scholarly journals A high accuracy modeling scheme for dynamic systems: spacecraft reaction wheel model

2022 ◽  
Vol 69 (1) ◽  
Author(s):  
Abd-Elsalam R. Abd-Elhay ◽  
Wael A. Murtada ◽  
Mohamed I. Yosof
Keyword(s):  
Attitude Control ◽  
Network Architecture ◽  
Evaluation Criteria ◽  
Complex Nature ◽  
Reaction Wheel ◽  
Nonlinear Constrained Optimization ◽  
Fitness Value ◽  
Mutation Strategy ◽  
Wheel Model ◽  
Effective Network

AbstractReaction wheels are crucial actuators in spacecraft attitude control subsystem (ACS). The precise modeling of reaction wheels is of fundamental need in spacecraft ACS for design, analysis, simulation, and fault diagnosis applications. The complex nature of the reaction wheel leads to modeling difficulties utilizing the conventional modeling schemes. Additionally, the absence of reaction wheel providers’ parameters is crucial for triggering a new modeling scheme. The Radial Basis Function Neural Network (RBFNN) has an efficient architecture, alluring generalization properties, invulnerability against noise, and amazing training capabilities. This research proposes a promising modeling scheme for the spacecraft reaction wheel utilizing RBFNN and an improved variant of the Quantum Behaved Particle Swarm Optimization (QPSO). The problem of enhancing the network parameters of the RBFNN at the training phase is formed as a nonlinear constrained optimization problem. Thus, it is proposed to efficiently resolve utilizing an enhanced version of QPSO with mutation strategy (EQPSO-2M). The proposed technique is compared with the conventional QPSO algorithm and different variants of PSO algorithms. Evaluation criteria rely upon convergence speed, mean best fitness value, stability, and the number of successful runs that has been utilized to assess the proposed approach. A non-parametric test is utilized to decide the critical contrast between the results of the proposed algorithm compared with different algorithms. The simulation results demonstrated that the training of the proposed RBFNN-based reaction wheel model with enhanced parameters by EQPSO-2M algorithm furnishes a superior prediction accuracy went with effective network architecture.

scholarly journals Controller Tuning and Robustness Testing of Attitude Control Laws for a CubeSat Mission

2021 ◽  
Author(s):  
Mohamed Jundi
Keyword(s):  
State Space ◽  
Attitude Control ◽  
Initial Conditions ◽  
Block Diagram ◽  
Linear Quadratic Regulator ◽  
Reaction Wheel ◽  
Linear Quadratic ◽  
Test Environment ◽  
Wheel Model ◽  
Space Equation

The purpose of this project was to create a test environment that can be used to test different controllers and their robustness. In this report, the equations of motion were derived using kinematics, with attitude quaternions, and spacecraft dynamics, with angular velocity and acceleration. The equations were combined and placed into the form of a linearized state-space equation. The different control methods being investigated, Linear Quadratic Regulator (LQR) for the reaction wheel model, and the Bdot with bias controller, were explained and the block diagram for each was shown. To setup the test, the tolerances for the roll, pitch, and yaw, and their rates, were taken from the mission requirement for the ESSENCE mission. The attitude tolerance being ±0.5deg and the angular rates requirement being ±0.05deg/s. Then the test setup was further explained. The test is broken up into different scripts and steps: 1. Main run function for simulation. Initializes simulation parameters. 2. Build state-space equation and calculate constant gain matrix. 3. Randomize initial conditions and pass onto simulation. 4. Post-processing and plot generation. 5. Statistics generation. This robust testing environment was used to test 5 different controllers for the reaction wheel model. Each controller was tested for 200 different simulations, in which the initial attitude, initial angular rates, and the center of mass were randomized. The first controller was successful for 198/200 simulations, where the only failure came from over-saturating the reaction wheels. The next three controllers had a perfect record and were successful for all 200 simulations each. The last controller, had only 71 successful simulations in the set, and a sample of one of the failed simulations was further investigated to see how it failed.

scholarly journals Controller Tuning and Robustness Testing of Attitude Control Laws for a CubeSat Mission

2021 ◽  
Author(s):  
Mohamed Jundi
Keyword(s):  
State Space ◽  
Attitude Control ◽  
Initial Conditions ◽  
Block Diagram ◽  
Linear Quadratic Regulator ◽  
Reaction Wheel ◽  
Linear Quadratic ◽  
Test Environment ◽  
Wheel Model ◽  
Space Equation

The purpose of this project was to create a test environment that can be used to test different controllers and their robustness. In this report, the equations of motion were derived using kinematics, with attitude quaternions, and spacecraft dynamics, with angular velocity and acceleration. The equations were combined and placed into the form of a linearized state-space equation. The different control methods being investigated, Linear Quadratic Regulator (LQR) for the reaction wheel model, and the Bdot with bias controller, were explained and the block diagram for each was shown. To setup the test, the tolerances for the roll, pitch, and yaw, and their rates, were taken from the mission requirement for the ESSENCE mission. The attitude tolerance being ±0.5deg and the angular rates requirement being ±0.05deg/s. Then the test setup was further explained. The test is broken up into different scripts and steps: 1. Main run function for simulation. Initializes simulation parameters. 2. Build state-space equation and calculate constant gain matrix. 3. Randomize initial conditions and pass onto simulation. 4. Post-processing and plot generation. 5. Statistics generation. This robust testing environment was used to test 5 different controllers for the reaction wheel model. Each controller was tested for 200 different simulations, in which the initial attitude, initial angular rates, and the center of mass were randomized. The first controller was successful for 198/200 simulations, where the only failure came from over-saturating the reaction wheels. The next three controllers had a perfect record and were successful for all 200 simulations each. The last controller, had only 71 successful simulations in the set, and a sample of one of the failed simulations was further investigated to see how it failed.

A TOPSIS-ELM framework for stock index price movement prediction

2021 ◽  
pp. 1-19
Author(s):  
Sidharth Samal ◽  
Rajashree Dash
Keyword(s):  
Network Architecture ◽  
Binary Classification ◽  
Evaluation Criteria ◽  
Multiple Criteria ◽  
Stock Index ◽  
Activation Functions ◽  
Price Movement ◽  
Movement Prediction ◽  
Learning Machine ◽  
Approximation Capability

In recent years Extreme Learning Machine (ELM) has gained the interest of various researchers due to its superior generalization and approximation capability. The network architecture and type of activation functions are the two important factors that influence the performance of an ELM. Hence in this study, a Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) oriented multi-criteria decision making (MCDM) framework is suggested for analyzing various ELM models developed with distinct activation functions with respect to sixteen evaluation criteria. Evaluating the performance of the ELM with respect to multiple criteria instead of single criterion can help in designing a more robust network. The proposed framework is used as a binary classification system for pursuing the problem of stock index price movement prediction. The model is empirically evaluated by using historical data of three stock indices such as BSE SENSEX, S&P 500 and NIFTY 50. The empirical study has disclosed promising results by evaluating ELM with different activation functions as well as multiple criteria.

Reaction wheel attitude control for space vehicles

1959 ◽  
Vol 4 (3) ◽  
pp. 139-149 ◽  
Author(s):  
R. Froelich ◽  
H. Papapoff
Keyword(s):  
Attitude Control ◽  
Space Vehicles ◽  
Reaction Wheel

Advanced reaction wheel controller for attitude control of spacecraft

1982 ◽  
Vol 9 (12) ◽  
pp. 697-702 ◽  
Author(s):  
K. Tsuchiya ◽  
M. Inoue ◽  
N. Wakasuqi ◽  
T. Yamaguchi
Keyword(s):  
Attitude Control ◽  
Reaction Wheel

scholarly journals 5G Wireless/Wired Convergence of UFMC Based Modulation for Intensity Modulation Direct Detection

2021 ◽  
Author(s):  
Keerthivasan K ◽  
Shibu S
Keyword(s):  
Network Architecture ◽  
Direct Detection ◽  
Low Cost ◽  
Cost Effective ◽  
Theoretical Work ◽  
Base Stations ◽  
Complex Nature ◽  
Implementation Model ◽  
Effective Manner ◽  
Media Applications

Faster data speeds, shorter end-to-end latencies, improved end-user service efficiency, and a wider range of multi-media applications are expected with the new 5G wireless services. The dramatic increase in the number of base stations required to meet these criteria, which undermines the low-cost constraints imposed by operators, demonstrates the need for a paradigm shift in modern network architecture. Alternative formats will be required for next-generation architectures, where simplicity is the primary goal. The number of connections is expected to increase rapidly, breaking the inherent complexity of traditional coherent solutions and lowering the resulting cost percentage. A novel implementation model is used to migrate complex-nature modulation structures in a highly efficient and cost-effective manner. Theoretical work to analyses modulations’ behavior over a wired/fiber setup and wireless mode is also provided. The state-of-the-art computational complexity, simplicity, and ease of execution while maintaining efficiency throughput and bit error rate.

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