ADAPTIVE NEURO-FUZZY OPTIMIZATION OF PID CONTROLLERS WITH DIGITAL TWIN INTEGRATION FOR DYNAMIC SYSTEMS

Stelian BRAD, Emilia BRAD, Dragoș BARTOȘ

Abstract


This paper proposes a practical framework for improving the performance of PID controllers. The core novel is the consideration of the digital twin of the controlled system in the fine-tuning process of the PID regulator, with adaptive neuro-fuzzy inference algorithms (ANFIS). The method is based on real-time feedback to adjust in a dynamic manner PID parameters, addressing challenges of nonlinear behaviors. Contributions include a customized ANFIS architecture, an optimization-specific cost function, and a feedback loop linking intelligent control strategies with system modeling. A case study on temperature regulation in 3D printing systems demonstrates reduced overshoot, faster stabilization, and elimination of steady-state errors, with improved energy efficiency. Comparative analysis confirms the method’s superiority over conventional techniques and evolutionary advanced algorithms. This research advances adaptive control systems, providing robust solutions for dynamic and nonlinear systems.

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References


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