Designing Adaptive PID Control Using Grey System Theory

This approach designs adaptive PID control using grey system theory, allowing for real-time online adjustment of PID parameters to adapt to environmental variations. The control method continuously fine-tunes parameters based on real-time environmental data, thereby delivering superior control performance. The application of grey system theory enhances PID control with intelligent adaptation capabilities, maintaining stable control effectiveness under diverse operating conditions. Key implementation aspects include: - Grey prediction models for forecasting system behavior - Real-time parameter optimization algorithms (e.g., GM(1,1) model) - Adaptive tuning mechanisms for proportional, integral, and derivative gains - Feedback loops integrating grey relational analysis This control methodology finds broad applications across industrial sectors including automated production lines, mechanical equipment control, and energy management systems, demonstrating robust performance in dynamic environments. The system typically employs modular code architecture with separate modules for grey prediction, PID calculation, and parameter adaptation, ensuring maintainability and scalability.