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

Abstract

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.

Citation

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. https://doi.org/10.1371/journal.pone.0298093

• 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
• DOI: https://doi.org/10.1371/journal.pone.0298093
• Public URL: https://hull-repository.worktribe.com/output/4732226