A reaction force estimation method of end effector of two-link manipulator using SMCSPO

2016 ◽  
Author(s):  
Wang Jie ◽  
Karam Dad ◽  
Min Cheol Lee
Keyword(s):  
Reaction Force ◽  
Estimation Method ◽  
Force Estimation ◽  
End Effector

scholarly journals Indirect Force Control of a Cable-Driven Parallel Robot: Tension Estimation using Artificial Neural Network trained by Force Sensor Measurements

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2520 ◽  
Author(s):  
Jinlong Piao ◽  
Eui-Sun Kim ◽  
Hongseok Choi ◽  
Chang-Bae Moon ◽  
Eunpyo Choi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Force Control ◽  
Estimation Method ◽  
Parallel Robot ◽  
Force Estimation ◽  
End Effector ◽  
Cable Properties ◽  
Cable Force ◽  
Artificial Neural

In a cable-driven parallel robot (CDPR), force sensors are utilized at each winch motor to measure the cable tension in order to obtain the force distribution at the robot end-effector. However, because of the effects of friction in the pulleys and the unmodeled cable properties of the robot, the measured cable tensions are often inaccurate, which causes force-control difficulties. To overcome this issue, this paper presents an artificial neural network (ANN)-based indirect end-effector force-estimation method, and its application to CDPR force control. The pulley friction and other unmodeled effects are considered as black-box uncertainties, and the tension at the end-effector is estimated by compensating for these uncertainties using an ANN that is developed using the training datasets from CDPR experiments. The estimated cable tensions at the end-effector are used to design a P-controller to track the desired force. The performance of the proposed ANN model is verified through comparisons with the forces measured directly at the end-effector. Furthermore, cable force control is implemented based on the compensated tensions to evaluate the performance of the CDPR in wrench space. The experimental results show that the proposed friction-compensation method is suitable for application in CDPRs to control the cable force.

Sliding Perturbation Observer Based Reaction Force Estimation Method of Surgical Robot Instrument for Haptic Realization

2015 ◽  
Vol 12 (02) ◽  
pp. 1550013 ◽  
Author(s):  
Sung Min Yoon ◽  
Min Cheol Lee ◽  
Chi Yen Kim
Keyword(s):  
Coulomb Friction ◽  
Sliding Mode ◽  
Reaction Force ◽  
Estimation Method ◽  
Surgical Robot ◽  
Model Parameters ◽  
Force Estimation ◽  
Haptic Information ◽  
Robust Controller ◽  
Practice Time

Previous research applied sliding mode control with a sliding perturbation observer (SMCSPO) algorithm as a robust controller to control a surgical robotic instrument and reported that reaction force loaded on the tip can be estimated similarly by the sliding perturbation observer (SPO). However, some factors, such as friction, in which it is difficult to find the model parameters beforehand, can have an effect on the reaction force estimation because the factors are included in the estimated perturbation. This paper addresses the SPO based reaction force estimation method to extract a pure reaction force on a surgical robot instrument in the case of including Coulomb friction due to the operation of cable-pulley structure. Coulomb friction can be estimated experimentally and compensated for from the estimated perturbation. An experimental evaluation was performed to prove the suggested estimation method. The results show that SPO can be substituted for sensors to measure the reaction force. This estimated reaction force will be used to realize the haptic function by sending the reaction force to a master device for a surgeon. The results will help to create surgical benefit such as shortening the practice time of a surgeon and providing haptic information to the surgeon by using it as haptic signal to protect an organ by forming a force boundary.

scholarly journals End-Effector Pose Estimation in Complex Environments Using Complementary Enhancement and Adaptive Fusion of Multisensor

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Mingrui Luo ◽  
En Li ◽  
Rui Guo ◽  
Jiaxin Liu ◽  
Zize Liang
Keyword(s):  
Kalman Filter ◽  
Pose Estimation ◽  
Unscented Kalman Filter ◽  
Rbf Neural Network ◽  
Estimation Error ◽  
Estimation Method ◽  
Original Data ◽  
Measurement Unit ◽  
Complex Environments ◽  
End Effector

Redundant manipulators are suitable for working in narrow and complex environments due to their flexibility. However, a large number of joints and long slender links make it hard to obtain the accurate end-effector pose of the redundant manipulator directly through the encoders. In this paper, a pose estimation method is proposed with the fusion of vision sensors, inertial sensors, and encoders. Firstly, according to the complementary characteristics of each measurement unit in the sensors, the original data is corrected and enhanced. Furthermore, an improved Kalman filter (KF) algorithm is adopted for data fusion by establishing the nonlinear motion prediction of the end-effector and the synchronization update model of the multirate sensors. Finally, the radial basis function (RBF) neural network is used to adaptively adjust the fusion parameters. It is verified in experiments that the proposed method achieves better performances on estimation error and update frequency than the original extended Kalman filter (EKF) and unscented Kalman filter (UKF) algorithm, especially in complex environments.

scholarly journals Verification of the Design Force Estimation Method for the Steel-type Breakwater in the Real Sea

2012 ◽  
Vol 12 (1) ◽  
pp. 205-215 ◽  
Author(s):  
Hyuck Min Kweon ◽  
Oh Kyun Kwon ◽  
Yu Sik Han ◽  
Kang Hun Yoon
Keyword(s):  
Estimation Method ◽  
Force Estimation ◽  
The Real ◽  
Steel Type

Effect of Load-Dependent Friction on the Estimation of Rack Force in Electric Power-Assisted Steering System

2019 ◽  
Vol 141 (11) ◽  
Author(s):  
Yijun Li ◽  
Taehyun Shim ◽  
Dexin Wang ◽  
Timothy Offerle
Keyword(s):  
Electric Power ◽  
A Priori ◽  
Estimation Method ◽  
Estimation Algorithm ◽  
Steering System ◽  
Estimation Methods ◽  
Current Steering ◽  
Force Estimation ◽  
Operation Conditions ◽  
Friction Estimation

The rack force is valuable information for a vehicle dynamics control system, as it relates closely to the road conditions and steering feel. Since there is no direct measurement of rack force in current steering systems, various rack force estimation methods have been proposed to obtain the rack force information. In order to get an accurate rack force estimate, it is important to have knowledge of the steering system friction. However, it is hard to have an accurate value of friction, as it is subject to variation due to operation conditions and material wear. Especially for the widely used column-assisted electric power steering (C-EPAS) system, the load-dependent characteristic of its worm gear friction has a significant effect on rack force estimation. In this paper, a rack force estimation method using a Kalman filter and a load-dependent friction estimation algorithm is introduced, and the effect of C-EPAS friction on rack force estimator performance is investigated. Unlike other rack force estimation methods, which assume that friction is known a priori, the proposed system uses a load-dependent friction estimation algorithm to determine accurate friction information in the steering system, and then a rack force is estimated using the relationship between steering torque and angle. The effectiveness of this proposed method is verified by carsim/simulink cosimulation.

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