Gaussian Adaptive Mutation Pigeon-Inspired Optimized Backstepping Controller for Aerial Manipulation Trajectory Tracking

For aerial manipulation, there are many challenging problems such as coupled force control and compensation of uncertain external disturbance. In this study, the backstepping controller utilizing a full coupled dynamic model is developed to control the Unmanned Aerial Vehicle (UAV) with a manipulator. In addition, the entire system is required to track the desired trajectory when the end-effector of the manipulator is disturbed by a lightweight object. Considering the precision requirement, the control problem is transformed into a controller parameter tuning problem and as well as makes full use of the heuristic optimization algorithm to search for a set of suitable parameters. Furthermore, in view of the difficulty of parameter tuning of the backstepping controller, a Gaussian Adaptive Mutation Pigeon-Inspired Optimization (GAMPIO) algorithm is proposed. Finally, a series of simulations, compared with other optimization algorithms, are conducted to demonstrate the GAMPIO’s superiority. The results verify that GAMPIO has faster convergence and better exploration capability to make end-effector performances better in trajectory tracking.