scholarly journals Contact force estimation for serial manipulator based on weighted moving average with variable span and standard Kalman filter with automatic tuning

2021 ◽  
Author(s):  
Feng Cao ◽  
Paul D. Docherty ◽  
XiaoQi Chen
Keyword(s):  
Kalman Filter ◽  
Contact Force ◽  
Moving Average ◽  
Automatic Tuning ◽  
Force Estimation ◽  
Variable Span ◽  
Weighted Moving Average

scholarly journals Contact Force Estimation for Serial Manipulator Based on Weighted Moving Average with Variable Span and Standard Kalman Filter with Automatic Tuning

2021 ◽  
Author(s):  
Feng Cao ◽  
Paul D. Docherty ◽  
XiaoQi Chen
Keyword(s):  
Kalman Filter ◽  
Response Time ◽  
Contact Force ◽  
Moving Average ◽  
Automatic Tuning ◽  
Estimation Methods ◽  
Force Estimation ◽  
Motor Current ◽  
Variable Span ◽  
Weighted Moving Average

Abstract Sensorless contact force estimation methods facilitate the application of the serial manipulators to manufacturing as they enable robots to interact with unexpected collisions at low cost. In this paper, an external force estimation approach with no embedded sensors is proposed. The approach combines a Weighted Moving Average (WMA) with variable span, the standard Kalman filter (SKF), and its tuning routines. Improved confidence in the motor output torque is achieved due to the reduction of the measurement noise in the motor current by the WMA. The span of the filter adapts continuously to achieve optimal tradeoff between response time and precision of estimation in real-time. With the comprehensive information of uncertainty in motor current noise and measurement errors of individual joints speed, an automatic tuning algorithm of the SKF is presented. Validation of the presented estimation approach in terms of estimation accuracy and response time was conducted on the Universal Robot 5 manipulator with differing end effector loads. It was found that the combined force estimation method leads to a reduction of the root-mean-square error and response time by 55.2% and 20.8% in comparison with the established method. The proposed method can be applied to any robotic manipulators as long as the motor information (current, joint position, and joint velocities) are available. Consequently, the cost of collision recognition could be reduced dramatically.

Improved exponential weighted moving average based measurement noise estimation for strapdown inertial navigation system/doppler velocity log integrated system

2020 ◽  
pp. 1-21
Author(s):  
Lanhua Hou ◽  
Xiaosu Xu ◽  
Yiqing Yao ◽  
Di Wang ◽  
Jinwu Tong
Keyword(s):  
Kalman Filter ◽  
Inertial Navigation System ◽  
Moving Average ◽  
Integrated System ◽  
Measurement Noise ◽  
Noise Estimation ◽  
Doppler Velocity ◽  
Forgetting Factor ◽  
Exponential Weighted Moving Average ◽  
Weighted Moving Average

Abstract The strapdown inertial navigation system (SINS) with integrated Doppler velocity log (DVL) is widely utilised in underwater navigation. In the complex underwater environment, however, the DVL information may be corrupted, and as a result the accuracy of the Kalman filter in the SINS/DVL integrated system degrades. To solve this, an adaptive Kalman filter (AKF) with measurement noise estimator to provide noise statistical characteristics is generally applied. However, existing methods like moving windows (MW) and exponential weighted moving average (EWMA) cannot adapt to a dynamic environment, which results in unsatisfactory noise estimation performance. Moreover, the forgetting factor has to be determined empirically. Therefore, this paper proposes an improved EWMA (IEWMA) method with adaptive forgetting factor for measurement noise estimation. First, the model for a SINS/DVL integrated system is established, then the MW and EWMA based measurement noise estimators are illustrated. Subsequently, the proposed IEWMA method which is adaptive to the various environments without experience is introduced. Finally, simulation and vehicle tests are conducted to evaluate the effectiveness of the proposed method. Results show that the proposed method outperforms the MW and EWMA methods in terms of measurement noise estimation and navigation accuracy.

Statistical Process Control Based on Kalman Filter in Manufacturing Process

2011 ◽  
Vol 201-203 ◽  
pp. 986-989
Author(s):  
Pei Wang ◽  
Ding Hua Zhang ◽  
Shan Li ◽  
Ming Wei Wang ◽  
Bing Chen
Keyword(s):  
Kalman Filter ◽  
Process Control ◽  
Statistical Process Control ◽  
Manufacturing Process ◽  
Average Method ◽  
Moving Average ◽  
Exponentially Weighted Moving Average ◽  
Statistical Process ◽  
Weighted Moving Average ◽  
Exponentially Weighted

In order to reduce the impact of data noise to quality control and make monitor results more precise in manufacturing process, the method of statistical process control based on Kalman filter was proposed. In this method, the statistical process control model based on Kalman filter was built and the quality control method of exponentially weighted moving average based on Kalman filter was put forward. While monitoring manufacturing process, first the technology of Kalman filter was used to smooth data and to reduce noise, and then control charts were built by the method of exponentially weighted moving average to monitor quality. Finally, the performance of the exponentially weighted moving average method based on Kalman filter and the tranditional exponentially weighted moving average method was compared. The performance result illustrates the feasibility and validity of the proposed quality monitor method.

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