Abstract
Quadrotor UAVs are one of the most preferred types of small unmanned aerial vehicles, due to their modest mechanical structure and propulsion precept. However, the complex non-linear dynamic behavior of the Proportional Integral Derivative (PID) controller in these vehicles requires advanced stabilizing control of their movement. Additionally, locating the appropriate gain for a model-based controller is relatively complex and demands a significant amount of time, as it relies on external perturbations and the dynamic modeling of plants. Therefore, developing a method for the tuning of quadcopter PID parameters may save effort and time, and better control performance can be realized. Traditional methods, such as Ziegler–Nichols (ZN), for tuning quadcopter PID do not provide optimal control and might leave the system with potential instability and cause significant damage. One possible approach that alleviates the tough task of nonlinear control design is the use of meta-heuristics that permit appropriate control actions. This study presents PID controller tuning using meta-heuristic algorithms, such as Genetic Algorithms (GAs), the Crow Search Algorithm (CSA) and Particle Swarm Optimization (PSO) to stabilize quadcopter movements. These meta-heuristics were used to control the position and orientation of a PID controller based on a fitness function proposed to reduce overshooting by predicting future paths. The obtained results confirmed the efficacy of the proposed controller in felicitously and reliably controlling the flight of a quadcopter based on GA, CSA and PSO. Finally, the simulation results related to quadcopter movement control using PSO presented impressive control results, compared to GA and CSA.
Highlights
There has been an upward trend in the popularity of Unmanned AerialVehicles (UAVs) in a wide range of practical applications
We present the evaluation results of the conventional ZN method along with the results of Genetic Algorithms (GAs), Crow Search Algorithm (CSA) and Particle Swarm Optimization (PSO) in adjusting the Proportional Integral Derivative (PID) parameters while controlling the movements of the quadcopter and provides an overall analysis of the varied computational time elapsed by these algorithms
The convergence curves of GA, PSO and CSA used to accomplish the optimization process presented in this work are presented, using different population sizes to demonstrate the ability of these algorithms to provide accurate control for the quadcopter movements through tuning the PID parameters of the quadcopter
Summary
There has been an upward trend in the popularity of Unmanned AerialVehicles (UAVs) in a wide range of practical applications. There has been a growing interest in quadcopter applications, such as object detection and landing to drone stations [1], rescue and disasters [2], agriculture [3], and even other tasks, such as those reported in [4]. It is critically essential for the success of the missions performed that the UAVs precisely and rapidly follow the required path in civilian purposes, such as logistics, mapping, search and rescue, surveillance, exploration and many more military missions, such as attack, defense, supervision and surveillance [5,6]
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