In the last decade, quadrotors have gained popularity among industry and academia due to their capabilities and the various applications in which they can be found. In addition to the above, because this is an underactuated system, researchers have found it a great challenge to control. Despite this, there is a wide variety of methodologies in the literature to control this type of system. Based on the above, this work proposed an alternative to trajectory tracking control for quadrotor unmanned aerial vehicles (UAV). The problem was divided into two main control loops: an outer control loop for the position coordinates, tackled through linear active disturbance rejection controllers (ADRC), and an inner control loop related to the orientation variables, addressed via robust proportional-integral-differential (PID) controllers. Furthermore, a generalized proportional integral observer (GPIO) was implemented to estimate the velocity and internal and external disturbances; therefore, the control strategy only depended on the attitude (position and orientation) quadrotor measurements. Then, the control performance was tested through numerical simulations and experimental tests, including wind disturbance inputs.

This research explores the application of metaheuristic algorithms to refine parameter estimation in dynamic systems, with a focus on the inverted pendulum model. Three optimization techniques, Particle Swarm Optimization (PSO), Continuous Genetic Algorithm (CGA), and Salp Swarm Algorithm (SSA), are introduced to solve this problem. Through a thorough statistical evaluation, the optimal performance of each technique within the dynamic methodology is determined. Furthermore, the efficacy of these algorithms is demonstrated through experimental validation on a real prototype, providing practical insights into their performance. The outcomes of this study contribute to the advancement of control strategies by integrating precisely estimated physical parameters into various control algorithms, including PID controllers, fuzzy logic controllers, and model predictive controllers. Each algorithm ran 1000 times, and the SSA algorithm achieved the best performance, with the most accurate parameter estimation with a minimum error of 0.01501 N m and a mean solution error of 0.01506 N m. This precision was further underscored by its lowest standard deviation in RMSE (1.443 99 × 10⁻⁶ N m), indicating remarkable consistency across evaluations. The 95% confidence interval for error corroborated the algorithm’s reliability in deriving optimal solutions.

This paper proposes a distance-based formation control strategy with real-time disturbance rejection for omnidirectional mobile robots. The introduced control algorithm is designed such that the leader tracks a desired trajectory while the follower keeps a desired distance and formation angle concerning the leader. In the first step, the evolution of distance and formation angle is obtained from a perturbed second-order dynamic model of the robot, aided by a general proportional integral observer (GPIO), added to estimate unwanted disturbances. Then, the control law is designed for both robots via the active disturbance rejection control (ADRC) methodology, which only depends on the position, distance, and orientation measurements. A numerical simulation compared with a robust controller exhibits the system’s behavior. Furthermore, a set of laboratory experiments is conducted to verify the performance of the proposed control system, where a motion capture system is used as a proof of concept. In this context, this is considered a previous step for further experimentation with onboard sensors.