An industrial control engineering project focused on identifying the dynamic behavior of an unknown system, developing mathematical models from input-output data, and designing suitable controllers for accurate and stable operation.
Industrial control systems play an essential role in improving manufacturing processes, increasing productivity, reducing operating costs, and maintaining consistent process performance.
In many practical applications, the internal structure and parameters of a process are unknown. Therefore, an appropriate dynamic model must first be identified before designing a controller.
In this project, black-box system identification techniques are used to model an unknown dynamic system based on measured input and output data.
After identifying and validating the system model, classical controllers are designed and tuned using the Ziegler–Nichols method. Their performance is then evaluated under reference changes, input disturbances, and output disturbances.
- Analyze the dynamic behavior of an unknown system
- Perform black-box system identification
- Develop mathematical models using input-output data
- Compare and validate identified models
- Select an appropriate model for controller design
- Design and tune industrial controllers
- Apply the Ziegler–Nichols tuning method
- Analyze closed-loop system stability
- Evaluate reference-tracking performance
- Investigate input disturbances
- Investigate output disturbances
- Improve disturbance rejection
System identification is the process of developing a mathematical model of a dynamic system using measured input and output data.
In this project, a black-box identification approach is used. This method does not require detailed knowledge of the physical structure of the system.
The identification process includes:
- Generating or collecting input-output data
- Preprocessing the measured data
- Selecting candidate model structures
- Estimating model parameters
- Comparing model responses
- Validating the identified models
- Selecting the most suitable model
After selecting the identified model, suitable controllers are designed to improve the system response.
The desired controller should provide:
- Stable closed-loop operation
- Accurate reference tracking
- Reduced steady-state error
- Acceptable rise time
- Acceptable settling time
- Reduced overshoot
- Effective disturbance rejection
The Ziegler–Nichols method is used to obtain initial controller parameters.
The resulting controller is evaluated using MATLAB and Simulink simulations. The parameters may then be adjusted according to the system response and performance requirements.
The performance of the designed controller is evaluated under different disturbance conditions.
An input disturbance affects the signal entering the process or actuator.
Examples include:
- Changes in actuator input
- Load variations
- Power-supply variations
- Unexpected process-input changes
An output disturbance directly affects the controlled output.
Examples include:
- Environmental changes
- External process interference
- Load disturbances
- Measurement variations
The controller is evaluated according to its ability to return the output to the desired operating condition.
The identified models and designed controllers are evaluated using:
- Model accuracy
- Reference tracking
- Rise time
- Settling time
- Overshoot
- Steady-state error
- Closed-loop stability
- Input disturbance rejection
- Output disturbance rejection
- MATLAB
- Simulink
- System Identification
- Black-Box Modeling
- Dynamic System Modeling
- Ziegler–Nichols Tuning
- PID Control
- Feedback Control
- Disturbance Analysis
- Industrial Control
industrial-control-system-identification/
├── README.md
├── documentation/
│ └── industrial-control-project-report-fa.pdf
├── project-files/
│ ├── MATLAB source codes
│ ├── Simulink models
│ └── system identification models
└── results/
└── simulation and response figures