Enhanced Nonlinear Control for Trajectory Tracking Control of a Quad-Copter System Using Redfox Algorithm

Quad-copters continue to be an area of active research due to their extensive applications in both civilian and military domains. In this paper, we present an advanced approach to enhance the attitude control of Quad-copters, focusing on trajectory tracking performance. We introduce a state-of-the-art Conditioned Adaptive Barrier Function Integral Terminal Sliding Mode Controller (CABFIT-SMC) for precise attitude control. To optimize the control law parameters effectively, we introduce the Redfox algorithm, a newly developed optimization technique inspired by the intelligence of red foxes in hunting and decision-making. The paper includes an in-depth comparative analysis of the Redfox-optimized CABFIT-SMC with the previously researched quantum particle swarm optimization (QPSO) algorithm, presented in our earlier work. The evaluation involves comparing graphs and tables for six different performance measures. These include mean absolute percentage error, root mean square error, integral square error, integral absolute error, integral time absolute error, and integral time square error. We confirm the stability of the system using Lyapunov stability analysis. To test how well the controller works, we use a challenging 3D-helical trajectory. This helps us see if the optimized controllers perform consistently and effectively. Furthermore, we validate the controllers using a Controller-in-Loop setup, demonstrating their effectiveness under realistic operating conditions. Our results demonstrate the CABFIT-SMC Redfox optimized outperforms QPSO-optimized CABFIT-SMC across all performance metrics, solidifying its effectiveness for precise attitude tracking of Quad-copter systems. The proposed approach contributes to improved maneuverability and control precision, with potential applications in various practical scenarios.