Design, Control and Experimental Validation of a Robust Adaptive Feedback Linearization Controller for a Quadcopter Manipulator System

Abstract

A quadcopter manipulator system (QMS) is an aerial robot comprising a quadcopter with a three degree of freedom manipulator mounted at the bottom of the vehicle. An aerial robot can reach otherwise inaccessible locations, and the manipulator can execute a variety of tasks. The presented thesis focuses on the design, control, and hardware implementation of a quadcopter manipulator system, with a robust adaptive non-linear controller. In the initial phase of the work, a thorough analysis of the workspace of the manipulator, which is mounted at the bottom of the vehicle, is presented. The proposed robotic arm has an extended workspace, allowing the end-effector to reach targets both above and below the airframe. During tasks involving interaction with walls and other structures, the drone's thrust can interact with the surroundings and cause counter moments on the system. A technique based on drone and target positions is proposed to solve this problem. A novel robust adaptive non-linear controller is designed and implemented in the second phase. With uncertain time-varying parameters, the system has coupled non-linear dynamics. A novel Augmented Adaptive Torque (AAT) control law is presented for the uncertain system, which combines a model reference adaptive controller with a feedback linearization controller. A strictly positive real-Lyapunov approach is used to create an adaptive law for estimating unknown system parameters. Lyapunov theory is used to investigate the closed-loop system's asymptotic stability. A bound on the parameter estimation error is derived utilizing the inputto- state (ISS) stability concept. The AAT control law is further combined with an estimate of the unknown bounded disturbance to create the Robust Augmented Adaptive Torque (RAAT) control law, ensuring robustness. The adaptive law is modified using a projection operator to ensure that the estimates are bounded. To validate the theoretical conclusions and corroborate the performance of the augmented adaptive torque control rule on the closed-loop system, simulations in MATLAB and ROS/Gazebo are provided. A three DoF 3D printed robotic arm is attached to an in-built quadcopter to create the QMS aerial robot, custom-built in the lab. To assess the performance of the proposed controller, real-time experiments using QMS hardware are carried out. The proposed method's efficiency is demonstrated by the aerial robot's trajectory tracking and stability during real-time testing in field experiments.