlinear control for quadrotor , 2016, CCC 2016.

[20]  Yang Li,et al.  Adaptive Neural Network Control of AUVs With Control Input Nonlinearities Using Reinforcement Learning , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[21]  Chiu-Hsiung Chen,et al.  Intelligent transportation control system design using wavelet neural network and PID-type learning algorithms , 2011, Expert Syst. Appl..

[22]  Anthony S. White,et al.  A comparison of classical, state-space and neural network control to avoid slip , 2005 .

[23]  P. K. Sinha,et al.  Neural Networks in Autonomous Vehicle Control , 1993 .

[24]  Feng Li,et al.  An Open Data Platform for Traffic Parameters Measurement via Multirotor Unmanned Aerial Vehicles Video , 2017 .

[25]  Paul Ortiz,et al.  Trajectory tracking of a quadrotor using sliding mode control , 2016, IEEE Latin America Transactions.

[26]  Mohamed Fanni,et al.  Methodology for identifying quadrotor parameters, attitude estimation and control , 2013, 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics.