Cascaded Control of Flexible-Joint Robots Based on Sliding-Mode Estimator Approach

The inherent highly nonlinear coupling and system uncertainties make the controller design for a flexible-joint robot extremely difficult. The goal of the control of any robotic system is to achieve high bandwidth, high accuracy of trajectory tracking, and high robustness, whereby the high bandwidth for flexible-joint robot is the most challenging issue. This paper is dedicated to design such a link position controller with high bandwidth based on sliding-mode technique. Then, two control approaches ((1) extended-regular-form approach and (2) the cascaded control structure based on the sliding-mode estimator approach) are presented for the link position tracking control of flexible-joint robot, considering the dynamics of AC-motors in robot joints, and compared with the singular perturbation approach. These two-link position controllers are tested and verified by the simulation studies with different reference trajectories and under different joint stiffness

Robot-Environment Interaction Control of a Flexible Joint Light Weight Robot Manipulator

This paper presents a weighted path planning approach for a light weight robot coming into compliant contact with the environment, as well as robot-environment interaction enabled impedance control. Using a joint torque sensor, Cartesian impedance control is introduced to realize the manipulator compliance control. Then the weighted path planning approach is developed to detect the contact condition and to control the interaction. Experiments are carried out on a 5-DOF light weight manipulator. The experimental results validate the developed control approach enhanced by the weighted path planning scheme

A Kalman Filter-Based Method for Reconstructing GMS-5 Land Surface Temperature Time Series

Satellite-derived environmental parameters play important roles in environmental research on global changes and regional resources. Atmosphere effects and sensor limitations often lead to data products that vary in quality. The main goal of time series data reconstruction is to use various statistical and numerical analysis methods and to stimulate changing seasonal or annual parameters, providing more complete data sets for correlational research. This paper aims to develop a time series reconstruction algorithm for LST based on data assimilation according to the current problems of unstable precision and unsatisfactory results, and the simplistic effects of evaluation methods while using remote sensing-derived LST data as the basic parameters and the daily LST data derived from the static meteorological satellite GMS-5 as the input data. The data assimilation system used the Kalman filter as the assimilation algorithm. A complete set of global refined LST time series data sets were obtained by constantly correcting the LST values according to the regional ground-based observations. This method was implemented using MATLAB software (version R2017a), and was applied and validated through partitioning using the principal elevation in the Beijing, Tianjin, and Hebei regions. The results show that the accuracy of the reconstructed LST data series improved significantly in terms of the mean and standard deviation. Better consistency was achieved between the variables obtained over a year from the reconstructed LST data and the ground observations from the LST data set