An intelligent control framework based on adaptive PID and reinforcement learning

With the advancement of control theory, complex dynamic systems face challenges like nonlinearity, parameter uncertainties, and external disturbances, which traditional PID control struggles to manage. This paper proposes a hybrid control framework combining PID control, reinforcement learning (RL), and large language models (LLM) to enhance system adaptability and intelligence. The PID controller is optimized for real-time control in dynamic environments, while RL continuously adjusts decisions based on feedback. LLMs reduce RL’s data and time requirements by aiding state analysis and control adjustments. Experimental results show that the proposed framework improves convergence speed, accuracy, and disturbance resistance over traditional methods, providing a novel approach to intelligent control of complex systems.