Multi-Optimization of Novel Conditioned Adaptive Barrier Function Integral Terminal SMC for Trajectory Tracking of a Quadcopter System

Researchers in the domain of unmanned aerial vehicles (UAVs) have recently shown a great deal of interest in the quadcopter domain due to its wide variety of applications. In addition to their military applications, quadcopters are now widely used in the civilian sector. This paper presents optimized controllers for attitude and altitude control of a quadcopter system. A novel conditioned adaptive barrier function integral terminal sliding mode controller (CABFIT-SMC) is designed to address the trajectory tracking problem of the quadcopter, and a sliding mode control (SMC) is implemented for comparative analysis. Four optimization algorithms (i.e., Ant Colony Optimization, Artificial Bee Colony, Particle Swarm Optimization, and Genetic Algorithm) have been used to optimize the proposed control laws. The quadcopter’s non-linear model is formulated through the Lagrange formalism in MATLAB ODE-45, incorporating gyroscopic moments and aerodynamic effects. A Lyapunov stability analysis is carried out to verify the system’s asymptotic stability. A graphical and tabular comparative analysis is provided for all optimized control laws. Five performance indexes, including mean absolute percentage error, root mean square error, integral square error, integral absolute error, and integral time absolute error, are used to determine the best control law. The proposed optimized controllers are evaluated for performance and consistency using a 3D-helical complex trajectory. Based on the rigorous performance evaluation, it has been demonstrated that the CABFIT-SMC controller optimized with the ABC algorithm achieved the most superior results, with the lowest MAPE value of 13.245, RMSE value of 0.0043, and transient response characteristics yielding the quickest rise and settling times. The commendable performance of the ABC-optimized CABFIT-SMC further reinforces its potential as a robust controller for precise attitude, altitude, heading, and position tracking in quadcopter systems.