Development and Optimization of Non-Linear Control Strategies for Attitude Control of a UAV system

Abstract

The field of unmanned aerial vehicles (UAVs) has experienced rapid advancements, with recent innovations emphasizing the potential of bicopters due to their precision in maneuvering, energy efficiency, and capacity to perform complex tasks, such as inspection, mapping, and transportation in confined spaces. This study focuses on the development and optimization of control strategies for precise trajectory tracking of a bicopter, particularly in managing its six degrees of freedom: roll, pitch, yaw, and the x, y, and z coordinates. To address the challenges posed by the bicopter’s nonlinear dynamics, two robust control techniques—Sliding Mode Control (SMC) and Finite Time Sliding Mode Control (FTSMC)—are implemented. The performance of these controllers is further enhanced through the application of three optimization algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Red Fox Optimization (RFO). A Lyapunov stability analysis is conducted to ensure system convergence to the desired values. Comparative studies are presented both graphically and in tabular form, evaluating the optimized control laws against five performance metrics: mean absolute percentage error, root mean square error, integral square error, integral absolute error, and integral time absolute error. The results demonstrate that the FTSMC-GA approach delivers superior performance in controlling the x, y, z coordinates and yaw, while GA-optimized FTSMC and RFO-optimized SMC outperform in roll tracking. For pitch tracking, RFO-optimized SMC and FTSMC emerge as the top performers. Additionally, a controller-in-the-loop test is conducted to validate real-time implementation, with controller-in-loop validation confirming no significant deviations from simulated outcomes.