Stabilization of a Cart-Pendulum System Using the Pole Placement Technique

Authors

DOI:

https://doi.org/10.4108/eetsmre.11143

Keywords:

Cart Inverted Pendulum, Pole Placement Control, State-Feedback Control, Linearization, Underactuated Systems, MATLAB Simulation

Abstract

This study addresses the stabilization of the Cart Inverted Pendulum (CIP), a system widely recognized as a foundational challenge in control theory because of its unstable, nonlinear, and under controlled properties. Achieving stability in the CIP is essential for developing control methods for modern applications like robotics. This paper implements the Pole Placement method to ensure the system remains stable. The method begins by linearizing the nonlinear system dynamics about the unstable vertical configuration and expressing them in a state-variable form. After confirming the system's controllability, a state-feedback controller is designed by strategically assigning closed-loop pole locations. This placement dictates the system's performance, allowing for specific targets for damping and setting time. The controller's effectiveness is demonstrated through numerical simulations in MATLAB/Simulink, which confirm the system's successful transition from instability to stable, regulated control of both the angle of pendulum and the position of cart. This analysis also illuminates the essential trade-off between the system's response speed and the required energy control. The work ultimately affirms that Pole Placement is a highly effective, understandable, and computationally practical technique for stabilizing complex underactuated systems.

References

[1] S. Mahmoudi Rashid, A. Rikhtehgar Ghiasi, and S. Ghaemi, “A new distributed robust H∞ control strategy for a class of uncertain interconnected large-scale time-delay systems subject to actuator saturation and disturbance,” Journal of Vibration and Control, vol. 31, no. 13–14, pp. 2651–2675, Jul. 2024, doi: 10.1177/10775463241259345.

[2] V. T. Duong, C. T. Truong, T. T. Nguyen, H. H. Nguyen, and T. T. Nguyen, “State space model identification using model reference adaptive approach: software and hardware-in-the-loop simulation,” Cogent Eng, vol. 11, no. 1, 2024, doi: 10.1080/23311916.2024.2434938.

[3] Nhut Thang Le, Cong Toai Truong, Huy Hung Nguyen, Tan Tien Nguyen, and Van Tu Duong, “Position Tracking Control of a Permanent Magnet Steering Wheel Equipped to a Vertical Tank Robot: From Theory, Simulation to Practice,” Measurement and Control, pp. 1–18, 2025, doi: 10.1177/00202940251372834.

[4] Nhut Thang Le, Minh Tri Nguyen, Cong Toai Truong, Van Tu Duong, and Huy Hung Nguyen, “Recursive Least Squares Algorithm for Online Parameter Identification of DC Motor: Theory and Practice,” in 10th International Conference on Engineering and Emerging Technologies, Dubai: IEEE, Dec. 2024, pp. 27–28. doi: 10.1109/ICEET65156.2024.10913584.

[5] N. T. Le et al., “Development of a Multi-Suspension Unit for Solar Cleaning Robots to Mitigate Vibration Impact on Photovoltaic Panels,” Applied Sciences (Switzerland), vol. 13, no. 22, Nov. 2023, doi: 10.3390/app132212104.

[6] M. T. Nguyen, C. T. Truong, V. T. Nguyen, V. T. Duong, H. H. Nguyen, and T. T. Nguyen, “Research on Adhesive Coefficient of Rubber Wheel Crawler on Wet Tilted Photovoltaic Panel,” Applied Sciences (Switzerland), vol. 12, no. 13, Jul. 2022, doi: 10.3390/app12136605.

[7] N. T. Le, C. T. Truong, H. H. Nguyen, T. T. Nguyen, and V. T. Duong, “Yaw angle determination of a mobile robot operating on an inclined plane using accelerometer and gyroscope,” Measurement, vol. 247, p. 116806, 2025, doi: 10.1016/j.measurement.2025.116806.

[8] N. T. Le et al., “Development of Two-Axes Gimbal System Testing Platform for Validating Tilt Sensor Precision,” in Recent Advances in Electrical Engineering and Related Sciences: Theory and Application, Singapore: Springer Nature Singapore, 2025, pp. 203–209. doi: 10.1007/978-981-96-4573-2_17.

[9] C. T. Truong et al., “Model identification of ventilation air pump utilizing Ridge-momentum regression and Grid-based structure optimization,” Mathematical Biosciences and Engineering 2025 8:2020, vol. 22, no. 8, pp. 2020–2038, 2025, doi: 10.3934/MBE.2025074.

[10] D. K. Nguyen, C. T. Truong, V. T. Duong, H. H. Nguyen, and T. T. Nguyen, “Model identification of two double-acting pistons pump,” Journal of Advanced Marine Engineering and Technology, vol. 47, no. 2, pp. 59–65, Apr. 2023, doi: 10.5916/jamet.2023.47.2.59.

[11] Cong Toai Truong et al., “Model Identification of Two Double-Acting Pistons Pump A NARX Network Approach,” in International Conference on Ubiquitous Robots, 2023. doi: 10.1109/UR57808.2023.10202388.

[12] Trung Dat Phan, Ly Xuan Truong Pham, Cong Toai Truong, Van Tu Duong, and Huy Hung Nguyen, “Adaptive Sliding Mode Control for a Blower-Based Breathing Simulator with Unknown and Time-Varying Airway Resistance and Compliance,” in 10th International Conference on Engineering and Emerging Technologies, Dubai: IEEE, Dec. 2024, pp. 27–28. doi: 10.1109/ICEET65156.2024.10913788.

[13] Cong Toai Truong, Kim Hieu Huynh, Van Tu Duong, Huy Hung Nguyen, Le An Pham, and Nguyen Tan Tien, “Model free volume and pressure cycled control of automatic bag valve mask ventilator,” AIMS Bioeng, pp. 192–207, 2021, doi: 10.3934/bioeng.2021017.

[14] Cong Toai Truong, Kim Hieu Huynh, Van Tu Duong, Huy Hung Nguyen, Le An Pham, and Tan Tien Nguyen, “Linear regression model and least square method for experimental identification of AMBU bag in simple ventilator,” International Journal of Intelligent Unmanned Systems, vol. 11, no. 3, pp. 378–395, Jun. 2023, doi: 10.1108/IJIUS-07-2021-0072.

[15] S. Ren, L. Han, J. Mao, and J. Li, “Optimized Trajectory Tracking for Robot Manipulators with Uncertain Dynamics: A Composite Position Predictive Control Approach,” Electronics (Switzerland), vol. 12, no. 21, Nov. 2023, doi: 10.3390/electronics12214548.

[16] L. Author et al., “Design and Implementation of Pole Placement Controllers,” IEEE Access, vol. 8, pp. 11245–11256, 2020.

[17] N. Author et al., “MATLAB-Based Simulation of Inverted Pendulum Systems,” Simulation Modelling Practice and Theory, vol. 96, 2020.

[18] O. Author and P. Author, “Trade-Off Analysis in Pole Placement Control Design,” Journal of Dynamic Systems, Measurement, and Control, vol. 142, 2020.

[19] M. Rani and S. S. Kamlu, “Optimal LQG controller design for inverted pendulum systems,” Scientific Reports, 2025

[20] S. Engelsman and F. Klein, “Inertial-based LQG control: A new look at inverted pendulum stabilization,” arXiv:2503.18926, 2025

[21] S. Farkhooi, Embedded model predictive control of the Furuta pendulum, M.S. thesis, Dept. Information Technology, Uppsala Univ., Uppsala, Sweden, 2025

[22] D.-B. Pham, Q.-T. Dao, and T.-V.-A. Nguyen, “Optimized hierarchical sliding mode control for the swing-up and stabilization of a rotary inverted pendulum,” Automation, vol. 5, no. 3, pp. 282–296, 2024

[23] S. Adamiat, W. Kouw, B. van Erp, and B. de Vries, “Message passing-based Bayesian control of a cart-pole system,” in Active Inference: 5th Int. Workshop, IWAI 2024, 2024, pp. 209–221

[24] D. Ju, J. Lee, and Y. S. Lee, “Transition control of a rotary double inverted pendulum using direct collocation and sim-to-real reinforcement learning,” Mathematics, vol. 13, no. 4, art. 640, 2025

[25] ME C134 / EE C128 Course Staff, Lab 6a: Pole placement for the inverted pendulum, Univ. of California, Berkeley, Berkeley, CA, USA, course notes, 2018

[26] S. Sawant, A. S. Anand, D. Reinhardt, and S. Gros, “Learning-based MPC from big data using reinforcement learning,” arXiv:2301.01667, 2023

[27] A. B. Kordabad, D. Reinhardt, A. S. Anand, and S. Gros, “On the improvement of model-predictive controllers,” preprint, 2023

[28] G. Rigatos, M. Abbaszadeh, P. Siano, G. Cuccurullo, J. Pomares, and B. Sari, “Nonlinear optimal control for the rotary double inverted pendulum,” Advanced Control for Applications: Engineering and Industrial Systems, vol. 6, no. 2, art. 140, 2024.

[29] Z. Ben Hazem, “Study of Q-learning and deep Q-network learning control for a rotary inverted pendulum system,” Discover Applied Sciences, vol. 6, art. 49, 2024.

[30] D. Ju, T. Lee, and Y. S. Lee, “Implementation of 12 transition controls for rotary double inverted pendulum using direct collocation,” in Proc. 21st Int. Conf. on Informatics in Control, Automation and Robotics (ICINCO), Porto, Portugal, 2024, pp. 92–100.

[31] Z. Ben Hazem, “Study of Q-learning and deep Q-network learning control for a rotary inverted pendulum system,” Discover Applied Sciences, vol. 6, art. 49, 2024.

[32] R. Hernandez, R. Garcia-Hernandez, and F. Jurado, “Modeling, simulation, and control of a rotary inverted pendulum: A reinforcement learning-based control approach.

[33] A. M. Al Juboori, M. T. Hussein, and A. S. G. Qanber, “Swing-up control of double-inverted pendulum systems,” Mechanical Sciences, vol. 15, pp. 47–54, 2024.

[34] Y. Wang, H. Guo, S. Wang, L. Qian, and X. Lan, “Bootstrapped model predictive control,” in Proc. 13th Int. Conf. on Learning Representations (ICLR), Singapore, 2025.

[35] W. Acuña-Bravo, A. G. Molano-Jiménez, and E. Canuto, “Embedded model control for underactuated systems: An application to Furuta pendulum,” Control Engineering Practice, vol. 113, p. 104854, 2021

[36] Indrazllo Siradjuddin et al., “Stabilising A Cart Inverted Pendulum System Using Pole Placement Control Method”, IEEE, 2017.

Downloads

Published

14-01-2026

How to Cite

1.
Bui Khanh Minh Q, Nguyen Ngoc H, Duong Thanh T. Stabilization of a Cart-Pendulum System Using the Pole Placement Technique. EAI Endorsed Sust Man Ren Energy [Internet]. 2026 Jan. 14 [cited 2026 Jan. 14];2(3). Available from: https://publications.eai.eu/index.php/sumare/article/view/11143