RBFNN and projection operator based robust adaptive control with input saturation of a quadrotor equipped with two manipulators constrained to a high voltage line

Abstract

The aim of this paper is to propose a robust adaptive control for a quadrotor equipped with two manipulators constrained along a high voltage line (HVL). Due to the urgent need of the HVL to inspect, the quadrotor compared to humans has many advantages such as cost and less time, higher accuracy, access to impassable points, and most importantly, reducing human victims. In this paper, two manipulators are used to hang the quadrotor from the HVL and move along it such that the quadrotor can perform monitoring operations more accurately. The motion equations are derived for the constrained quadrotor by the Lagrangian method. A control law is derived based on inverse dynamics of reduced-form equations. A robust adaptive controller is proposed for control of the constrained quadrotor in the presence of dynamic uncertainties and external disturbances. The nonlinear terms in the dynamic model are approximated by radial basis function neural network (RBFNN) and adaptive laws based on the projection operator. Moreover, saturation functions are considered for inputs in the controller by an auxiliary system. Stability analysis of the closed-loop system is performed by the Lyapunov theory. In order to confirm the effectiveness of the proposed method, some numerical simulations are carried out. The simulation results show the quadrotor can move along the HVL in the presence of the dynamic model uncertainties and the external disturbances and satisfy the considered constraints.