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Temperature Control Lab

The temperature control lab is an application of feedback control with an Arduino, an LED, two heaters, and two temperature sensors. The heater power output is adjusted to maintain a desired temperature setpoint. Thermal energy from the heater is transferred by conduction, convection, and radiation to the temperature sensor. Heat is also transferred away from the device to the surroundings. This lab is a resource for model identification and controller development. It is a pocket-sized lab with software in Python, MATLAB, and Simulink for the purpose of reinforcing control theory for students. Learning modules include:

Portable Temperature Control Lab for Learning Process Control

This lab teaches principles of system dynamics and control. In particular, this lab reinforces:

Student Resources (MATLAB/Simulink and Python)

Instructor and Development Resources

Lab Instructions

Steps 1-3 with the single heater should be completed with this lab. The dual heater models and advanced control modules are included as additional but optional information.

A report is due at the end of the project that details the modeling, parameter estimation, and control performance. Each student should complete the project and report individually.

SimTune for TCLab

SimTune from APCO, Inc. provides a convenient interface to the TCLab that does not require programming in MATLAB or Python. It is a full-featured software package to implement modeling and PID tuning principles. The SimTune software has a library of simulated process scenarios for modeling and PID controller tuning. The software includes common forms learned in this course as well as common industrial forms such as Honeywell Equations (A, B, C Equations), Allen Bradley (Dependent and Independent Equations), and Control Microsystems (ScadaPack). The licensed software is freely available for those who are registered for the course, thanks to a donation by APCO, Inc.

Advanced Control Methods

The temperature control lab is also used for Advanced Estimation and Control in the Dynamic Optimization Course. The difference between the PID lab and the advanced control methods is that the model is directly used to control the process versus only for tuning correlations. This approach is called Model Predictive Control (MPC) because the simulated system is driven to a desired set point with the use of an optimizer. Also, instead of estimating the model once from step tests, Moving Horizon Estimation (MHE) updates the model with every new measurement. The updated model is transferred to MPC for improved performance through adaptive control.

References

  1. Rossiter, J.A., Jones, B.L., Pope, S., Hedengren, J.D., Evaluation and Demonstration of Take Home Laboratory Kit, Invited Session: Demonstration and poster session, 12th IFAC Symposium on Advances in Control Education, July 7-9, 2019, 52 (9), pp. 56-61, Philadelphia, PA, USA. Preprint
  2. Hedengren, J.D., Martin, R.A., Kantor, J.C., Reuel, N., Temperature Control Lab for Dynamics and Control, AIChE Annual Meeting, Orlando, FL, Nov 2019. Abstract
  3. Oliveira, P.M., Hedengren, J.D., An APMonitor Temperature Lab PID Control Experiment for Undergraduate Students, 24th IEEE Conference on Emerging Technologies and Factory Automation (ETFA), Sep 10th - 13th, 2019, pp. 790-797, Zaragoza, Spain. Preprint
  4. Park, J., Patterson, C., Kelly, J., Hedengren, J.D., Closed-Loop PID Re-Tuning in a Digital Twin By Re-Playing Past Setpoint and Load Disturbance Data, AIChE Spring Meeting, New Orleans, LA, April 2019. Abstract

Additional Activities

Additional activities are available with each homework assignment in the Process Dynamics and Control Course. The TCLab assignments are listed in the right column of the course schedule with the associated background lecture material and the simulation assignment in the other columns. The TCLab assignments reinforce lecture material with TCLab activities.

Model Development

Control Development