Fuzzy, Neural, and Neuro-Fuzzy Controllers for an Inverted Pendulum

This work presents the modeling, implementation, and analysis of the inverted pendulum system and the development and evaluation of various controllers for its stabilization. Since the inverted pendulum is highly nonlinear and unstable, its control poses a significant challenge in control theory. To address this challenge, advanced intelligent control techniques, including fuzzy, neural, and neuro-fuzzy control, offer greater adaptability and learning capability than traditional methods. These controllers are implemented in MatLab software, utilizing Simulink to design and simulate control systems. At the same time, a test environment is developed to evaluate the performance of each controller under different operating conditions, considering aspects such as stability, transient response, and adaptability to disturbances. Furthermore, the experimental results obtained from simulations and tests demonstrate that intelligent control techniques can be highly competitive compared to traditional controllers, as methods based on identification and adaptive modeling enhance the system’s stability and dynamic response, achieving better performance in terms of settling time, overshoot, and robustness against disturbances. Ultimately, this study highlights the potential of intelligent control for complex dynamic systems and underscores its applicability in a wide range of automation and robotics problems.