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Control strategies for inverted pendulum: A comparative analysis of linear, nonlinear, and artificial intelligence approaches

Irfan, Saqib; Zhao, Liangyu; Ullah, Safeer; Mehmood, Adeel; Butt, Muhammad Fasih Uddin


Saqib Irfan

Liangyu Zhao

Safeer Ullah

Muhammad Fasih Uddin Butt


An inverted pendulum is a challenging underactuated system characterized by nonlinear behavior. Defining an effective control strategy for such a system is challenging. This paper presents an overview of the IP control system augmented by a comparative analysis of multiple control strategies. Linear techniques such as linear quadratic regulators (LQR) and progressing to nonlinear methods such as Sliding Mode Control (SMC) and back-stepping (BS), as well as artificial intelligence (AI) methods such as Fuzzy Logic Controllers (FLC) and SMC based Neural Networks (SMCNN). These strategies are studied and analyzed based on multiple parameters. Nonlinear techniques and AI-based approaches play key roles in mitigating IP nonlinearity and stabilizing its unbalanced form. The aforementioned algorithms are simulated and compared by conducting a comprehensive literature study. The results demonstrate that the SMCNN controller outperforms the LQR, SMC, FLC, and BS in terms of settling time, overshoot, and steady-state error. Furthermore, SMCNN exhibit superior performance for IP systems, albeit with a complexity trade-off compared to other techniques. This comparative analysis sheds light on the complexity involved in controlling the IP while also providing insights into the optimal performance achieved by the SMCNN controller and the potential of neural network for inverted pendulum stabilization.


Irfan, S., Zhao, L., Ullah, S., Mehmood, A., & Butt, M. F. U. (2024). Control strategies for inverted pendulum: A comparative analysis of linear, nonlinear, and artificial intelligence approaches. PLoS ONE, 19(3 March), Article e0298093.

Journal Article Type Article
Acceptance Date Jan 17, 2024
Online Publication Date Mar 7, 2024
Publication Date Mar 1, 2024
Deposit Date Jul 4, 2024
Publicly Available Date Jul 4, 2024
Journal PLoS ONE
Print ISSN 1932-6203
Electronic ISSN 1932-6203
Publisher Public Library of Science
Peer Reviewed Peer Reviewed
Volume 19
Issue 3 March
Article Number e0298093
Public URL


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Copyright Statement
Copyright: © 2024 Irfan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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