Attitude controller optimization of four-rotor unmanned air vehicle

Abstract

Quadrocopters, which are getting much popular day to day, are unmanned air vehicles which have four rotors to fly and maneuver in the air. They are used in military or commercial areas. Researchers are also interested in quadrocopters because their physical structure is simple; they can be modeled linear or nonlinear; and many problems about control, optimization, or artificial intelligence can be studied on it. A stable quadrocopter design requires firstly a good controller design. There are many control methods applied to quadrocopters but the most used one is the PID controller. Every controller needs to be tuned well for a good performance. Optimization algorithms are one of the popular tools for tuning the controllers. They can be used to produce optimum controller parameters with less experiment and in a short time. Each optimization algorithm has a different characteristic and different performance depending on the problem. So a comparison is needed among some popularly used optimization algorithms. In this study, a quadrocopter is modeled, and four PID controllers are designed to control attitude and hover. The optimum parameters for controllers are determined by the artificial bee colony algorithm, particle swarm optimization, and genetic algorithm. Performance of the controllers and optimization algorithms is given comparatively.

Quadrotor UAV attitude stabilization using fuzzy robust control

“…In Guerrero et al (2014), a feedback event-triggered controller was proposed, by considering a general affine nonlinear system to asymptotically stabilize the attitude dynamics, with low computational cost. In Erkol (2018), an attitude PID controller was proposed, with optimization algorithm for tuning gains, improving the closed-loop performance. In Lotufo et al (2019), an active disturbance rejection algorithm was designed, which was combined with an embedded control, based on disturbance estimation and a reference model, for the case of a single input attitude stabilization.…”

Optimización de controles mediante algoritmos metaheurísticos aplicado a vehículos aéreos

“…Existen trabajos previos como (Ustunkok and Karakaya, 2019), donde para resolver el problema de los parámetros óptimos, implementan el algoritmo meta-heurístico de nube de partículas (PSO), donde la función de costo es la sumatoria de la media de los errores absolutos; se hace una comparación en-tre el desempeño de los algoritmos de tipo P, PD y PID. Otro ejemplo es (Erkol, 2018), donde se lleva a cabo la optimización de parámetros de un controlador PID para las dinámicas de orientación y altura empleando los algoritmos de colonia de hormigas (ABC), PSO y genético (GA) y se compara contra el método de sintonización de Ziegler-Nichols. Estos trabajos aplicados únicamente a cuatro de la seis dinámicas de UAV (altitud y orientación).…”

An effective proportional-double derivative-linear quadratic regulator controller for quadcopter attitude and altitude control

“…Before the controller can be designed, first, the dynamics model of the quadcopter must be derived and studied. Basically, there are several types of formulism can be used for the derivation, but the most commonly used by the researchers are based on the Newton-Euler or Euler-Lagrange formulism [8]. Thus, in this paper, the dynamic modelling of the quadcopter was done using the Newton-Euler formulation approach.…”

“…Thus, in this paper, the dynamic modelling of the quadcopter was done using the Newton-Euler formulation approach. The system that was modelled by any approach can be controlled by various methods such as PID control [8][9][10], LQR control [11][12][13], for the linear controller or Backstepping Control (BC) [14,15], Sliding Mode Control (SMC) [16][17][18] for the nonlinear controller to name a few. However, the linear controller like PID is the most commonly used for the quadcopter control and stabilization due to its simplicity, easy to design, and can provide sufficient performance [19].…”