School of Aerospace Engineering, Beijing Institute of Technology, Beijing, China.
Department of Electrical Engineering, Quaid-e-Azam College of Engineering & Technology, Sahiwal, Pakistan.
PLoS One. 2024 Mar 7;19(3):e0298093. doi: 10.1371/journal.pone.0298093. eCollection 2024.
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.
倒立摆是一种具有非线性行为的挑战性欠驱动系统。为这样的系统定义有效的控制策略是具有挑战性的。本文介绍了倒立摆控制系统的概述，并对多种控制策略进行了比较分析。线性技术，如线性二次调节器（LQR），以及非线性方法，如滑模控制（SMC）和反推控制（BS），以及人工智能（AI）方法，如模糊逻辑控制器（FLC）和基于 SMC 的神经网络（SMCNN）。这些策略基于多个参数进行了研究和分析。非线性技术和基于人工智能的方法在减轻倒立摆的非线性和稳定其不平衡形式方面发挥着关键作用。通过进行全面的文献研究，对上述算法进行了模拟和比较。结果表明，在稳定时间、超调量和稳态误差方面，SMCNN 控制器优于 LQR、SMC、FLC 和 BS。此外，与其他技术相比，SMCNN 对于倒立摆系统表现出更好的性能，尽管存在复杂性权衡。这种比较分析揭示了控制倒立摆所涉及的复杂性，同时也提供了关于 SMCNN 控制器实现的最优性能以及神经网络对于倒立摆稳定的潜力的见解。
