Group Coevolution and Immigration Pigeon-Inspired Optimized Dual-layer Controller for Aerial Manipulator Trajectory Tracking

Abstract

The aerial manipulator expands the scope of unmanned aerial vehicle (UAV)’s application as well as increases the difficulties in the design of the controller. To better control the aerial manipulator for different trajectories tracking under different conditions, a new dual-layer controller is designed in this paper. The integral backstepping sliding mode controller (IBSMC) is applied to the outer-loop controller and backstepping controller (BC) is applied to the inner-loop controller. To improve the performance of the system, an improved pigeon-inspired optimization (PIO) algorithm called group coevolution and immigration pigeon-inspired optimization (GCIPIO) algorithm is proposed to optimize the controller parameters of IBSMC. GCIPIO algorithm utilizes the group coevolution and immigration mechanisms. A series of simulations are conducted to show the advantage of the proposed method. The results illustrate that the proposed method ensures the closed-loop system has less end-effector tracking error.