Accelerated Control Using Stochastic Dual Simplex Algorithm and Genetic Filter for Drone Application
Year: 2021
Type: article
Abstract: This article presents a new proportional-integral-derivative-accelerated (PIDA) control with a derivative filter to improve quadcopter/drone flight stability in a noisy environment. The mathematical model is derived from having an accurate model with a high level of fidelity by addressing the problems of nonlinearity, uncertainties, and coupling. These uncertainties and measurement noises cause instability in flight and automatic hovering. The proposed controller associated with a heuristic genetic filter (GF) addresses these challenges. The tuning of the proposed PIDA controller associated with the objective of controlling is performed by stochastic dual simplex algorithm. GF is applied to the PIDA control to estimate the observed states and parameters of quadcopters in both attitude and altitude. The simulation results show that the proposed control associated with GF has a strong ability to track the desired point in the presence of disturbances.
Authors: Seid Miad Zandavi, Vera Chung, Ali Anaissi
Institutions: The University of Sydney, UNSW Sydney
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4205547119