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Control strategies for inverted pendulum: A comparative analysis of linear, nonlinear, and artificial intelligence
Saqib Irfan1, Liangyu Zhao1, Safeer Ullah2
1School of Aerospace Engineering, Beijing Institute of Technology, Beijing, China.
Plos One
|March 7, 2024
Summary
Controlling nonlinear inverted pendulum (IP) systems is complex. A comparative study shows Sliding Mode Control based Neural Networks (SMCNN) offers superior stabilization, outperforming linear and other nonlinear methods.
Area of Science:
- Robotics and Control Systems
- Nonlinear Dynamics
- Artificial Intelligence in Engineering
Background:
- The inverted pendulum (IP) is a classic underactuated system with inherent nonlinear dynamics.
- Developing effective control strategies for unstable systems like the IP is a significant challenge in control engineering.
Purpose of the Study:
- To provide an overview of inverted pendulum control systems.
- To comparatively analyze various control strategies including linear, nonlinear, and AI-based methods.
- To evaluate the performance and complexity trade-offs of different control approaches for IP stabilization.
Main Methods:
- A comprehensive literature review and simulation-based comparison of control strategies.
- Analysis of Linear Quadratic Regulators (LQR), Sliding Mode Control (SMC), Back-Stepping (BS), Fuzzy Logic Controllers (FLC), and SMC based Neural Networks (SMCNN).
- Performance evaluation based on parameters such as settling time, overshoot, and steady-state error.
Main Results:
- Nonlinear and AI-based methods effectively mitigate the nonlinearity and instability of the inverted pendulum.
- The SMC based Neural Network (SMCNN) controller demonstrated superior performance compared to LQR, SMC, FLC, and BS.
- SMCNN achieved better results in terms of settling time, overshoot, and steady-state error.
Conclusions:
- The SMCNN controller offers optimal performance for inverted pendulum stabilization, surpassing traditional and other advanced control techniques.
- While SMCNN provides superior results, it involves a higher system complexity compared to simpler methods.
- This study highlights the potential of neural networks, particularly SMCNN, for addressing complex control challenges in unstable systems.
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