FPGA-based wideband force sensing with Kalman-filter-based disturbance observer

In addition to the declining birthrate and aging population in Japan, there has been a recent decrease in its total population. This threatens to exacerbate a shortage in labor force, which could trigger an increase in the luggage transport costs of transportation companies or the service industry. The demand for inverted two-wheeled luggage transport vehicles has been increasing steadily owing to their high mobility, compactness, affordability, and pivotal turnability. However, owing to their statical instability, these vehicles are limited. Accordingly, stability can be improved in these systems by configuring a spreading system and applying a disturbance observer based on a Kalman filter. The application of a Kalman filter enables us to estimate the disturbance in which the error between the true and estimated values is the least mean square. Furthermore, we validated the efficiency of the proposed method via its translational movement, turning angle control, and load-loading/unloading experiments using various loads.

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Applications of disturbance observer and kalman filter based force sensation in motion control

2018 IEEE 15th International Workshop on Advanced Motion Control (AMC) ◽

10.1109/amc.2019.8371166 ◽

2018 ◽

Cited By ~ 1

Author(s):

Thao Tran Phuong ◽

Kiyoshi Ohishi ◽

Yuki Yokokura ◽

Yoshinori Takei

Keyword(s):

Kalman Filter ◽

Motion Control ◽

Disturbance Observer

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Application of Linear Observers to Estimate States of a Roll Simulator

Volume 2: Dynamic Modeling and Diagnostics in Biomedical Systems; Dynamics and Control of Wind Energy Systems; Vehicle Energy Management Optimization; Energy Storage, Optimization; Transportation and Grid Applications; Estimation and Identification Methods, Tracking, Detection, Alternative Propulsion Systems; Ground and Space Vehicle Dynamics; Intelligent Transportation Systems and Control; Energy Harvesting; Modeling and Control for Thermo-Fluid Applications, IC Engines, Manufacturing ◽

10.1115/dscc2014-5889 ◽

2014 ◽

Author(s):

Scott B. Zagorski ◽

Gary J. Heydinger ◽

Dennis A. Guenther

Keyword(s):

Nonlinear Dynamics ◽

Experimental Data ◽

Kalman Filter ◽

Extended Kalman Filter ◽

Kalman Filters ◽

Disturbance Observer ◽

Gain Scheduling ◽

System Parameters ◽

Scheduling Scheme

In this research, a variety of Kalman Filters are implemented in an effort to estimate sled speed of a Roll Simulator. An Extended Kalman Filter (EKF) is incorporated to capture the nonlinear dynamics of the sled-platform assembly to estimate sled speed for the entire motion, as a linear Kalman Filter was found to be inadequate. When applied to experimental data, the EKF over-estimates sled speed, which is due to a disturbance force and/or uncertainty in system parameters. In combination with the disturbance observer, the Kalman Filter adequately estimates sled speed for experimental data. For lower speed/payload applications, a Kalman Filter using an accelerometer and measured drum speed is able to accurately track sled speed when a gain scheduling scheme is employed.

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