Design of optimized nonlinear control laws for navigation of UAV systems

Abstract

In the rapidly evolving field of unmanned aerial vehicles (UAVs), the precise trajectory tracking of quadcopters has become increasingly critical. Advanced control strategies are essential to enhance the performance and reliability of these aerial vehicles. This research introduces a state-of-the-art control method known as the Terminal Super Twisting Sliding Mode Control (T-STSMC) to improve quadcopter trajectory tracking. A comparative analysis is conducted by implementing the standard Sliding Mode Control (SMC) alongside the T-STSMC. Optimal adaptive gains for these controllers are determined using the Red Fox (RO) optimization algorithm. This study utilizes a nonlinear model of the quadcopter that accounts for gyroscopic moments and aerodynamic effects, formulated within the MATLAB ODE-45/23 environment. A Lyapunov stability analysis is performed to verify the asymptotic stability of the system. This research explores a novel terminal super twisting sliding mode control (T STSMC) approach that leverages Red Fox optimization to address the computational burden associated with traditional nonlinear control laws. By training the Red Fox algorithm on performance data, we propose a cost-effective alternative that significantly reduces computational requirements. The performance of the proposed controllers is evaluated using various cost functions: Integral Square Error (ISE), Integral Absolute Error (IAE), Integral Time Absolute Error (ITAE), and Mean Square Error (MSE). The results demonstrate that T-STSMC with MSE (MSE-TSTSMC) achieves performance comparable to the original T-STSMC while requiring a significantly smaller computational footprint. A comparative analysis involving various cost functions showcases the remarkable efficacy of T-STSMC-MSE. Specifically, the simulation times for MAPE in T-STSMC MSE for roll, pitch, and yaw are 0.4987s, 0.5463s, and 0.0153s, respectively. The find ings reveal that both the MSE-TSTSMC and ITAE-TSTSMC configurations closely adhere to the reference line, indicating their superior capability in maintaining the correct yaw, roll, and pitch angles. This research significantly advances the field by demonstrating the advantages of employing heuristic Red Fox optimization in conjunc tion with advanced control strategies such as terminal-STSMC. These findings have broad implications for the future development and deployment of robust, optimized control systems not only for quadcopters but potentially for other aerial vehicles as well. The study underscores the importance of computational cost analysis for UAV control systems, promoting efficient resource utilization, enabling real-time decisionmaking, and fostering autonomy and safety in UAV operations.