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1 – 10 of 12
Article
Publication date: 31 May 2022

Jiacai Wang, Jiaoliao Chen, Libin Zhang, Fang Xu and Lewei Zhi

The sensorless external force estimation of robot manipulator can be helpful for reducing the cost and complexity of the robot system. However, the complex friction phenomenon of…

Abstract

Purpose

The sensorless external force estimation of robot manipulator can be helpful for reducing the cost and complexity of the robot system. However, the complex friction phenomenon of the robot joint and uncertainty of robot model and signal noise significantly decrease the estimation accuracy. This study aims to investigate the friction modeling and the noise rejection of the external force estimation.

Design/methodology/approach

A LuGre-linear-hybrid (LuGre-L) friction model that combines the dynamic friction characteristics of the robot joint and static friction of the drive motor is proposed to improve the modeling accuracy of robot friction. The square root cubature Kalman filter (SCKF) is improved by integrating a Sage Window outer layer and a nonlinear disturbance observer (NDOB) inner layer. In the outer layer, Sage Window is integrated in the square root Kalman filter (W-SCKF) to dynamically adjust noise statistics. NDOB is applied as the inner layer of W-SCKF (NDOB-WSCKF) to obtain the uncertain state variables of the state model.

Findings

A peg-in-hole contact experiment conducted on a real robot demonstrates that the average accuracy of the estimated joint torque based on LuGre-L is improved by 4.9% in contrast to the LuGre model. Based on the proposed NDOB-WSCKF, the average estimation accuracy of the external joint torque can reach up to 92.1%, which is improved by 4%–15.3% in contrast to other estimation methods (SCKF and NDOB).

Originality/value

A LuGre-L friction model is proposed to handle the coupling of static and dynamic friction characteristics for the robot manipulator. An improved SCKF is applied to estimate the external force of the robot manipulator. To improve the noise rejection ability of the estimation method and make it more resistant to unmodeled state variable, SCKF is improved by integrating a Sage Window and NDOB, and a NDOB-WSCKF external force estimator is developed. Validation results demonstrate that the accuracy of the robot dynamics model and the estimated external force is improved by the proposed method.

Details

Industrial Robot: the international journal of robotics research and application, vol. 50 no. 1
Type: Research Article
ISSN: 0143-991X

Keywords

Article
Publication date: 11 July 2019

Kai Zhao, Li-Guo Tan and Shen-Min Song

This paper aims to give the centralized and distributed fusion estimator for nonlinear multi-sensor networked systems with packet loss compensation and correlated noises and give…

Abstract

Purpose

This paper aims to give the centralized and distributed fusion estimator for nonlinear multi-sensor networked systems with packet loss compensation and correlated noises and give the corresponding square-root cubature Kalman filter.

Design/methodology/approach

Based on the Gaussian approximation recursive filter framework, the authors derive the centralized fusion filter and using the projection theorem, the authors derive the centralized fusion smoother. Then, based on the fast batch covariance intersection fusion algorithm, the authors give the corresponding results for distributed fusion estimators.

Findings

Designing the fusion estimators for nonlinear multi-sensor networked systems with packet loss compensation and correlated noises is necessary. It is useful for general nonlinear systems.

Originality/value

Throughout the whole study, the main highlights of this paper are as follows: packet loss compensation and correlated noises are considered in nonlinear multi-sensor networked systems. There are no relevant conclusions in the existing literature; centralized and distributed fusion estimators are derived based on the above system; for the posterior covariance with compensation factor and correlated noises, a new square-root factor of the error covariance is derived; and the new square-root factor of the error covariance is used to replace the numerical implementation of the covariance in cubature Kalman filter (CKF), which simplified the problem in calculating the posterior covariance in CKF.

Details

Sensor Review, vol. 39 no. 5
Type: Research Article
ISSN: 0260-2288

Keywords

Article
Publication date: 21 December 2021

Yunpu Zhang, Gongguo Xu and Ganlin Shan

Continuous and stable tracking of the low-altitude maneuvering targets is usually difficult due to terrain occlusion and Doppler blind zone (DBZ). This paper aims to present a…

Abstract

Purpose

Continuous and stable tracking of the low-altitude maneuvering targets is usually difficult due to terrain occlusion and Doppler blind zone (DBZ). This paper aims to present a non-myopic scheduling method of multiple radar sensors for tracking the low-altitude maneuvering targets. In this scheduling problem, the best sensors are systematically selected to observe targets for getting the best tracking accuracy under maintaining the low intercepted probability of a multi-sensor system.

Design/methodology/approach

First, the sensor scheduling process is formulated within the partially observable Markov decision process framework. Second, the interacting multiple model algorithm and the cubature Kalman filter algorithm are combined to estimate the target state, and the DBZ information is applied to estimate the target state when the measurement information is missing. Then, an approximate method based on a cubature sampling strategy is put forward to calculate the future expected objective of the multi-step scheduling process. Furthermore, an improved quantum particle swarm optimization (QPSO) algorithm is presented to solve the sensor scheduling action quickly. Optimization problem, an improved QPSO algorithm is presented to solve the sensor scheduling action quickly.

Findings

Compared with the traditional scheduling methods, the proposed method can maintain higher target tracking accuracy with a low intercepted probability. And the proposed target state estimation method in DBZ has better tracking performance.

Originality/value

In this paper, DBZ, sensor intercepted probability and complex terrain environment are considered in sensor scheduling, which has good practical application in a complex environment.

Article
Publication date: 25 October 2021

Xinjian Ma, Shiqian Liu, Huihui Cheng and Weizhi Lyu

This paper aims to focus on the sensor fault-tolerant control (FTC) for civil aircraft under exterior disturbance.

Abstract

Purpose

This paper aims to focus on the sensor fault-tolerant control (FTC) for civil aircraft under exterior disturbance.

Design/methodology/approach

First, a three-step cubature Kalman filter (TSCKF) is designed to detect and isolate the sensor fault and to reconstruct the sensor signal. Meanwhile, a nonlinear disturbance observer (NDO) is designed for disturbance estimation. The NDO and the TSCKF are combined together and an NDO-TSCKF is proposed to solve the problem of sensor faults and bounded disturbances simultaneously. Furthermore, an FTC scheme is designed based on the nonlinear dynamic inversion (NDI) and the NDO-TSCKF.

Findings

The method is verified by a Cessna 172 aircraft model under bias gyro fault and constant angular rate disturbance. The proposed NDO-TSCKF has the ability of signal reconstruction and disturbance estimation. The proposed FTC scheme is also able to solve the sensor fault and disturbance simultaneously.

Research limitations/implications

NDO-TSCKF is the novel algorithm used in sensor signal reconstruction for aircraft. Then, disturbance observer-based FTC can improve the flight control system performances when the system with faults.

Practical implications

The NDO-TSCKF-based FTC scheme can be used to solve the sensor fault and exterior disturbance in flight control. For example, the bias gyro fault with constant angular rate disturbance of a civil aircraft is studied.

Social implications

Signal reconstruction for critical sensor faults and disturbance observer-based FTC for civil aircraft are useful in modern civil aircraft design and development.

Originality/value

This is the research paper studies on the signal reconstruction and FTC scheme for civil aircraft. The proposed NDO-TSCKF is better than the current reconstruction filter because the failed sensor signal can be reconstructed under disturbances. This control scheme has a better fault-tolerant capability for sensor faults and bounded disturbances than using regular NDI control.

Details

Aircraft Engineering and Aerospace Technology, vol. 94 no. 2
Type: Research Article
ISSN: 1748-8842

Keywords

Article
Publication date: 6 November 2018

Kai Xiong and Liangdong Liu

The successful use of the standard extended Kalman filter (EKF) is restricted by the requirement on the statistics information of the measurement noise. The covariance of the…

Abstract

Purpose

The successful use of the standard extended Kalman filter (EKF) is restricted by the requirement on the statistics information of the measurement noise. The covariance of the measurement noise may deviate from its nominal value in practical environment, and the filtering performance may decline because of the statistical uncertainty. Although the adaptive EKF (AEKF) is available for recursive covariance estimation, it is often less accurate than the EKF with accurate noise statistics.

Design/methodology/approach

Aiming at this problem, this paper develops a parallel adaptive EKF (PAEKF) by combining the EKF and the AEKF with an adaptive law, such that the final state estimate is dominated by the EKF when the prior noise covariance is accurate, while the AEKF is activated when the actual noise covariance deviates from its nominal value.

Findings

The PAEKF can reduce the sensitivity of the algorithm to the model uncertainty and ensure the estimation accuracy in the normal case. The simulation results demonstrate that the PAEKF has the advantage of both the AEKF and the EKF.

Practical implications

The presented algorithm is applicable for spacecraft relative attitude and position estimation.

Originality/value

The PAEKF is presented for a kind of nonlinear uncertain systems. Stability analysis is provided to show that the error of the estimator is bounded under certain assumptions.

Details

Aircraft Engineering and Aerospace Technology, vol. 91 no. 1
Type: Research Article
ISSN: 1748-8842

Keywords

Article
Publication date: 4 November 2021

B. Omkar Lakshmi Jagan and S. Koteswara Rao

Doppler-Bearing Tracking (DBT) is commonly used in target tracking applications for the underwater environment using the Hull-Mounted Sensor (HMS). It is an important and…

Abstract

Purpose

Doppler-Bearing Tracking (DBT) is commonly used in target tracking applications for the underwater environment using the Hull-Mounted Sensor (HMS). It is an important and challenging problem in an underwater environment.

Design/methodology/approach

The system nonlinearity in an underwater environment increases due to several reasons such as the type of measurements taken, the speeds of target and observer, environmental conditions, number of sensors considered for measurements and so on. Degrees of nonlinearity (DoNL) for these problems are analyzed using a proposed measure of nonlinearity (MoNL) for state estimation.

Findings

In this research, the authors analyzed MoNL for state estimation and computed the conditional MoNL (normalized) using different filtering algorithms where measurements are obtained from a single sensor array (i.e. HMS). MoNL is implemented to find out the system nonlinearity for different filtering algorithms and identified how much nonlinear the system is, that is, to measure nonlinearity of a problem.

Originality/value

Algorithms are evaluated for various scenarios with different angles on the target bow (ATB) in Monte-Carlo simulation. Computation of root mean squared (RMS) errors in position and velocity is carried out to assess the state estimation accuracy using MATLAB.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 15 no. 3
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 28 December 2020

Zhen Zhou, Dongqing Wang and Boyang Xu

The purpose of this paper is to explore a multi-innovation with forgetting factor-based EKF-SLAM (FMI-EKF-SLAM) algorithm to solve the error increasing problem, caused by the…

Abstract

Purpose

The purpose of this paper is to explore a multi-innovation with forgetting factor-based EKF-SLAM (FMI-EKF-SLAM) algorithm to solve the error increasing problem, caused by the Extended Kalman filtering (EKF) violating the local linear assumption in simultaneous localization and mapping (SLAM) for mobile robots because of strong nonlinearity.

Design/methodology/approach

A multi-innovation with forgetting factor-based EKF-SLAM (FMI-EKF-SLAM) algorithm is investigated. At each filtering step, the FMI-EKF-SLAM algorithm expands the single innovation at current step to an extended multi-innovation containing current and previous steps and introduces the forgetting factor to reduce the effect of old innovations.

Findings

The simulation results show that the explored FMI-EKF-SLAM method reduces the state estimation errors, obtains the ideal filtering effect and achieves higher accuracy in positioning and mapping.

Originality/value

The method proposed in this paper improves the positioning accuracy of SLAM and improves the EKF, so that the EKF has higher accuracy and wider application range.

Details

Assembly Automation, vol. 41 no. 1
Type: Research Article
ISSN: 0144-5154

Keywords

Article
Publication date: 2 May 2017

Kai Xiong and Chunling Wei

This paper aims to present a multiple-model adaptive estimator (MMAE) to calibrate the star sensor low frequency error (LFE). The star sensor LFE, which is caused primarily by the…

Abstract

Purpose

This paper aims to present a multiple-model adaptive estimator (MMAE) to calibrate the star sensor low frequency error (LFE). The star sensor LFE, which is caused primarily by the periodic thermal distortion, has a great impact on spacecraft attitude determination accuracy.

Design/methodology/approach

The unfavorable effect of the LFE can be partly eliminated by using the calibration algorithm based on the augmented Kalman filter (AKF). However, the AKF may be worse than the traditional Kalman filter (KF) in the absence of the LFE. To cope with this problem, the MMAE is applied first time for combining the AKF and the KF in the spacecraft attitude determination system, such that satisfactory performance can be achieved in different operating scenarios.

Findings

The convergence of the presented MMAE is demonstrated through a formal derivation. A novel method is proposed to tune the MMAE design parameter, such that the convergence rate of the estimator is increased. It is shown via numerical studies that the presented algorithm outperforms the AKF and the KF.

Practical implications

The calibration algorithm is applicable for spacecraft attitude determination.

Originality/value

An effective star sensor LFE calibration algorithm based on the MMAE is developed. In addition, a novel method is proposed to increase convergence rate of the estimator.

Details

Aircraft Engineering and Aerospace Technology, vol. 89 no. 3
Type: Research Article
ISSN: 1748-8842

Keywords

Article
Publication date: 12 January 2024

Wei Xiao, Zhongtao Fu, Shixian Wang and Xubing Chen

Because of the key role of joint torque in industrial robots (IRs) motion performance control and energy consumption calculation and efficiency optimization, the purpose of this…

Abstract

Purpose

Because of the key role of joint torque in industrial robots (IRs) motion performance control and energy consumption calculation and efficiency optimization, the purpose of this paper is to propose a deep learning torque prediction method based on long short-term memory (LSTM) recurrent neural networks optimized by particle swarm optimization (PSO), which can accurately predict the the joint torque.

Design/methodology/approach

The proposed model optimized the LSTM with PSO algorithm to accurately predict the IRs joint torque. The authors design an excitation trajectory for ABB 1600–10/145 experimental robot and collect its relative dynamic data. The LSTM model was trained with the experimental data, and PSO was used to find optimal number of LSTM nodes and learning rate, then a torque prediction model is established based on PSO-LSTM deep learning method. The novel model is used to predict the robot’s six joint torque and the root mean error squares of the predicted data together with least squares (LS) method were comparably studied.

Findings

The predicted joint torque value by PSO-LSTM deep learning approach is highly overlapped with those from real experiment robot, and the error is quite small. The average square error between the predicted joint torque data and experiment data is 2.31 N.m smaller than that with the LS method. The accuracy of the novel PSO-LSTM learning method for joint torque prediction of IR is proved.

Originality/value

PSO and LSTM model are deeply integrated for the first time to predict the joint torque of IR and the prediction accuracy is verified.

Details

Industrial Robot: the international journal of robotics research and application, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0143-991X

Keywords

Article
Publication date: 16 March 2022

Rong Wang, Jin Wu, Chong Li, Shengbo Qi, Xiangrui Meng, Xinning Wang and Chengxi Zhang

The purpose of this paper is to propose a high-precision attitude solution to solve the attitude drift problem caused by the dispersion of low-cost micro-electro-mechanical system…

Abstract

Purpose

The purpose of this paper is to propose a high-precision attitude solution to solve the attitude drift problem caused by the dispersion of low-cost micro-electro-mechanical system devices in strap-down inertial navigation attitude solution of micro-quadrotor.

Design/methodology/approach

In this study, a three-stage attitude estimation scheme that combines data preprocessing, gyro drifts prediction and enhanced unscented Kalman filtering (UKF) is proposed. By introducing a preprocessing model, the quaternion orientation is calculated as the composition of two algebraic quaternions, and the decoupling feature of the two quaternions makes the roll and pitch components independent of magnetic interference. A novel real-time based on differential value (DV) estimation algorithm is proposed for gyro drift. This novel solution prevents the impact of quartic characteristics and uses the iterative method to meet the requirement of real-time applications. A novel attitude determination algorithm, the pre-process DV-UKF algorithm, is proposed in combination with UKF based on the above solution and its characteristics.

Findings

Compared to UKF, both simulation and experimental results demonstrate that the pre-process DV-UKF algorithm has higher reliability in attitude determination. The dynamic errors in the three directions of the attitude are below 2.0°, the static errors are all less than 0.2° and the absolute attitude errors tailored by average are about 47.98% compared to the UKF.

Originality/value

This paper fulfils an identified need to achieve high-precision attitude estimation when using low-cost inertial devices in micro-quadrotor. The accuracy of the pre-process DV-UKF algorithm is superior to other products in the market.

Details

Aircraft Engineering and Aerospace Technology, vol. 94 no. 7
Type: Research Article
ISSN: 1748-8842

Keywords

1 – 10 of 12