Optimized Conditioned Adaptive Barrier Function Integral Terminal SMC for Trajectory Tracking of a Quadcopter System via Quantum PSO Algorithm

Quadcopters, as a subset of unmanned aerial vehicles (UAVs), have seen a surge in research interest given their vast application potential in civilian and military arenas. This paper introduces an optimized approach to attitude control in quadcopters. An innovative Conditioned Adaptive Barrier Function Integral Terminal Sliding Mode Controller (CAB FIT-SMC) has been developed to tackle the problem of trajectory tracking in the quadcopter. Moreover, a sliding mode control (SMC) is instituted for comparative analysis. The optimization of these control laws is accomplished using the quantum particle swarm optimization (QPSO) algorithm. This algorithm blends the principles of particle swarm optimization (PSO) and quantum mechanics, offering a metaheuristic approach to optimization. The nonlinear model of the quadcopter has been framed utilizing the Lagrange formalism, integrated with MATLAB's ODE-45, and accounts for gyroscopic moments and aerodynamic effects. The system's asymptotic stability is verified through a Lyapunov stability analysis. The paper includes a comparative analysis of all optimized control laws through graphical and tabular comparative analysis. The efficacy of the control law is evaluated against six performance metrics, namely mean absolute percentage error, root mean square error, integral square error, integral absolute error, integral time absolute error, and integral time square error, to identify the superior control law. A 3D-helical complex trajectory is utilized for evaluating the performance and consistency of the proposed optimized controllers. The results indicate that the CABFIT-SMC controller, optimized with the QPSO algorithm, outperforms the optimized SMC across all performance metrics, thus proving its effectiveness for attitude tracking of a quadcopter system.