High Performance Force Sensing Based on Kalman-Filter-Based Disturbance Observer Utilizing FPGA

The trajectory-tracking performance of the automobile electro-coating conveying mechanism is severely interrupted by highly nonlinear crossing couplings, unmodeled dynamics, parameter variation, friction, and unknown external disturbance. In this article, a sliding mode control with a nonlinear disturbance observer is proposed for high-accuracy motion control of the conveying mechanism. The nonlinear disturbance observer is designed to estimate not only the internal/external disturbance but also the model uncertainties. Based on the output of the nonlinear disturbance observer, a sliding mode control approach is designed for the hybrid series–parallel mechanism. Then, the stability of the closed-loop system is proved by means of a Lyapunov analysis. Finally, simulations with typical desired trajectory are presented to demonstrate the high performance of the proposed composite control scheme.

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Stabilization Control of Inverted Two-Wheeled Luggage Transport Vehicle Using a Kalman Filter-Based Disturbance Observer

Journal of Robotics and Mechatronics ◽

10.20965/jrm.2021.p0643 ◽

2021 ◽

Vol 33 (3) ◽

pp. 643-652

Author(s):

Hironori Matsubara ◽

◽

Yuki Nagatsu ◽

Hideki Hashimoto

Keyword(s):

Kalman Filter ◽

Disturbance Observer ◽

Service Industry ◽

High Mobility ◽

Least Mean Square ◽

Transport Costs ◽

Turning Angle ◽

Stabilization Control ◽

Translational Movement ◽

Transport Vehicle

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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Parameter Estimation in a Hydrostatic System Using Extended Kalman Filter

10.1115/imece2001/fpst-25000 ◽

2001 ◽

Author(s):

Yuvin A. Chinniah ◽

Richard Burton ◽

Saeid Habibi

Keyword(s):

Kalman Filter ◽

Bulk Modulus ◽

Extended Kalman Filter ◽

High Performance ◽

State Space Model ◽

System Response ◽

Actuation System ◽

Accuracy And Precision ◽

Hydrostatic System ◽

Entrained Air

Abstract In this paper, the Extended Kalman Filter (EKF) estimation technique is applied to a novel hydrostatic actuation system referred to as the Electrohydraulic Actuator (EHA). A state space model of the EHA is developed and the effective bulk modulus is estimated in simulation. The EHA is a high performance actuation system capable of moving large loads with very high accuracy and precision. In a practical situation, this parameter is very difficult to measure directly as it depends on entrained air which cannot be known at a particular point of time. The bulk modulus is critical for system response and a low bulk modulus as a result of air in the system can seriously hinder the performance of EHA and cause safety problems.

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