ANGULAR MOTION ESTIMATION AND ITS APPLICATION TO THE STABILIZATION OF A BALLBOT
Mechatronics Engineering, MSc. Thesis, 2016
Prof. Dr. Mustafa Ünel (Thesis Advisor), Assoc. Prof. Kemalettin Erbatur,
Assoc. Prof. Şeref Naci Engin
Date & Time: August 8th, 2016 – 10:30 AM
Keywords : angular motion estimation, Euler angles, angular rates, IMU, sensor fusion, Kalman filter, ballbot, stabilization, acceleration control, balancing control
Reliable angular motion estimation have received significant attention in recent years due to remarkable advances in sensor technologies and related requirements in many control applications including stabilization of robotic platforms. The goal of the stabilization control is to maintain the desired orientation by rejecting external disturbances.
In this thesis, a novel master-slave Kalman filter is proposed where an extended Kalman filter (EKF) and a classical Kalman filter (KF) are integrated in a master-slave configuration to estimate reliable angular motion signals including Euler angles, rates and accelerations by fusing measurements of an inertial measurement unit (IMU). Estimated angular motion signals are used as feedback in both balancing and position control of a ballbot, which is a single spherical wheeled mobile platform driven with three omniwheels. An experimental ballbot system is designed and constructed for implementing estimation and control algorithms. Furthermore, nonlinear dynamical model of the ballbot is derived using Euler-Lagrange formulation, and balancing and position controllers are designed. Robustness of the controllers is achieved by employing cascaded control loops enhanced with acceleration feedback (AFB) to provide higher stiffness to the system. Effectiveness of the proposed fusion and control algorithms are validated by several simulations and experiments where performance comparison with a conventional PD controller is also made.