Intelligent Control Strategies for Autonomous Mechanical Systems Using Deep Learning

Autonomous mechanical systems increasingly require intelligent control mechanisms capable of adapting to dynamic, uncertain, and nonlinear operating environments. Traditional model-based and rule-driven control strategies often struggle to achieve robust performance under such conditions due to their limited generalization and reliance on precise system modeling. This paper presents an intelligent control framework for autonomous mechanical systems leveraging deep learning techniques to enable adaptive, data-driven decision making. The proposed approach integrates deep neural networks with real-time sensor feedback to learn optimal control policies directly from system interactions, reducing dependence on explicit mathematical models. A combination of convolutional and recurrent neural architectures is employed to capture spatial-temporal dynamics of mechanical states, while reinforcement learning is utilized for continuous action optimization. The framework is evaluated through simulation and experimental case studies involving autonomous robotic platforms, demonstrating improved stability, tracking accuracy, and adaptability compared to conventional control methods. Results indicate that deep learning-based controllers can effectively handle system uncertainties, external disturbances, and complex motion patterns, offering a scalable solution for next-generation autonomous mechanical systems in industrial, medical, and robotic applications.