An unmanned aerial manipulator (UAM) is a novel flying robot consisting of an unmanned aerial vehicle (UAV) and a multi-degree-of-freedom (DoF) robotic arm. It can actively interact with the environment to conduct dangerous or inaccessible tasks for humans. Owing to the underactuated characteristics of UAVs and the coupling generated by the rigid connection with the manipulator, robustness and a high-precision controller are critical for UAMs. In this paper, we propose a nonsingular global fast terminal sliding mode (NGFTSM) controller for UAMs to track the expected trajectory under the influence of disturbances based on a reasonably simplified UAM system dynamics model. To achieve active anti-disturbance and high tracking accuracy in a UAM system, we incorporate an RBF neural network into the controller to estimate lumped disturbances, including internal coupling and external disturbances. The controller and neural network are derived according to Lyapunov theory to ensure the system’s stability. In addition, we propose a set of illustrative metrics to evaluate the performance of the designed controller and compare it with other controllers by simulations. The results show that the proposed controller can effectively enhance the robustness and accuracy of a UAM system with satisfactory convergence. The experimental results also verify the effectiveness of the proposed controller.

The aerial manipulator is a novel flying robot consisting of an unmanned aerial vehicle (UAV) and a multi-degree-of-freedom (DoF) robotic arm. It can actively interact with the environment to conduct dangerous or inaccessible tasks for humans. In this paper, we propose a composite control scheme considering force and position for the aerial manipulator to operate in steady contact with the environment when influenced by external disturbances. First, a contact force control method without employing the force sensor is obtained on the mechanical relationship of the system’s contact with the environment. Second, we regard the system’s internal coupling and external disturbance as lumped disturbances and design an extended state observer (ESO) to estimate them. Combined with the disturbance estimation and the nonsingular global fast sliding mode algorithm, a controller derived from the Lyapunov theory is proposed. Finally, we compare the proposed controller with the other four controllers through simulations and actual flight experiments. The results show that the proposed controller can effectively restrain disturbances, reduce convergence time, and guarantee steady contact under external disturbances.

Contact force control for Unmanned Aerial Manipulators (UAMs) is a challenging issue today. This paper designs a new method to stabilize the UAM system during the formation of contact force with the target. Firstly, the dynamic model of the contact process between the UAM and the target is derived. Then, a non-singular global fast terminal sliding mode controller (NGFTSMC) is proposed to guarantee that the contact process is completed within a finite time. Moreover, to compensate for system uncertainties and external disturbances, the equivalent part of the controller is estimated by an adaptive radial basis function neural network (RBFNN). Finally, the Lyapunov theory is applied to validate the global stability of the closed-loop system and derive the adaptive law for the neural network weight matrix online updating. Simulation and experimental results demonstrate that the proposed method can stably form a continuous contact force and reduce the chattering with good robustness.

Unmanned Aerial Manipulation (UAM) is a novel type of Unmanned Aerial Vehicle (UAV) equipped with manipulators instead of manual operation in hazardous and unreachable environments. The combination of UAV and manipulator unavoidably causes a significant predicament due to the increase of nonlinearity and coupling of the UAM system. Consequently, the system’s robustness becomes more vulnerable in the presence of system uncertainty and external disturbance. In addition, as a real-time embedded system, rapid and precise tracking of the desired trajectory is an essential aspect of UAM performance. This study aims to establish the dynamic model of UAM and propose a global fast terminal sliding mode controller for trajectory tracking. The controller is derived from Lyapunov theory to ensure the stability of the closed-loop system. We propose a set of illustrative metrics to evaluate the performance of the designed controller and compare it with the other two controllers by simulation. The results show that the proposed controller can effectively reduce the convergence time of tracking error and has good robustness and mechanical properties. And experimental results also verified its effectiveness.