An adaptive hierarchical control for aerial manipulators

Abstract

SUMMARYThis paper addresses the trajectory tracking control problem for a quadrotor aerial vehicle, equipped with a robotic manipulator (aerial manipulator). The controller is organized in two layers: in the top layer, an inverse kinematics algorithm computes the motion references for the actuated variables; in the bottom layer, a motion control algorithm is in charge of tracking the motion references computed by the upper layer. To the purpose, a model-based control scheme is adopted, where modelling uncertainties are compensated through an adaptive term. The stability of the proposed scheme is proven by resorting to Lyapunov arguments. Finally, a simulation case study is proposed to prove the effectiveness of the approach.

Preliminary design, modeling and control of a fully actuated quadrotor UAV

“…In this Section, a model-based motion control scheme for the ODQuad is developed. The control scheme is inherited from the approach developed in [5] for aerial manipulators and is based on a two-loop approach. The outer loop implements the position controller: its output is the force to be applied to the vehicle's platform required to achieve the desired vehicle's position trajectory and the reference angles for the gimbal joints.…”

“…Since, in practice, α d and β d are affected by noise, the desired velocities and accelerations are to be computed by using suitable robustto-noise filters. A possible approach is detailed in [5], where the time-varying filter proposed in [18] is adopted in order to compute both the first and second time-derivatives of the reference values. The commanded moment to be applied to the platform and the joint torques can be computed as in (34)…”

“…Moreover, it is assumed that measurements of the angular positions of the two joints are available as well, while velocities are obtained via numerical filtering of the corresponding variables. Measurement noise has been not introduced, to better highlight the intrinsic tracking performance of the proposed controller; in the presence of the measurement noise, however, the approach in [18] can be adopted, wich has been proven to be effective (see, e.g., the results in [5]).…”

“…Vertical Take Off and Landing (VTOL) Unmanned Aerial Vehicles (UAVs) offer several advantages over standard mobile robots operating on ground in many application scenarios (e.g., inspection and manteinance services [1], data collection and exploration operations [2], precision agriculture [3]). More recently, aerial manipulators (i.e., UAVs equipped with grippers or robotic arms) opened up a new stream of applications, e.g., in assembly of structures in remote environments, maintenaince operations and payload transportation [4], [5].…”

Aerial Grasping of an Object in the Strong Wind: Robust Control of an Aerial Manipulator

“…Thus, the aerial manipulator system control has attracted considerable attention, and a variety of control methods have been used in aerial manipulator systems. These include adaptive control [11][12][13], back-stepping [14,15], model predictive control (MPC) [16][17][18], impedance control [19,20], and so on. In one study, in order to carry and transport an unknown payload, an adaptive controller was designed for an aerial manipulator, composed of a quadrotor and a 2-DoF of robotic arm, which can estimate parameters of the payload online and track the desired trajectory [12].…”