Claudio Rosales

Unmanned Aerial Vehicles (UAVs) are known to have limited payloads, which challenges their widespread use in transporting heavy goods. Meanwhile, collaboration between multiple UAVs in performing such a task may be a promising solution. To address the issues associated with the simultaneous use of UAVs, this paper presents a formation control system for transporting a payload suspended via a cable using two UAVs. The control structure is based on a layered scheme that combines a null-space-based kinematic controller with a PID controller associated with each UAV (quadcopters) with a neural correction system. The null-space supervisor controller is designed to generate the desired velocity for the UAV system to maintain formation. This proposal aims to avoid obstacles, balance the weight distribution across each vehicle, and also reduce the payload trajectory tracking error. The PID controller associated with the neural correction system receives these desired speeds and performs dynamic compensation, taking into account parametric uncertainties and dynamic disturbances caused by the movement of the payload coupled to the UAV systems. The stability analysis of the entire control system is performed using Lyapunov theory. Detailed dynamic models of each UAV in the system, the flexible cables, and the payload are presented in a realistic scenario. Finally, numerical simulations demonstrate the good performance of the UAV system control in formation.

Interest in multilift rotorcraft systems has reemerged in recent years to overcome some of the problems in single unmanned aerial vehicle handling and delivery systems. Using multiple unmanned rotorcrafts (URs), the load weight can be evenly distributed among the vehicles, extending the flight time and therefore increasing the working distance of such systems. Moreover, with a slung-load configuration, bigger size packages with complex shape and equipment that may interfere with the onboard electronics can be carried. In this context, this article presents a payload-based unified motion control that allows any number of URs to cooperatively transport a slung load in forward flight. The motion control comprises path-following and trajectory-tracking algorithms, considers obstacle avoidance, implements a specific strategy for load weight distribution (load equalization) by regulating the relative altitude of each UR, and is robust to disturbances such as wind. In addition, this work shows that load equalization is essential for slung-load cooperative transport, especially in forward flight. The controller’s stability is analytically studied and the good performance of the system is demonstrated through exhaustive simulations using validated dynamic models for mini-helicopters, cables, and payload. Finally, the relation between load weight and number of URs is analyzed by considering several scenarios.

In recent years, multiple applications have emerged in the area of payload transport using unmanned aerial vehicles (UAVs). This has attracted considerable interest among the scientific community, especially the cases involving one or several rotary-wing UAVs. In this context, this work proposes a novel measurement system which can estimate the payload position and the force exerted by it on the UAV. This measurement system is low cost, easy to implement, and can be used either in indoor or outdoor environments (no sensorized laboratory is needed). The measurement system is validated statically and dynamically. In the first test, the estimations obtained by the system are compared with measurements produced by high-precision devices. In the second test, the system is used in real experiments to compare its performance with the ones obtained using known procedures. These experiments allowed to draw interesting conclusions on which future research can be based.

Summary: In this paper, a novel adaptive PID controller for trajectory‐tracking tasks is proposed. It is implemented in discrete time over a hexacopter, and it takes into consideration the unmanned aerial vehicles (UAVs) nonlinear model. The PID controller is developed following an adaptive neural technique, and its stability is verified by the Lyapunov discrete theory. Besides, the neural identification of the dynamic model of the UAV is presented to backpropagate output errors to adjust PID gains with the purpose of reducing the control errors. The validation of the proposed algorithm is performed through experimental results with a hexacopter.

This paper presents a new dynamic obstacle avoidance strategy based on time-variation of a potential field, with multiple control objectives. The strategy uses the null space of a Jacobian matrix to achieve the different control objectives in a non-conflicting way while using either a flexible or a rigid formation to avoid static and mobile obstacles. By modifying the priorities of control objectives without changing the controller structure. Overall control system stability is analyzed and proven through Lyapunov theory. Experimental results for a three-robot formation show the performance of the proposed controllers.

This work proposes an adaptive dynamic controller for an unmanned aerial vehicle (UAV) to track a desired trajectory. Initially, reference velocities are generated by a controller that is based only on the kinematic model of the UAV. Subsequently, new control actions are calculated to compensate for the internal dynamics of the robot. Then the model parameters that characterize the robot dynamics are updated during navigation, characterizing an adaptive controller. In this way, the performance of the flight application with the quadrotor is improved, since the control errors are minimized. The stability of the proposed control system is proven, based on the Lyapunov theory. Finally, simulated results are presented, demonstrating the good performance of the controller even without any previous knowledge of the values of the parameters of the UAV dynamics.

This work presents an adaptive trajectory tracking controller for an unmanned aerial vehicle (UAV) which combines a feedback linearization controller based on a nominal model of a quadrotor and a Neuro Adaptive Compensation (NAC). The NAC is introduced in order to minimize the control errors caused by uncertainties in the nominal parameters. The uncertain parameters of the nominal model are balanced by a Neuro Adaptive Compensator. The proposed adaptive control scheme is robust and efficient to achieve a good trajectory following performance for outdoor and indoor applications. The analysis of the neural approximation error on the control errors is included. Finally, the effectiveness of the control system is proved through numerical simulation.