Unmanned Aerial System Trajectory Tracking Based on Diversified Grey Wolf Optimization Algorithm

Trajectory tracking is one of the most important aspects of an unmanned aerial system (or quad-copter) for selecting the optimal path from the source to the destination. This article presents a mathematical framework and approach for addressing the challenge of nonlinear systems like quad-copter. A novel control system for the quad-copter’s positions
$\left ({{z,y,x} }\right)$
and attitudes (roll (
$\phi $
), yaw (
$\psi $
), pitch (
$\theta $
)) has been proposed based on optimisation techniques that are integrated with proportional-integral (PI) controllers. Astute position update methods such as helical, circular, etc. have been introduced using different algorithms like particle swarm optimization (PSO), grey Wolf optimization (GWO), and the diversified grey wolf optimizer (DGWOA) algorithm. Following that, in an iterative procedure, a variety of leadership levels are used to update the individual’s position, and the leadership is modified through the use of an adaptive mechanism. For validation, the proposed algorithm’s effectiveness is evaluated based on the convergence rate compared to that of other meta-heuristic algorithms. Owing to its inadequate exploration, PSO leads to challenges with parameter selection, whereas GWO is easy to get to the local optimum. The concept and execution of DGWOA have been implemented to update the Unmanned Aerial Systems (UAS) controlled parameters in order to overcome these limitations. The proposed algorithm’s performance for path planning in a complex and cluttered environment is investigated. The simulation shows that the DGWOA algorithm has a faster response as compared to the reference and
$\left ({{z,y,x}}\right)$
has been improved with (92.87, 96.95, and 99.69) percentage along with eliminating the shortcomings of PSO & GWO.