Deep Reinforcement Learning-Based Intelligent Control Systems For Industrial Automation
Keywords:
Deep reinforcement learning, industrial automation, intelligent control systems, robotics, machine learning, adaptive control, optimizationAbstract
Background: Industrial automation has evolved significantly with the
integration of intelligent technologies, enabling enhanced productivity,
precision, and operational efficiency. However, traditional control
strategies such as Proportional-Integral-Derivative (PID) controllers
and rule-based systems are limited in their ability to handle complex,
nonlinear, and dynamic industrial environments. Deep reinforcement
learning (DRL), a combination of reinforcement learning and deep
neural networks, has emerged as a powerful approach for developing
adaptive and autonomous control systems.
Objective: This study aims to design, implement, and evaluate a DRL-
based intelligent control system for industrial automation, focusing on improving system adaptability, efficiency, and decision-makingcapabilities in complex environments.
Methods: A simulation-based experimental framework was developed
using DRL algorithms, including Deep Q-Network (DQN) and Proximal
Policy Optimization (PPO). The system was implemented using Python,
TensorFlow, and OpenAI Gym. Industrial scenarios such as robotic arm
control and process optimization were simulated. Performance metrics
including convergence rate, energy consumption, response time, and
control accuracy were analyzed.
Key Findings: The results demonstrate that DRL-based control systems
outperform traditional approaches, achieving up to 30% improvement
in efficiency, reduced energy consumption, and faster adaptation
to environmental changes. PPO exhibited superior stability and
convergence compared to DQN.
Conclusion: DRL-based intelligent control systems offer a scalable and
robust solution for modern industrial automation, enabling real-time
decision-making and adaptive control in complex environments.
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