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An Adaptive Neuro-Fuzzy Model for Attitude Estimation and Control of a 3 DOF System

Author

Listed:
  • Xin Wang

    (Department of Kinesiology, Shenyang Sport University, Shenyang 110102, China)

  • Seyed Mehdi Abtahi

    (Department of Mechanical Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA)

  • Mahmood Chahari

    (Department of Mechanical Engineering, State University of New York at Binghamton, 4400 Vestal Parkway, Binghamton, NY 13902, USA)

  • Tianyu Zhao

    (Key Laboratory of Structural Dynamics of Liaoning Province, College of Sciences, Northeastern University, Shenyang 110819, China)

Abstract

In recent decades, one of the scientists’ main concerns has been to improve the accuracy of satellite attitude, regardless of the expense. The obvious result is that a large number of control strategies have been used to address this problem. In this study, an adaptive neuro-fuzzy integrated system (ANFIS) for satellite attitude estimation and control was developed. The controller was trained with the data provided by an optimal controller. Furthermore, a pulse modulator was used to generate the right ON/OFF commands of the thruster actuator. To evaluate the performance of the proposed controller in closed-loop simulation, an ANFIS observer was also used to estimate the attitude and angular velocities of the satellite using magnetometer, sun sensor, and data gyro data. However, a new ANFIS system was proposed that can jointly control and estimate the system attitude. The performance of the proposed controller was compared to the optimal PID controller in a Monte Carlo simulation with different initial conditions, disturbance, and noise. The results show that the proposed controller can surpass the optimal PID controller in several aspects including time and smoothness. In addition, the ANFIS estimator was examined and the results demonstrate the high ability of this designated observer. Consequently, evaluating the performance of PID and the proposed controller revealed that the proposed controller consumed less control effort for satellite attitude estimation under noise and uncertainty.

Suggested Citation

  • Xin Wang & Seyed Mehdi Abtahi & Mahmood Chahari & Tianyu Zhao, 2022. "An Adaptive Neuro-Fuzzy Model for Attitude Estimation and Control of a 3 DOF System," Mathematics, MDPI, vol. 10(6), pages 1-16, March.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:6:p:976-:d:774235
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    References listed on IDEAS

    as
    1. Zhankui Zeng & Shijie Zhang & Yanjun Xing & Xibin Cao, 2014. "Robust Adaptive Filter for Small Satellite Attitude Estimation Based on Magnetometer and Gyro," Abstract and Applied Analysis, Hindawi, vol. 2014, pages 1-7, May.
    2. Zhang, Wei & Li, Xiang & Ma, Hui & Luo, Zhong & Li, Xu, 2021. "Transfer learning using deep representation regularization in remaining useful life prediction across operating conditions," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    3. Gilberto Arantes & Luiz S. Martins-Filho & Adrielle C. Santana, 2009. "Optimal On-Off Attitude Control for the Brazilian Multimission Platform Satellite," Mathematical Problems in Engineering, Hindawi, vol. 2009, pages 1-17, November.
    4. Rahnavard, Mostafa & Ayati, Moosa & Hairi Yazdi, Mohammad Reza & Mousavi, Mohammad, 2019. "Finite time estimation of actuator faults, states, and aerodynamic load of a realistic wind turbine," Renewable Energy, Elsevier, vol. 130(C), pages 256-267.
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    Cited by:

    1. Omer Saleem & Faisal Abbas & Jamshed Iqbal, 2023. "Complex Fractional-Order LQIR for Inverted-Pendulum-Type Robotic Mechanisms: Design and Experimental Validation," Mathematics, MDPI, vol. 11(4), pages 1-21, February.

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