A control strategy for enabling a team of unmanned aircraft systems (UASs) to cooperatively transport a cable-suspended rigid-bodypayload of unknown characteristics is proposed. Considering the rigid-body payload as a disturbance with unmodeled dynamics affecting the aircraft, a fuzzy approximator is designed to identify the lumped disturbances existing in the system, which include also the forces of interaction between the aircraft. Next, a nonlinear controller is designed, which makes use of the approximator to compensate such disturbances. Making use of the fuzzy approximator and the nonlinear controller, a novel consensus-type strategy is proposed, enabling the load transportation task by means of two cooperative quad rotorcraft UASs. The stability of the proposed methodology is demonstrated making use of Lyapunov theory. The nonlinear consensus controller enhanced with fuzzy compensation is compared with respect to a feedback controller with feedforward compensation, demonstrating superior performance of the proposed technique in terms of robustness and stability. Experimental results demonstrate the applicability of the theoretical framework for executing the real-time transportation of a cable-suspended rigid-body payload by means of a team of two cooperative quad rotor UASs.

The development of quadrotor unmanned aerial vehicles (QUAVs) is a growing field due to their wide range of applications. QUAVs are complex nonlinear systems with a chaotic nature that require a controller with extended dynamics. PD and PID controllers can be successfully applied when the parameters are accurate. However, this parameterization process is complicated and time-consuming; most of the time, parameters are chosen by trial and error without guaranteeing good performance. The originality of this work is to present a novel nonlinear mathematical model with aerodynamic moments and forces in the Newton–Euler formulation, and identify metaheuristic algorithms applied to parameter optimization of compensated PD and PID controls for tracking the trajectories of a QUAV. Eight metaheuristic algorithms (PSO, GWO, HGS, LSHADE, LSPACMA, MPA, SMA and WOA) are reported, and RMSE is used to measure each dynamic performance of the simulations. For the PD control, the best performance is obtained with the HGS algorithm with an RMSE = 0.037247252379126. For the PID control, the best performance is obtained with the HGS algorithm with an RMSE = 0.032594309723623. Trajectory tracking was successful for the QUAV by minimizing the error between the desired and actual dynamics.

Unmanned aerial vehicles (UAVs) facilitate complex activities and are widely used for aerial transport. Quadrotor UAVs (QUAV), the most popular UAV containing four motors, are characterised by higher control properties since they have fewer actuators than degrees of freedom, implying a nonlinear underactuated system. In addition, the coupling of dynamics, flaws while modelling and parameter uncertainty are the factors that hinder the design and implementation of a controller. Here, we present the modelling, optimisation, simulation, and implementation methodology for controllers, proportional-integralderivative (PID), and super-twisting-sliding mode control (ST-SMC). We carry out the parameterisation problem of controllers using the hunger game search (HGS) metaheuristic algorithm. This process was developed offline, and the values obtained were successfully implemented in simulation and experimental form. The testing platform comprises a motion capture system, Vicon® Bonita cameras, linked by ROS, that allows the known position and the attitude of a Parrot® QUAV bebop1. The whole six dynamics of the QUAV are included in the implementation, translational trajectories X-Y are trapezoidal, and the altitude trajectory is a ramp. The results enabled the comparison of the statistics calculation of each controller. Successful tracking trajectories were obtained even with disturbance when the ST-SMC algorithm was implemented with root mean square error (RMSE)=0.0176.

Sustainability is one of the grand challenges of humanity. This urgency has driven alternatives such as using microalgae to capture and transform carbon dioxide (CO ₂ ) into valuable products. Although all biotechnological processes face several challenges such as scaling up, on-line monitoring, and control of critical biological variables (pH, temperature). For photobioreactors other variables such as light intensity which commonly affects growth phenotypes, are critical to have more efficient bioreactor operations while decreasing shading effects. This work describes the design and development of a low-cost monitoring and control system designed to automate the measuring process of meaningful variables in a photobioreactor. The proposed system is based on Raspberry Pi 4 microcomputer programming with the open-source programming language Python. The performance of the system was evaluated in a bubble column over a ten-day course of growth of the algae Haematococcus lacustris . As expected, the cost of this monitoring system was only the 6% of the average potential cost found in the commercial market. The system has other advantages for example that it is portable and can be installed in different types of photobioreactors (airlift, stirred tank, raceway). Moreover, all collected data was visualized in the display and stored for external analysis in compatible formats. When the algae H. lacustris was inoculated and the variables such as pH and volume started to change we found that our system achieved comparable performance to external sensors (with a measurement error of less than 1%) while attaining final biomass densities of up to 1, which is remarkably higher than the obtained in flasks cultures (0.1). Development of reliable low-cost monitoring systems facilitates the expansion of operation to precision and parallel culturing of light-driven biotechnology processes.

El trabajo reporta la evaluacion de desempeño de cuatro algoritmos metaheurísticos para optimizar el control de velocidad de un motor de imanes permanentes (MIP); el controlador tiene integrados tres controles PI, en consecuencia, hay que sintonizar seis ganancias. Los algoritmos considerados; son enjambre de partículas conocido como (PSO), evolucion diferencial mejorada (LSHADE), optimizacion de búsqueda dorada (GSO), y axolote mexicano (MAO). El rendimiento de los controles del motor se evalua sometíendolos a cambios en la referencia de velocidad y a perturbaciones en el par de carga. LSHADE obtiene el mejor rendimiento con un valor mínimo de la funcion de coste de 1.1474, le sigue el PSO convencional con un valor de 2.9567, el MAO con un valor de 13.09, y el GSO con 266.63. Aunque los algoritmos GSO y MAO son tecnicas de optimización recientes, no han conseguido mejorar a los algoritmos PSO y LSHADE para este caso concreto de regulacion de velocidad MIP.