Contact Force Estimation for Robot Manipulator Using Semiparametric Model and Disturbance Kalman Filter

Jin, Hu; Xiong, Rong

doi:10.1109/tie.2017.2748056

Cited by 93 publications

(53 citation statements)

References 32 publications

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“…e auxiliary variable z is the key to the relationship between sliding mode control and hybrid impedance control. By substituting equation (19) into equation (18) and by eliminating z and _ z in the expression, the following equation is obtained after rearrangement:…”

Section: Design Of the Hybrid Sliding Mode Impedance Controlmentioning

confidence: 99%

“…Agarwal and Parthasarathy [18] used the disturbance observer output as a part of a nonlinear system state variable, treated the output error as an independent Gaussian white noise, and used an extended Kalman filter to track the disturbance. Hu and Xiong [19] compensated the rigid body dynamics model's error with a multilayer perceptron and combined this model with a semiparametric model and a perturbed Kalman filter to improve the accuracy and robustness of force estimation under uncertainty. Van Damme et al [20] used the leastsquares method and a disturbance observer to estimate the robot end forces from noisy driver torques and discussed the intrinsic relationship between the least-squares method and the disturbance observer method.…”

Section: Introductionmentioning

confidence: 99%

“…References [10][11][12][13][14][15][16][17][18][19][20][21][22][23] mentioned the use of the disturbance observer to estimate external forces, but the research topics are different from the topics studied in this paper. References [10,11,15] were validated only on two-link robots with simple dynamics models.…”

Section: Introductionmentioning

confidence: 99%

“…References [10,11,15] were validated only on two-link robots with simple dynamics models. References [12][13][14]19] took collaborative robots as the research object, and this kind of robot is directly driven by DC motors. As compared with normal industrial robots, the transmission mechanism of DC motor-driving robots is greatly simplified, and the noise in the motor torque signal is greatly reduced.…”

Section: Introductionmentioning

confidence: 99%

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Disturbance Observer‐Based Robot End Constant Contact Force‐Tracking Control

Liang

2019

Complexity

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A disturbance observer-based hybrid sliding mode impedance control method is proposed in this paper, which is able to achieve robot end constant contact force-tracking control without force/torque sensors. The method requires only the values of joint torque, joint angle, and joint angular velocity, which are converted by robot servo motor signals, to implement the control. The control scheme consists of two parts: one is a disturbance observer and the other is a hybrid sliding mode impedance controller. The disturbance observer, which takes robot internal signals mentioned above as the inputs to estimate the robot end contact force, is designed based on generalized momentum, thus improving the estimation accuracy. The hybrid sliding mode impedance controller, which uses the values estimated by the disturbance observer and the robot internal signals as the inputs to calculate the corresponding position adjustment, integrates both the impedance control and sliding mode control, thus improving the force-tracking performance and robustness. Experimental results show that the proposed disturbance observer-based hybrid sliding mode impedance control method possesses high control precision.

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Section: Design Of the Hybrid Sliding Mode Impedance Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Disturbance Observer‐Based Robot End Constant Contact Force‐Tracking Control

Liang

2019

Complexity

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“…It provides a mathematical framework for predicting unmeasured variables from indirectly noisy measurements. As a predictive tool, Kalman filter is mainly used to estimate the state of dynamic systems, such as process control [35,36], flood forecasting [37], radar tracking [38], GNSS navigation [39,40] and performance analysis of estimation systems. Sedano et al [41] used a Kalman filter algorithm to achieve spatiotemporal fusion of existing Landsat TM and 250-m NDVI MODIS (MOD13Q1) images for predictions of synthetic Landsat NDVI values.…”

Section: Introductionmentioning

confidence: 99%

Harmonizing Multi-Source Remote Sensing Images for Summer Corn Growth Monitoring

Zhang

Zhu

et al. 2019

Remote Sensing

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Continuous monitoring of crop growth status using time-series remote sensing image is essential for crop management and yield prediction. The growing season of summer corn in the North China Plain with the period of rain and hot, which makes the acquisition of cloud-free satellite imagery very difficult. Therefore, we focused on developing image datasets with both a high temporal resolution and medium spatial resolution by harmonizing the time-series of MOD09GA Normalized Difference Vegetation Index (NDVI) images and 30-m-resolution GF-1 WFV images using the improved Kalman filter model. The harmonized images, GF-1 images, and Landsat 8 images were then combined and used to monitor the summer corn growth from 5th June to 6th October, 2014, in three counties of Hebei Province, China, in conjunction with meteorological data and MODIS Evapotranspiration Data Set. The prediction residuals (∆PR K ) in NDVI between the GF-1 observations and the harmonized images was in the range of −0.2 to 0.2 with Gauss distribution. Moreover, the obtained phenological curves manifested distinctive growth features for summer corn at field scales. Changes in NDVI over time were more effectively evaluated and represented corn growth trends, when considered in conjunction with meteorological data and MODIS Evapotranspiration Data Set. We observed that the NDVI of summer corn showed a process of first decreasing and then rising in the early growing stage and discuss how the temperature and moisture of the environment changed with the growth stage. The study demonstrated that the synthesized dataset constructed using this methodology was highly accurate, with high temporal resolution and medium spatial resolution and it was possible to harmonize multi-source remote sensing imagery by the improved Kalman filter for long-term field monitoring.synthesized vegetation index products have many applications in monitoring global climate change, material and energy cycles and the growth of surface vegetation. integrated the MODIS eight-day and 16-day products to quantitatively estimate green leaf area index (GLAI) over corn fields and evaluate the potential and limitations of the satellite data. Doraiswamy et al. [6] evaluated the applicability of the eight-day MODIS composite imagery in monitoring crop growth and yield estimation and the results showed that these images were very reliable. Sakamoto et al. [7,8] proposed a method which could identify the growth period of maize and soybean with two-step filtering based on MODIS data. However, the low spatial resolution (i.e., 250 m or 500 m) will cause a lot of errors by mixing multiple objects which have great differences in spectral curves and phenological characteristics in one pixel. Wang et al. [9] monitored and evaluated the growth condition of spring maize based on MODIS vegetation index products, MODIS LAI products and Global Land Surface Satellites LAI product. Moreover, the medium-resolution satellites (i.e., Landsat and GF-1) have developed rapidly and can well express the detailed featu...

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Robot joint friction compensation learning enhanced by 6D virtual sensor

Roveda

Bussolan

Braghin

et al. 2022

Intl J Robust & Nonlinear

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High-performance robot control is one of the most investigated topics in both research and industry. Being able to compensate for robot dynamics is indeed one major challenge. Joint friction is commonly the main issue, especially in sensorless (i.e., no availability of torque/force sensors) compliance-controlled robots for interaction application purposes. The presented paper aims to propose a methodology for sensorless Cartesian impedance controlled robots to learn local friction compensation controllers. Exploiting a 6D virtual sensor to quantify the joint friction effects, a Bayesian optimization (BO)-based algorithm is proposed to minimize the estimated external interaction in free-motion tasks (related to friction effects). The BO algorithm enhances the impedance control performance by tuning the model-based friction compensator parameters.To validate the proposed approach, experimental tests have been executed on a Franka EMIKA panda robot, highlighting the suitability of the proposed 6D virtual sensor and BO-based algorithm to minimize the estimated external interaction for joint friction compensation purposes.

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Contact Force Estimation for Robot Manipulator Using Semiparametric Model and Disturbance Kalman Filter

Cited by 93 publications

References 32 publications

Disturbance Observer‐Based Robot End Constant Contact Force‐Tracking Control

Disturbance Observer‐Based Robot End Constant Contact Force‐Tracking Control

Harmonizing Multi-Source Remote Sensing Images for Summer Corn Growth Monitoring

Robot joint friction compensation learning enhanced by 6D virtual sensor

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