Six-DOF Spacecraft Optimal Trajectory Planning and Real-Time Attitude Control: A Deep Neural Network-Based Approach

Chai, Runqi; Tsourdos, Antonios; Chai, Senchun; Xia, Yuanqing; Chen, C. L. Philip

doi:10.1109/tnnls.2019.2955400

Cited by 113 publications

(38 citation statements)

References 35 publications

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“…The analysis indicates which parameters are sensitive to various inputs and which ones are robust. Considering the references [84,85], experiments were conducted by defining four-parameter levels, as represented in Table 6. Afterward, an orthogonal array can be generated to characterize various parameter combinations (as represented in Table 7).…”

Section: Sensitivity Analysis Of Designed Modelmentioning

confidence: 99%

RETRACTED ARTICLE: Evolving deep convolutional neutral network by hybrid sine–cosine and extreme learning machine for real-time COVID19 diagnosis from X-ray images

Khishe²,

Mohammadi

et al. 2021

Soft Comput

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The COVID19 pandemic globally and significantly has affected the life and health of many communities. The early detection of infected patients is effective in fighting COVID19. Using radiology (X-Ray) images is, perhaps, the fastest way to diagnose the patients. Thereby, deep Convolutional Neural Networks (CNNs) can be considered as applicable tools to diagnose COVID19 positive cases. Due to the complicated architecture of a deep CNN, its real-time training and testing become a challenging problem. This paper proposes using the Extreme Learning Machine (ELM) instead of the last fully connected layer to address this deficiency. However, the parameters’ stochastic tuning of ELM’s supervised section causes the final model unreliability. Therefore, to cope with this problem and maintain network reliability, the sine–cosine algorithm was utilized to tune the ELM’s parameters. The designed network is then benchmarked on the COVID-Xray-5k dataset, and the results are verified by a comparative study with canonical deep CNN, ELM optimized by cuckoo search, ELM optimized by genetic algorithm, and ELM optimized by whale optimization algorithm. The proposed approach outperforms comparative benchmarks with a final accuracy of 98.83% on the COVID-Xray-5k dataset, leading to a relative error reduction of 2.33% compared to a canonical deep CNN. Even more critical, the designed network’s training time is only 0.9421 ms and the overall detection test time for 3100 images is 2.721 s.

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Section: Sensitivity Analysis Of Designed Modelmentioning

confidence: 99%

RETRACTED ARTICLE: Evolving deep convolutional neutral network by hybrid sine–cosine and extreme learning machine for real-time COVID19 diagnosis from X-ray images

Khishe²,

Mohammadi

et al. 2021

Soft Comput

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show abstract

“…They must both meet the kinematic and dynamic constraints of the robot manipulator, and the trajectory must be continuous, smooth, and impact-free within the performance requirements of the robot manipulator's components; that is, the speed and acceleration must not have sudden changes [10]. At present, the research on optimal trajectory planning mainly focuses on time-optimal trajectory planning, energy-optimal trajectory planning, impact-optimal trajectory planning, and hybrid optimal trajectory planning [11][12][13].…”

Section: Related Workmentioning

confidence: 99%

Trajectory Planning of Robot Manipulator Based on RBF Neural Network

Song

Bai

et al. 2021

Entropy

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Robot manipulator trajectory planning is one of the core robot technologies, and the design of controllers can improve the trajectory accuracy of manipulators. However, most of the controllers designed at this stage have not been able to effectively solve the nonlinearity and uncertainty problems of the high degree of freedom manipulators. In order to overcome these problems and improve the trajectory performance of the high degree of freedom manipulators, a manipulator trajectory planning method based on a radial basis function (RBF) neural network is proposed in this work. Firstly, a 6-DOF robot experimental platform was designed and built. Secondly, the overall manipulator trajectory planning framework was designed, which included manipulator kinematics and dynamics and a quintic polynomial interpolation algorithm. Then, an adaptive robust controller based on an RBF neural network was designed to deal with the nonlinearity and uncertainty problems, and Lyapunov theory was used to ensure the stability of the manipulator control system and the convergence of the tracking error. Finally, to test the method, a simulation and experiment were carried out. The simulation results showed that the proposed method improved the response and tracking performance to a certain extent, reduced the adjustment time and chattering, and ensured the smooth operation of the manipulator in the course of trajectory planning. The experimental results verified the effectiveness and feasibility of the method proposed in this paper.

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“…Mostly, HAR solutions are developed using artificial neural network (ANN), extreme learning machine (Semwal, 2019), support vector machine (Anguita, 2012), Naive Bayes, decision tree, K-nearest neighbour (Semwal and Nandi, 2015; Raj, 2018) and deep learning methods (Semwal, 2017; Semwal, 2017). The use of deep learning methods for HAR has significantly increased recognition accuracy (Chai, 2019; Murad and Pyun, 2017). The strength of deep learning is that it can automatically extract features as per the task requirement.…”

Section: Introductionmentioning

confidence: 99%

Wearable sensor-based pattern mining for human activity recognition: deep learning approach

Bijalwan¹,

Semwal

Gupta

2021

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Purpose This paper aims to deal with the human activity recognition using human gait pattern. The paper has considered the experiment results of seven different activities: normal walk, jogging, walking on toe, walking on heel, upstairs, downstairs and sit-ups. Design/methodology/approach In this current research, the data is collected for different activities using tri-axial inertial measurement unit (IMU) sensor enabled with three-axis accelerometer to capture the spatial data, three-axis gyroscopes to capture the orientation around axis and 3° magnetometer. It was wirelessly connected to the receiver. The IMU sensor is placed at the centre of mass position of each subject. The data is collected for 30 subjects including 11 females and 19 males of different age groups between 10 and 45 years. The captured data is pre-processed using different filters and cubic spline techniques. After processing, the data are labelled into seven activities. For data acquisition, a Python-based GUI has been designed to analyse and display the processed data. The data is further classified using four different deep learning model: deep neural network, bidirectional-long short-term memory (BLSTM), convolution neural network (CNN) and CNN-LSTM. The model classification accuracy of different classifiers is reported to be 58%, 84%, 86% and 90%. Findings The activities recognition using gait was obtained in an open environment. All data is collected using an IMU sensor enabled with gyroscope, accelerometer and magnetometer in both offline and real-time activity recognition using gait. Both sensors showed their usefulness in empirical capability to capture a precised data during all seven activities. The inverse kinematics algorithm is solved to calculate the joint angle from spatial data for all six joints hip, knee, ankle of left and right leg. Practical implications This work helps to recognize the walking activity using gait pattern analysis. Further, it helps to understand the different joint angle patterns during different activities. A system is designed for real-time analysis of human walking activity using gait. A standalone real-time system has been designed and realized for analysis of these seven different activities. Originality/value The data is collected through IMU sensors for seven activities with equal timestamp without noise and data loss using wirelessly. The setup is useful for the data collection in an open environment outside the laboratory environment for activity recognition. The paper also presents the analysis of all seven different activity trajectories patterns.

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Six-DOF Spacecraft Optimal Trajectory Planning and Real-Time Attitude Control: A Deep Neural Network-Based Approach

Cited by 113 publications

References 35 publications

RETRACTED ARTICLE: Evolving deep convolutional neutral network by hybrid sine–cosine and extreme learning machine for real-time COVID19 diagnosis from X-ray images

RETRACTED ARTICLE: Evolving deep convolutional neutral network by hybrid sine–cosine and extreme learning machine for real-time COVID19 diagnosis from X-ray images

Trajectory Planning of Robot Manipulator Based on RBF Neural Network

Wearable sensor-based pattern mining for human activity recognition: deep learning approach

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