Sep 2026· Journal of Undergraduate Research International· 0 citations
TL;DR
An efficient simulation-based control framework was developed, in which a nonlinear model was derived using the Newton–Euler method and subsequently linearized via feedback linearization, thereby enhancing the quadcopter stability and trajectory tracking and contributing to more reliable real-world flight performance.
Abstract
Quadcopters are widely used in modern applications; however, maintaining a stable hover and accurate path following remains challenging owing to their nonlinear dynamics. In this study, an efficient simulation-based control framework was developed, in which a nonlinear model was derived using the Newton–Euler method and subsequently linearized via feedback linearization. In MATLAB/Simulink, two proportional–integral–derivative tuning methods—manual tuning and a genetic algorithm (GA) with a population size of 30 and up to 30 generations—were employed to design the controller. Manual tuning achieved an integral square error (ISE) value of 0.03316, whereas the GA-based approach generated an ISE of 0.030056, corresponding to an average improvement of 13.9%. Furthermore, the GA-based tuning produced a smoother system response with reduced overshoot, thereby enhancing the quadcopter stability and trajectory tracking and contributing to more reliable real-world flight performance.
This paper presents a comparative study of quadrotor trajectory tracking performance using proportional integral derivative (PID) controllers optimized by two nature-inspired metaheuristic algorithms: the flower pollination algorithm (FPA) and particle swarm optimization (PSO). A nonlinear dynamic model of the quadroto...
An optimal control strategy is implemented using a genetic algorithm (GA) to operate a machine vision-equipped tracker robot. First, the governing dynamic equations of the robot system are retrieved, and then the kinematic connections are collected in accordance with the requirements of the camera. The obtained open-lo...
A. Sindhu, J. Jayalakshmi, Beulah Jabaseeli N et al.· 2026 2nd International Confe...· 0 citations
A hybrid control framework that combines a quadratic programming (QP)-based trajectory planner with a linear quadratic regulator (LQR) for disturbance rejection with the potential of combining model-based optimization and intelligent tuning for advanced crane control systems is proposed.
X. Nguyen, Viet Y Nguyen, Thi-van-Anh Nguyen· Engineering Research Express· 0 citations
This study addresses the Linear Quadratic Regulator (LQR)-based control of a triple inverted pendulum system with nonlinear, unstable, and multivariable dynamics. Since LQR performance depends on the selection of the Q and R weighting matrices, a total of nine parameters are optimized using metaheuristic algorithms. Ge...
Berivan Koç, Handan Gursoy-Demir· Çukurova Üniversitesi Mühend...· 0 citations
Quadrotor unmanned aerial vehicles (UAVs) are increasingly deployed in aerial photography, search and rescue, surveillance, and logistics applications, yet their inherently unstable and nonlinear dynamics make controller design and validation a central challenge. This paper presents the derivation of a full twelve-stat...
Mohamed Juldeh Barrie, Abdulrahman Atiku, Z. Abdi et al.· Global Journal of Engineerin...· 0 citations
This work introduces a Model Predictive Control (MPC) path tracking controller, which is developed to boost robustness, tracking precision, and vehicle stability when navigating high-speed and high-curvature driving scenarios. First, a 3-degree-of-freedom (3-DOF) dynamic model of the vehicle is established to serve as...
Hanzhengnan Yu, Xiao-Yi Hou, Hao Zhang et al.· SAE technical paper series· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
