Adaptive Prescribed Performance Control of Unmanned Aerial Manipulator With Disturbances

This article presents the problem of autonomous control of an unmanned aerial manipulator (UAM) developed for operation with unknown disturbances, wherein the disturbances from the coupling effect between the UAM and the external environment need to be considered. Regarding the coupling force as a disturbance to the entire UAM system, an adaptive prescribed performance control (APPC) scheme utilizing the knowledge of prescribed performance is proposed to guarantee the transient and steady-state performance responses. Also, an adaptive law is designed to estimate the upper boundary parameters of the UAM system uncertainties and disturbances, wherein the restrictive constant boundary assumptions and the prior information of the upper bound are not required in the controller design. Furthermore, to enable safe manipulation in a realistic situation, an end-effector trajectory generation method is presented satisfying the joint angle limitation. For the validation of the proposed method, the simulation results of numerical simulation comparisons are shown. Moreover, experimental scenarios including stable flight and simulated co-work with humans in complex environments are designed to verify the proposed method.
Note to Practitioners
—This article is motivated by the problem of aerial manipulation under unknown disturbances, which may be caused by the wide movement of the manipulator and the sudden loading or unloading of an object. Existing approaches for aerial manipulation often require the assumption of a constant or slowly varying external disturbance. However, a priori bounded disturbance might impose a priori bound on the system state before obtaining closed-loop stability. In this article, the proposed controller with an adaptive law is designed to estimate the upper boundary parameters of the overall disturbances and ensure the predefined performance, so that the prior information of the upper bound of disturbances is not required. The performance of the proposed control strategy is demonstrated via numerical simulation comparisons and experiments, including stale flight and simulated co-work with humans in a complex environment.