Optimizing inverted pendulum control: Integrating neural network adaptability

Research Article
Open access

Optimizing inverted pendulum control: Integrating neural network adaptability

Yusen Xie 1* , Yingjie Mi 2
  • 1 School of Engineering, The University of Sydney, Sydney, Australia    
  • 2 School of Engineering, The University of Sydney, Sydney, Australia    
  • *corresponding author yxie8714@uni.sydney.edu.au
Published on 8 November 2024 | https://doi.org/10.54254/2755-2721/101/20241008
ACE Vol.101
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-691-4
ISBN (Online): 978-1-83558-692-1

Abstract

This study explores the implementation and efficacy of a neural network controller for an inverted pendulum system, contrasting it with traditional state feedback control. Initially, state feedback control exhibited limitations in managing complex system dynamics. Subsequently, a neural network controller was developed, trained using datasets from both uncontrolled and refined state space models. The refined model yielded lower training loss and superior control performance. This research demonstrates the neural network controller’s enhanced adaptability and precision, offering significant improvements over traditional methods in controlling dynamic systems like inverted pendulums.

Keywords:

Inverted Pendulum, Control, Neural Network, Machine Learning, System Dynamics, Performance Optimization

Xie,Y.;Mi,Y. (2024). Optimizing inverted pendulum control: Integrating neural network adaptability. Applied and Computational Engineering,101,213-223.
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References

[1]. Hai-Wu Lee et al. “Research on the Stability of Biped Robot Walking on Different Road Surfaces”. In: ICKII. 2018, pp. 54–57. DOI: 10.1109/ICKII.2018.8569084.

[2]. S. H. Collins and A. Ruina. “A Bipedal Walking Robot with Efficient and Human-Like Gait”. In: Proceedings of the 2005 IEEE International Conference on Robotics and Automation. Barcelona, Spain, 2005, pp. 1983–1988. DOI: 10.1109/ROBOT.2005.1570404.

[3]. T. Sugihara, Y. Nakamura, and H. Inoue. “Real-time humanoid motion generation through ZMP manipulation based on inverted pendulum control”. In: Proceedings 2002 IEEE International Conference on Robotics and Automation. Vol. 2. Washington, DC, USA, 2002, pp. 1404–1409. DOI: 10.1109/ROBOT.2002.1014740.

[4]. Hanafiah Yussof and Masahiro Ohka. “Optimum Biped Trajectory Planning for Humanoid Robot Navigation in Unseen Environment”. In: (2010). DOI: 10.5772/9262.

[5]. Kiam Heong Ang, G. Chong, and Yun Li. “PID control system analysis, design, and technology”. In: IEEE Transactions on Control Systems Technology 13.4 (2005), pp. 559–576. DOI: 10.1109/ TCST.2005.847331.

[6]. Y. Okumura et al. “Realtime ZMP compensation for biped walking robot using adaptive inertia force control”. In: Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003). Vol. 1. Las Vegas, NV, USA, 2003, pp. 335–339. DOI: 10.1109/ IROS.2003.1250650.

[7]. S. Kajita et al. “Biped walking pattern generation by using preview control of zero-moment point”. In: 2003 IEEE International Conference on Robotics and Automation. Vol. 2. Taipei, Taiwan, 2003, pp. 1620–1626. DOI: 10.1109/ROBOT.2003.1241826.

[8]. Miomir Vukobratovic and Branislav Borovac. “Zero-Moment Point - Thirty Five Years of its Life”. In: I. J. Humanoid Robotics 1 (2004), pp. 157–173. DOI: 10.1142/S0219843604000083.

[9]. M. W. Spong. Inverted Pendulum: Simulink Modeling. Control Tutorials for MATLAB and Simulink, University of Michigan. 2008. URL: https://ctms.engin.umich.edu/ CTMS/index.php?example=InvertedPendulum&section=SimulinkModeling.

[10]. L. B. Prasad, B. Tyagi, and H. O. Gupta. “Optimal control of nonlinear inverted pendulum dynamical system with disturbance input using PID controller & LQR”. In: 2011 IEEE International Conference on Control System, Computing and Engineering. Penang, Malaysia, 2011, pp. 540–545. DOI: 10.1109/ICCSCE.2011.6190585.

[11]. David Rumelhart, Geoffrey Hinton, and Ronald Williams. “Learning representations by back- propagating errors”. In: Nature 323 (1986), pp. 533–536. URL: https://doi.org/10. 1038/323533a0.

Cite this article

Xie,Y.;Mi,Y. (2024). Optimizing inverted pendulum control: Integrating neural network adaptability. Applied and Computational Engineering,101,213-223.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2nd International Conference on Machine Learning and Automation

ISBN：978-1-83558-691-4(Print) / 978-1-83558-692-1(Online)
Editor：Mustafa ISTANBULLU
Conference website: https://2024.confmla.org/
Conference date: 12 January 2025
Series: Applied and Computational Engineering
Volume number: Vol.101
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Hai-Wu Lee et al. “Research on the Stability of Biped Robot Walking on Different Road Surfaces”. In: ICKII. 2018, pp. 54–57. DOI: 10.1109/ICKII.2018.8569084.

[2]. S. H. Collins and A. Ruina. “A Bipedal Walking Robot with Efficient and Human-Like Gait”. In: Proceedings of the 2005 IEEE International Conference on Robotics and Automation. Barcelona, Spain, 2005, pp. 1983–1988. DOI: 10.1109/ROBOT.2005.1570404.

[3]. T. Sugihara, Y. Nakamura, and H. Inoue. “Real-time humanoid motion generation through ZMP manipulation based on inverted pendulum control”. In: Proceedings 2002 IEEE International Conference on Robotics and Automation. Vol. 2. Washington, DC, USA, 2002, pp. 1404–1409. DOI: 10.1109/ROBOT.2002.1014740.

[4]. Hanafiah Yussof and Masahiro Ohka. “Optimum Biped Trajectory Planning for Humanoid Robot Navigation in Unseen Environment”. In: (2010). DOI: 10.5772/9262.

[5]. Kiam Heong Ang, G. Chong, and Yun Li. “PID control system analysis, design, and technology”. In: IEEE Transactions on Control Systems Technology 13.4 (2005), pp. 559–576. DOI: 10.1109/ TCST.2005.847331.

[6]. Y. Okumura et al. “Realtime ZMP compensation for biped walking robot using adaptive inertia force control”. In: Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003). Vol. 1. Las Vegas, NV, USA, 2003, pp. 335–339. DOI: 10.1109/ IROS.2003.1250650.

[7]. S. Kajita et al. “Biped walking pattern generation by using preview control of zero-moment point”. In: 2003 IEEE International Conference on Robotics and Automation. Vol. 2. Taipei, Taiwan, 2003, pp. 1620–1626. DOI: 10.1109/ROBOT.2003.1241826.

[8]. Miomir Vukobratovic and Branislav Borovac. “Zero-Moment Point - Thirty Five Years of its Life”. In: I. J. Humanoid Robotics 1 (2004), pp. 157–173. DOI: 10.1142/S0219843604000083.

[9]. M. W. Spong. Inverted Pendulum: Simulink Modeling. Control Tutorials for MATLAB and Simulink, University of Michigan. 2008. URL: https://ctms.engin.umich.edu/ CTMS/index.php?example=InvertedPendulum&section=SimulinkModeling.

[10]. L. B. Prasad, B. Tyagi, and H. O. Gupta. “Optimal control of nonlinear inverted pendulum dynamical system with disturbance input using PID controller & LQR”. In: 2011 IEEE International Conference on Control System, Computing and Engineering. Penang, Malaysia, 2011, pp. 540–545. DOI: 10.1109/ICCSCE.2011.6190585.

[11]. David Rumelhart, Geoffrey Hinton, and Ronald Williams. “Learning representations by back- propagating errors”. In: Nature 323 (1986), pp. 533–536. URL: https://doi.org/10. 1038/323533a0.

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