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

Abstract

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.

Improved Altitude Control Algorithm for Quadcopter Unmanned Aerial Vehicles

“…In other studies [18][19][20], the second order SMC method was also used to improve quadcopter altitude control performances. Yet another method was presented by Muliadi et al [21], where the authors proposed a neural network approach to control UAV altitude dynamics. The results obtained with this method were verified through comparisons with a conventional proportional-integral-derivative (PID) control system.…”

“…The results obtained with this method were verified through comparisons with a conventional proportional-integral-derivative (PID) control system. However, these approaches [15][16][17][18][19][20][21] have a common disadvantage in that the SMC technique generates a high chattering control signal method which reduces the lifetime of the entire system.…”

“…Quadcopters are an important class of UAVs that are superior in many ways: they have a simple structure, are quick to manufacture, and are inexpensive [4,5]. Controlling their position, in general, and altitude, in particular, has been the subject of numerous studies [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23] with ever more advanced algorithms having been proposed to address the quadcopter control problem.…”

Neural Network and Fuzzy-logic-based Self-tuning PID Control for Quadcopter Path Tracking

“…where μ is the input vector, e is the error vector and ec is the changing rate of system error expressed in (14).…”

“…To solve the stabilization problem of quadrotor, different nonlinear control learning algorithms have been developed. The fuzzy logic control (Harik et al, 2017, Mardan et al, 2017, Gül & Arıso, 2013 and the neural network controller (Çakir & Yüksel, 2017, Muliadi & Kusumoputro, 2018, Lei et al,2014 are used to build and design an intelligent flight control module for a quadrotor UAV, and they are widely used for nonlinear plants due to their characteristic of self-learning, self-organizing and self-adapting (Belhadri et al, 2016). So, combining the characteristics of PID/PD controllers with intelligent algorithms represents a very promising technique for a nonlinear system.…”