Neural Networks Application for the Data of PID Controller for Acrobot

Area of Science:

• Robotics and Control Systems
• Artificial Intelligence
• Computational Engineering

Background:

• Acrobot systems exhibit complex operating states and locomotion processes, necessitating sophisticated control algorithms.
• Traditional control methods face limitations in complex environments without software fine-tuning.
• Designing effective control strategies is crucial for Acrobot system performance and reliability.

Purpose of the Study:

• To propose and evaluate novel design problems and control strategies for Acrobot systems.
• To integrate Artificial Neural Networks (ANN) with Proportional-Integral-Derivative (PID) controllers for enhanced Acrobot control.
• To improve system security against potential cyber-attacks on automatic control systems.

Main Methods:

• Development of mathematical models and simulations using MATLAB and Simulink.
• Application of ANN for PID controller data to create a closed-form solution.
• Adjustment of simulation parameters to meet predefined criteria for intuitive interpretation.

Main Results:

• Simulation data provided more accurate system performance analysis than previous methods.
• The integration of ANN with PID controllers demonstrated enhanced control capabilities.
• The proposed methods confirmed the effectiveness of ANN in artificial intelligence applications for control systems.

Conclusions:

• The study successfully demonstrated the effectiveness of ANN as an intelligent control method for Acrobot systems.
• Neural network applications are highly versatile and can be synchronized with other control methods.
• The proposed control strategies offer new insights for vocational education, scientific research, and system security.