Process Dynamics and Control Course

Matlab Dynamics and Control course with Live Scripts and the Temperature Control Lab
Updated 19 Aug 2022

Process Dynamics and Control in MATLAB

This course focuses on a complete start to finish process of physics-based modeling, data driven methods, and controller design. Although some knowledge of computer programming is required, students are led through several introductory topics that develop an understanding of numerical methods in process control. Students should start with the Begin MATLAB Short Course that takes 2-3 hours to complete.

course overview

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This course focuses on methods that are used in practice for simple or complex systems. It is divided into three main parts including (1) data driven modeling and controller development, (2) physics-based modeling and controller development, and (3) advanced controls with optimization. Example problems are provided throughout in the MATLAB programming language.

View Process Dynamics and Control Course on File Exchange


John Hedengren

John Hedengren leads the BYU PRISM group with interests in combining data science, optimization, and automation with current projects in hybrid nuclear energy system design and unmanned aerial vehicle photogrammetry. He earned a doctoral degree at the University of Texas at Austin and worked 5 years with ExxonMobil Chemical prior to joining BYU in 2011.

Teaching Assistant

Joshua Hammond

Joshua Hammond is an experienced researcher in Process Systems Engineering leveraging Data Science, Machine Learning, Optimization, and domain knowledge to achieve optimal solutions. Joshua developed the MATLAB dynamics and control course from the Process Dynamics and Control Course with technical support from Colin Anderson and Nathanael Nelson. Assignment solution videos are published to the Horizon PSE YouTube Channel.

Course Objectives

It is the intent of this course to help the student to:

  1. Understand and be able to describe quantitatively the dynamic behavior of process systems.
  2. Learn the fundamental principles of classical control theory, including different types of controllers and control strategies.
  3. Develop the ability to describe quantitatively the behavior of simple control systems and to design control systems.
  4. Develop the ability to use computer software to help describe and design control systems.
  5. Learn how to tune a control loop and to apply this knowledge in the laboratory.
  6. Gain a brief exposure to advanced control strategies.

Course Schedule

course overview

Class Topic Assignment TCLab Activity
L01 Course Introduction Begin MATLAB Begin MATLAB (Continued)
L02 Simulate Dynamics in MATLAB Simulate HIV Infection Step Test Simulation
L03 Physics-based Dynamic Modeling Derive Balance Equations Convective Heat Transfer
L04 Transient Balance Equations Tank Blending Simulation Radiative Heat Transfer
L05 Linearize Balance Equations Linearize Differential Equations Linearize Energy Balance
L06 First-Order Linear Dynamics with Dead Time using Graphical Fitting Methods Graphical FOPDT Fit TCLab Graphical FOPDT Fit
L07 Optimize Model Parameter Fit Parameter Regression Regression FOPDT
L08 Exam Review on Modeling and Dynamics Practice Exam
L09 Exam on Dynamic Modeling
Class Topic Assignment TCLab Activity
L10 Control Design Controller Design Exercise TCLab Controller Design
L11 Proportional-only (P-only) Control Tank Level P-only Control
L12 Proportional Integral (PI) Control Auto Cruise Control PI Control
L13 Proportional Integral Derivative (PID) Control Blending Control PID Control
L14 Case Study: Level Control Level Control PI Control Tuning
L15 Case Study: Nonlinear System Control Exothermic Reactor PID Control Tuning
L16 Case Study: Disturbances Type-I Diabetic Blood Glucose PID with Feedforward
L17 Valve Design Principles Valve Design Exercise Heater Actuator
L18 Sensors and Data Acquisition Sensor Design Exercise Temperature Sensor
L19 Exam Review on Sensors, Actuators, and Controllers Practice Exam
L20 Exam on Sensors, Actuators, and Controllers
Class Topic Assignment TCLab Activity
L21 Single Heater Modeling TCLab Project Overview Dual Heater Modeling 2
L22 Single Heater Regression Dual Heater Regression 2
L23 Single Heater Control 2 Page Report Dual Heater Control 2
L24 Laplace Transforms Laplace Transform Applications Impulse Response
L25 Transfer Functions Block Diagrams with Transfer Functions Block Diagram
L26 State Space Models Reactor State Space State Space Simulation
L27 Second Order Systems with Graphical Fitting Second Order Estimation: Graphical On/Off Control
L28 Second Order Optimization Second Order Estimation: Optimization Second Order Regression
L29 Simulation of FOPDT, SOPDT, and Higher Order Systems Distillation Control Higher Order Regression
L30 Stability Analysis Controller Stability Limits P-Only Stability Analysis
L31 Cascade Control and Feedforward Control Cascade or Feedforward Control Design Cascade Control
L32 Exam Review on Dynamic Systems Analysis Practice Exam
L33 Exam on Dynamic Systems Analysis
Class Topic Assignment TCLab Activity
L34 Control Project Introduction Control Project
L35 Optimization Introduction Control Project
L36 Linear Programming Control Project
L37 Scheduling Optimization Control Project
L38 Nonlinear Programming Control Project
L39 Machine Learning Classification Control Project
L40 Model Predictive Control Control Project
L41 Project Help Session Control Project
L42 Final Exam Review Practice Exam
Final Project Report (2 pages) and Presentation (5 min) Final Exam

Github Logo MATLAB and Python Repository on Github

Github Logo Course Web-site and Schedule

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TCLab Temperature Control Lab

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The materials in this archive are released under the MIT License. The financial assistance of MathWorks is gratefully acknowledged with technical assistance of Melda Ulusoy and others at MathWorks.

Cite As

John Hedengren (2024). Process Dynamics and Control Course (, GitHub. Retrieved .

Park, Junho, et al. “Benchmark Temperature Microcontroller for Process Dynamics and Control.” Computers &Amp\Mathsemicolon Chemical Engineering, vol. 135, Elsevier BV, Apr. 2020, p. 106736, doi:10.1016/j.compchemeng.2020.106736.

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To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.