2축 짐벌 구조 적재 장치를 위한 최소제곱법 기반 시스템 식별
Least Squares Method-Based System Identification for a 2-Axes Gimbal Structure Loading Device
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초록
This study shows a system identification method of a balancing loading device for a stair climbing delivery robot. The balancing loading device is designed as a 2-axes gimbal structure and is interpreted as two independent pendulum structures for simplifying. The loading device’s properties such as mass, moment of inertia, and position of the center of gravity are changeable for luggage. The system identification process of the loading device is required, and the controller should be optimized for the system in real-time. In this study, the system identification method is based on least squares method to estimate the unknown parameters of the loading device’s dynamic equation. It estimates the unknown parameters by calculating them that minimize the error function between the real system’s motion and the estimated system’s motion. This study improves the accuracy of parameter estimation using a null space solution. The null space solution can produce the correct parameters by adjusting the parameter’s relative sizes. The proposed system identification method is verified by the simulation to determine how close the estimated unknown parameters are to the real parameters.
Abstract
This study shows a system identification method of a balancing loading device for a stair climbing delivery robot. The balancing loading device is designed as a 2-axes gimbal structure and is interpreted as two independent pendulum structures for simplifying. The loading device’s properties such as mass, moment of inertia, and position of the center of gravity are changeable for luggage. The system identification process of the loading device is required, and the controller should be optimized for the system in real-time. In this study, the system identification method is based on least squares method to estimate the unknown parameters of the loading device’s dynamic equation. It estimates the unknown parameters by calculating them that minimize the error function between the real system’s motion and the estimated system’s motion. This study improves the accuracy of parameter estimation using a null space solution. The null space solution can produce the correct parameters by adjusting the parameter’s relative sizes. The proposed system identification method is verified by the simulation to determine how close the estimated unknown parameters are to the real parameters.
참고문헌 (10)
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