This project focuses on identifying dynamic system models from step response data using classical process identification techniques widely applied in control engineering.
- Ziegler-Nichols
- Hägglund
- Smith (1st and 2nd order models)
- Sundaresan & Krishnaswamy
- Mollenkamp
The identified models are evaluated using:
- Mean Squared Error (MSE)
- IAE (Integral of Absolute Error)
- ISE (Integral of Squared Error)
- ITAE (Integral of Time-weighted Absolute Error)
Six different datasets are analyzed, each representing a system under a unit step input.
- Compare classical identification techniques
- Analyze model accuracy and robustness
- Provide reproducible benchmarks for control applications
- Python
- Process control
- Industrial system modeling
- Controller design (PID tuning)
This project is part of an academic study in control systems engineering, with a strong focus on practical implementation and performance evaluation.