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Process Control

Process Dynamics and Control for Chemical Engineering Education

Use this page to plan a course, add a control assignment, build a laboratory, learn a skill, or find a process dataset. The resources connect control theory with chemical process models, instrumentation, computation, and engineering decisions.

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Resource key

  • Best use shows where a resource fits.
  • Audience identifies instructors, learners, or both.
  • Level shows the background knowledge needed.
  • Time gives an estimated workload.
  • Scale describes course scope, model size, or setup effort.
  • Runs where distinguishes browser, desktop, local server, and hosted services.
  • Data location shows whether code and prompts stay on a device or go to a service.
  • Access covers cost, login, and reuse terms.
  • Watch for flags limits before assigning a resource.

For instructors

Choose the size of your change

Ways to add process control learning to a course
Change Time What to add Good starting point
One class 30 to 75 minutes A prediction, simulation, or tuning task followed by an engineering check LearnChemE process control resources
One assignment One to two weeks A dynamic model, controller design, simulation, and short interpretation Ready-to-adapt assignments
Laboratory sequence Three to six meetings Step testing, parameter estimation, PID tuning, disturbance tests, and reflection Temperature Control Lab
Course redesign One term Modeling, feedback, instrumentation, data, computation, projects, and AI-aware assessment Course models below

Learning goals for a current control course

Outcomes that connect theory with process practice
Area Students should be able to Evidence
Dynamic modeling Derive and simulate dynamic models from balances, constitutive relations, and operating assumptions Equations, units, initial conditions, and limiting-case checks
System response Connect poles, time constants, zeros, gain, delay, and damping to process behavior Plots plus a physical explanation
Feedback design Select, tune, and compare controllers for setpoint and disturbance goals Closed-loop tests and justified performance measures
Instrumentation Choose measurements, manipulated variables, valves, alarms, and control structures P&ID or control narrative with failure considerations
Advanced control Explain constraints, interactions, estimation, and prediction in multivariable or model predictive control A checked simulation or design study
Data and computation Estimate models from process data and distinguish identification error from control performance Code, residuals, validation data, and reproducible settings
Professional judgment Evaluate stability, safety, uncertainty, operability, and the limits of a model or generated answer Engineering review and documented assumptions

A staged course plan

Example progression from process response to plantwide decisions
Stage Core topics Student activity Useful resource
1. Dynamic thinking Accumulation, state, time scale, linearization, and simulation Predict a response before running the model MIT OpenCourseWare 10.450
2. Process response First and second order systems, delay, frequency response, and empirical models Fit a model to a step test and inspect residuals BYU Process Dynamics and Control
3. Feedback Stability, PID, tuning, anti-windup, and performance Compare two tuning methods under a disturbance and constraint ControlGuru
4. Structure and hardware Sensors, valves, cascade, feedforward, ratio, and override control Create a control narrative and test an instrument fault Process Control Design and Practice
5. Estimation and interactions System identification, observers, multivariable systems, and relative gain Estimate a model from held-out data and select control pairings SIPPY examples
6. Constrained control MPC, optimization, state estimation, and plantwide goals Test constraints, mismatch, disturbances, and recovery do-mpc CSTR example
7. Integrated project Safety, economics, data, communication, and responsible tool use Defend a control design with code, tests, and an operating recommendation Cases and datasets

Ready-to-adapt assignments

Assignments with clear engineering outputs
Assignment Time Suggested source What students submit
Model a tank or reactor One week
Introductory		 Notre Dame notebooks Balance equations, assumptions, code, plots, unit checks, and a limiting case
Identify a process from data One to two weeks
Intermediate		 BYU course or SIPPY Training and validation split, fitted model, residual plots, uncertainty, and intended use
Tune and stress-test PID One laboratory
Intermediate		 TCLab or LearnChemE simulations Tuning choice, disturbance and noise tests, actuator behavior, and performance comparison
Design a control structure Two weeks
Intermediate		 Process Control Design and Practice P&ID markup, pairing choices, alarm and interlock discussion, and startup considerations
Review control-system cybersecurity One to two weeks
Advanced		 NIST SP 800-82 Rev. 3 Process and network boundary, data flows, threat scenarios, safety and availability effects, layered protections, and incident response priorities
Investigate an industrial fault Two to four weeks
Advanced		 Tennessee Eastman or IndPenSim Fault definition, detection delay, false alarms, process explanation, and operator response
Design and review MPC Three to five weeks
Advanced		 Fired heater, adhesive coater, or do-mpc CSTR Model, constraints, estimator assumptions, scenarios, benchmark controller, and limitations
Audit an AI-assisted solution One week
Intermediate		 GenAI guidance below Saved prompt or interaction summary, corrected solution, tests, sources, and explanation of rejected suggestions

For learners

A path from dynamic models to a checked control project
Step Start here What to do Result
1. Build a dynamic model BYU Process Dynamics and Control Derive one model from balances and predict its response before simulating it One to two weeks
Checked model and response plots
2. Learn feedback ControlGuru and LearnChemE Practice stability, PID tuning, setpoint tracking, and disturbance rejection Two to four weeks
Controller comparison
3. Work with data SIPPY examples Fit a model with one dataset and evaluate it on different data One to two weeks
Model and validation report
4. Add hardware or a realistic case TCLab or an industrial case Test noise, constraints, mismatch, disturbances, and recovery Two to four weeks
Scenario results
5. Complete a project Assignment ideas State the control goal, compare a baseline, test failure cases, and explain what an operator should do Three to six weeks
Reproducible project and oral explanation

No-cost local path: combine the BYU or Notre Dame course materials with python-control or the GNU Octave control package, then use the TCLab emulator before adding hardware.

Control problem workflow

  1. State the process, operating point, controlled variables, manipulated variables, disturbances, measurements, and constraints.
  2. Write the balances and assumptions. Check dimensions, signs, initial conditions, and steady state.
  3. Predict the important response features before running software.
  4. Choose a simple baseline controller before adding complexity.
  5. Test setpoint changes, disturbances, noise, model mismatch, saturation, and sensor or actuator faults.
  6. Inspect solver messages, time steps, tolerances, and sensitivity to parameters.
  7. Explain stability, safety, performance, uncertainty, and the range where the result can be trusted.

Courses, books, and teaching materials

Course models

Courses that instructors can adopt or adapt
Resource Audience and level What is included Best use Access and limits
Process Dynamics and Control
Brigham Young University		 Instructor and learner
Senior undergraduate		 Syllabus, schedule with assignments, notes, quizzes, projects, Python, MATLAB, Simulink, PID, feedforward, cascade, MPC, and TCLab Full chemical engineering control course Free online. The Python repository has no stated license. The MATLAB repository uses MIT. An earlier MATLAB and Simulink course remains available as an archive.
Process Control
University of Notre Dame, 2026		 Instructor and learner
Senior undergraduate		 Current syllabus, schedule, and assignments with modeling, TCLab, PID, optimization, dynamic optimization, and MPC Current course sequence and topic planning Open course site. Some videos, submissions, and solutions use institutional systems. Check individual notebooks for reuse terms.
CBE30338 Chemical Process Control
University of Notre Dame		 Instructor and learner
Senior undergraduate		 More than 70 Jupyter notebooks on modeling, PID, optimization, estimation, and predictive control Notebook-based course or selected assignments Free and MIT licensed. The collection is archived, so test dependencies before class.
Process Control
LearnCHE and McMaster		 Instructor and learner
Senior undergraduate		 Archived course outline, videos, calendar, seven assignments with solutions, projects, and examinations Course structure, instructor planning, and legacy problem sets Free 2014 archive. Most LearnCHE materials use CC BY-SA 4.0, but check each item. Review old software and external links before assigning them.
Process Control Education
McMaster University		 Instructor and learner
Senior undergraduate		 Flipped course, Marlin textbook, e-lessons, quizzes, tutorials, slides, workshops, a sample 15-week course, and learning support materials. The ASEE case study explains the redesign. Complete course design and flipped lessons Free to study on an older HTTP-only site. Link to the work instead of redistributing it.
MIT OpenCourseWare 10.450 Instructor and learner
Undergraduate		 Lecture notes, 11 problem sets and a paper assignment, MATLAB files, and spreadsheets Problem sets and modeling notes Free under MIT OpenCourseWare terms. The course is from 2006.
Process Control Design and Practice
Rowan University		 Instructor and learner
Senior undergraduate		 Slides, text, syllabus, projects, P&IDs, hardware, architecture, batch control, HMI, and specifications Practice-focused companion to a theory course Core materials are free. Reuse terms are not clear for every item. Some tests and solutions require contacting the author.
Process Control Educational Resources
Villanova University		 Instructor and learner
Intermediate		 Inverted-class MATLAB and Simulink modules on ODEs, Laplace transforms, and PID Short MATLAB-based modules Academic use only. MATLAB and Simulink are required.
Process Control and Instrumentation and Advanced Process Control
NPTEL		 Learner and instructor
Undergraduate to graduate		 Video-based introductory and advanced courses with notes and assessment material Self-study or supplemental lectures Free to view. Certification may cost money. Reuse rights are not clear.
Numerical Optimal Control
University of Freiburg		 Instructor and learner
Graduate		 Lectures, manuscript, exercises, code, solutions, examinations, and project guidance using Python or MATLAB with CasADi Advanced optimal-control module or graduate follow-on course Free online archive. Plan for 10 to 20 hours per week. Reuse rights are not stated.

Books and reference collections

Open and commercial references for process dynamics and control
Resource and author Level and publication Topics and best use Access and limits
Chemical Process Dynamics and Controls
Woolf		 Undergraduate
Living open text		 Modeling, instrumentation, PID, multivariable systems, optimization, statistics, and cases CC BY 3.0. Student-contributed chapters have uneven depth, so review assigned sections.
Process Control: Designing Processes and Control Systems for Dynamic Performance
Marlin		 Undergraduate
Second edition, 2000		 Full chemical process control text with supporting course materials Free to read through McMaster. Use links and citations rather than redistributing the full work.
Lessons in Industrial Instrumentation
Kuphaldt		 Introductory to intermediate
Living open reference		 Instrumentation, sensors, valves, P&IDs, PID, PLCs, troubleshooting, and worksheets CC BY 4.0. The collection is large and broader than a typical chemical engineering course.
Feedback Systems
Astrom and Murray		 Intermediate to graduate
Second edition materials		 General feedback theory, exercises, lectures, software, and optimization-based control A complete PDF and supporting materials are free online. Site content is CC BY-NC-SA 4.0 unless noted otherwise. Check the book PDF terms before redistribution.
CACHE Virtual Process Control Book Advanced undergraduate or graduate
Legacy archive		 Instructor-selected readings on multivariable, robust, nonlinear, optimal, and model predictive control Free archive. Rights vary by item and some external links are old.
Process Dynamics and Control
Seborg, Edgar, Mellichamp, and Doyle		 Undergraduate
Fourth edition, 2016		 Established chemical engineering textbook and course reference Commercial. Companion presentation slides are copyrighted supplements.
Principles and Practice of Automatic Process Control
Smith and Corripio		 Undergraduate
Third edition, 2005		 Applied industrial control strategies, modeling, tuning, plantwide control, and case studies Commercial and older. Confirm availability and companion software before adoption.
Process Control: Modeling, Design, and Simulation
Bequette		 Undergraduate or graduate
Second edition, 2023		 Modeling, controller design, safety, digital control, and current MATLAB and Simulink examples Commercial. Check student price and software access before adoption.
Process Dynamics, Modeling, and Control
Ogunnaike and Ray		 Advanced undergraduate or graduate
1994		 Detailed chemical process dynamics and multivariable control reference Commercial textbook. Advanced for some undergraduate sequences.
Model Predictive Control: Theory, Computation, and Design
Rawlings, Mayne, and Diehl		 Graduate
Second edition, 2026		 MPC theory, computation, code, figures, homework guidance, examinations, and errata Downloadable text and teaching supplements. Copyright remains with the publisher, so link to the files rather than redistributing them.
Chemical Process Control
Riggs		 Intermediate
Legacy edition		 Practice-oriented chemical process control reference Commercial and older. Use the library catalog to confirm local availability.

Simulations, laboratories, and hardware

Activities from browser exercises to physical control systems
Resource Best use Scale and runs where Access and limits
LearnChemE Process Control Resources Short screencasts, quiz screencasts, ConcepTests, simulations, and virtual laboratories Minutes to several class meetings
Browser and downloadable activities		 Public learner resources. Instructor-only materials require an approved university account. Terms vary by item.
LearnChemE process control simulations and virtual laboratories PID, dynamics, and simulated equipment activities without local hardware One class to a multi-session assignment
Browser		 Free to use online. Simulated equipment does not replace physical laboratory practice.
Temperature Control Lab and advanced TCLab sequence Hands-on modeling, parameter estimation, PID, state estimation, MPC, and MHE Emulator or Arduino-based kit
Local Python, MATLAB, or Simulink		 The emulator lowers setup effort. Physical work needs a kit, USB access, power, support, and lab time. The Python package uses Apache licensing.
Using Microcontrollers in Chemical Engineering Classrooms
AIChE		 Faculty introduction with parts lists, code, execution advice, and Python or MATLAB examples One-hour instructor development
Browser		 Login is required. Reuse is restricted.
Control101 MATLAB Toolbox Live scripts, animations, virtual laboratories, and authentic control scenarios Several activities
MATLAB desktop		 The page identifies BSD and CC BY-NC-SA terms for different materials. Check the selected item.
University of Almeria Control Education Hub PID, loop shaping, dead time, interactions, feedforward, interactive tools, and virtual or remote labs Browser and desktop tools
Varied setup		 Some materials accompany books or need local software.
UNED Interactive Control Tools
Linear Control System Design
Closed-Loop Shaping
Time Series Analysis and Time Series Generator		 Linear design, loop shaping, and time-series exploration Standalone applications
Windows and macOS		 Free but dated. Test the binaries on current systems before assigning them.
Control Tutorials for MATLAB and Simulink Tool training, live scripts, models, and general control examples Short tutorials to full modules
MATLAB and Simulink		 CC BY-SA 4.0. Most examples are mechanical rather than chemical.
MIT Drone Control Course Archive Project-based control course design with small drones Legacy MATLAB, Simulink, and hardware project Historical inspiration only. The hardware is dated and may be difficult to source.

Short video collections include the LearnChemE process control playlist, the Georgia Tech and LearnChemE playlist, the BYU Python for Process Control playlist, and the Control Loop Foundation overview.

Software for modeling, identification, and control

Open and commercial tools for control education
Tool Category Teaching use Runs where and data location Access and limits
python-control Classical analysis Linear and nonlinear systems, frequency response, state space, LQR, estimation, and examples Local Python
Models and data stay local		 BSD-3-Clause. Some advanced functions need the optional Slycot package.
ControlSystems.jl Classical analysis and Julia ecosystem Linear systems, time and frequency response, PID, LQR, identification, robust control, MPC, and documented examples Local Julia
Models and data stay local		 MIT. The ecosystem is broad and active. Learners new to Julia need language setup and orientation.
GNU Octave control package Classical analysis Free MATLAB-like exercises in system analysis, control synthesis, PID, state space, and frequency response Local GNU Octave
Models and data stay local		 GPL-3.0-or-later with BSD-3-Clause components. It uses SLICOT and needs a local Octave installation.
GEKKO Dynamic optimization and MPC Dynamic simulation, parameter estimation, MHE, MPC, regression, and real-time optimization Local or hosted Python solve MIT. Remote mode sends a model to a service. Use GEKKO(remote=False) when work must remain local.
SIPPY System identification SISO and MIMO identification with input-output and subspace methods Local Python
Data stay local		 LGPL-3.0. Requires CasADi and may use Slycot. Use the current Examples folder.
simple-pid PID and hardware Small PID coding exercises and hardware loops Local Python
Data stay local		 MIT. It supplies a controller, not a plant model or course sequence.
do-mpc Dynamic optimization and MPC Nonlinear and robust MPC, state estimation, uncertainty, and constraints Local Python with CasADi
Data stay local		 LGPL-3.0. Solver setup and computation make it better for advanced courses.
CasADi Dynamic optimization and optimal control Algorithmic differentiation, nonlinear optimization, dynamic models, and optimal-control transcription Local C++, Python, MATLAB, or Octave LGPL-3.0. It is a framework, not a turnkey controller. Learners need optimization, differential equation, and programming background.
IDAES control models Process modeling and control Rigorous process modeling, dynamic simulation, PID, and optimization projects Local Python, Pyomo, IDAES, and solvers Open source and powerful. Installation and model complexity are high for an introductory course.
harold Classical analysis Alternative Python systems and control toolbox Local Python MIT. The project is smaller and less active than python-control.
Skogestad-Python Companion examples Examples for multivariable control Local Python Legacy student-contributed code tested with older Python versions. No license is posted.
MathWorks courseware Commercial courseware Curated control courses, assignments, apps, MATLAB, Simulink, and TCLab materials Desktop and some browser services Some training is free. Accounts, institutional access, and paid toolboxes may be needed.
AspenTech Academic Program Industrial commercial tools HYSYS Dynamics, process simulation, advanced process control, and industry workflows Licensed desktop and supported services Institutional license, account, installation, and support access are required.
Control Station Academics Industrial commercial tools Integrated curriculum with textbook, lectures, laboratories, software, and faculty materials Commercial desktop environment Quote or demonstration required. Confirm pricing and student access before adoption.

Industrial cases and process datasets

Choose a case that matches the course goal. Before assigning a download, check its size, format, license, software needs, and whether the process is simulated or physical.

Cases for modeling, control, monitoring, and fault studies
Case or dataset Engineering use Level and scale Access and limits
Tennessee Eastman Challenge Process Archive Plantwide, decentralized, fault-tolerant, and predictive control case studies Advanced
Large process with disturbances and many variables		 Public archive. Much of the MATLAB and Fortran code targets old software releases.
Modern Tennessee Eastman Data Repository PCA, PLS, FDA, CVA, fault detection, monitoring, and data-driven control Intermediate to advanced
Training and test files with 52 variables		 BSD-3-Clause. Learners need process context before interpreting fault labels.
IndPenSim Dataset Batch monitoring, fault detection, data-driven control, and advanced process control Advanced
100 simulated penicillin batches, about 2.5 GB		 CC BY 4.0. Large download and simulated data. A companion notebook is available.
PRONTO Heterogeneous Process Dataset Process monitoring with alarms, ultrasound, pressure, video, and mixed data types Advanced
About 1.7 GB in several formats		 Publicly downloadable. No license is shown, so verify reuse and cite the dataset. Storage and preprocessing add setup time.
Fired Heater MPC Project
Georgia Tech		 Industry-based multivariable estimation and MPC design Advanced
Multi-session team project		 MATLAB interface and large simulation files. Copyrighted. Contact the author before redistributing.
Adhesive Coater MPC Project
MIT		 Self-contained multivariable MPC design assignment Advanced
MATLAB project download		 Legacy files with no visible license. Test in a current MATLAB release.
APMonitor Nonlinear Model Library Reactors, columns, tanks, fermentation, fuel cells, and other instructor-built problems Introductory to advanced
More than 40 model entries of varied size		 Public model collection. Licensing is not stated consistently for each model.
do-mpc CSTR Nonlinear robust MPC under uncertainty and constraints Advanced
Course-sized Python model		 LGPL-3.0. Requires Python, CasADi, a solver, and nonlinear control background.
IDAES Examples Rigorous process models, dynamic simulation, PID, steam systems, reactors, and flowsheet work Advanced
Small examples to full flowsheets		 Open source. Installation and solver needs are substantial.
Process Control Case Histories Discussion of pH, compressor surge, pressure, temperature, interlocks, and startups Intermediate
Reading or incident-analysis assignment		 Free to read. The complete work is not openly licensed, so link to it rather than redistributing it.

Data-Driven Engineering, GenAI, and control

Data-driven methods can estimate models, states, faults, and control policies. GenAI can help plan, explain, code, and review. Neither replaces a physical model, a baseline controller, or an independent safety check.

Data-driven control learning

Resources that connect process data with dynamics and control
Resource Best use What students should check
CACHE Data-Driven Engineering resources Sensor data, time series, hybrid models, system discovery, and engineering datasets Sampling, leakage, physical bounds, extrapolation, drift, and uncertainty
Data-Driven Science and Engineering Commercial Cambridge textbook with free videos, problem sets, code, and datasets on system identification, reduced-order models, dynamics, and control Does the learned structure match the physics and intended operating range? Reuse terms for the free supplements are not stated.
PySINDy Examples Discovering governing equations from dynamic data Noise sensitivity, derivative estimates, term selection, and validation on a new trajectory
SIPPY Examples Classical and subspace system identification Excitation, order selection, residual correlation, and held-out prediction
PC-Gym documentation and repository Reinforcement-learning benchmarks for CSTRs, extraction, crystallization, and other process control problems with NMPC comparisons MIT and identified as pre-release. Pin a tested version. Use simulation first, compare with a model-based baseline, and check constraints, disturbances, sample needs, reward design, and reproducibility.

Teach effective GenAI use

A workflow that keeps engineering reasoning visible
Stage Appropriate AI help Required student evidence
Define Ask for missing variables, assumptions, disturbances, constraints, failure modes, and clarification questions Student-written problem statement and final assumptions
Plan Compare modeling, identification, control, and testing approaches before coding Chosen plan, rejected options, and reasons
Build Draft equations, pseudocode, tests, plotting code, or documentation in small steps Version history and explanation of accepted changes
Critique Search for sign errors, hidden assumptions, instability, saturation, numerical problems, and missing tests Independent hand check, baseline comparison, and corrected result
Stress-test Suggest disturbances, parameter errors, noise, delays, constraints, and sensor or actuator failures Scenario table, plots, and control response discussion
Communicate Improve organization and identify claims that need sources Accurate citations, concise engineering recommendation, and known limits

For local and commercial assistants, coding agents, privacy choices, prompting, and tool safeguards, use the CACHE Programming GenAI guide. For LLM foundations and Agentic Engineering, use the CACHE AI and Machine Learning guide.

Safe defaults for control agents

Use the NIST Generative AI Profile to build a risk register, test plan, source record, and incident exercise.

  • Use a simulator, emulator, or read-only data copy before connecting any agent to equipment or a live process.
  • Do not send proprietary plant data, student records, credentials, unpublished work, or restricted files to an unapproved service.
  • Give tools the smallest needed permissions and limit files, commands, network access, cost, and run time.
  • Log prompts, tool calls, code changes, solver output, and model or service versions.
  • Require a person to approve changes to controllers, alarms, interlocks, grades, equipment, or safety-related documents.
  • Test generated code against normal, boundary, failure, and recovery cases.
  • Keep a known safe controller and a clear stop condition.

Assessment and engineering review

Assess the reasoning and evidence that surround the final plots. A polished response or stable-looking simulation is not enough.

Evidence of individual understanding

Assessment methods that work with or without AI tools
Method What it reveals Example
Prediction before simulation Physical understanding and sign reasoning Sketch the expected response and explain gain, delay, and time scale
Staged submission Planning, revision, and debugging Submit problem definition, baseline, model, controller, tests, and final recommendation in stages
Live modification Ownership of code and model Change a constraint, disturbance, parameter, or sensor and explain the response
Oral check Individual understanding in teamwork Explain one equation, tuning choice, plot, failure case, and design tradeoff
Error diagnosis Critical review skill Find and repair a plausible sign, units, sampling, model, or controller error
Comparison to a baseline Whether complexity adds value Compare advanced control with a simple PID or manual operating policy

Control review checklist

Questions to ask before accepting a model or controller
Review area Questions Evidence
Problem Are the controlled, manipulated, measured, and disturbance variables clear? Are constraints and performance goals stated? Control objective and variable table
Model Do equations, units, signs, initial conditions, steady states, and assumptions match the process? Derivation, dimensional checks, and limiting cases
Data Is the experiment sufficiently exciting? Are sampling, filtering, missing data, drift, and leakage handled? Experiment design, raw plots, and held-out validation
Stability Is the stated stability result valid for the model, operating range, delay, and uncertainty? Analysis plus nonlinear or scenario tests
Performance Are setpoint, disturbance, noise, interaction, and recovery results compared fairly? Common scenarios and stated metrics
Actuators and sensors Are saturation, rate limits, dead band, noise, delay, failure, and calibration considered? Stress tests and fallback behavior
Numerics Do integration steps, solver status, tolerances, initial guesses, and scaling support the claim? Solver record and refinement or sensitivity study
Safety and operation What happens on failure? Which alarms, interlocks, manual actions, and safe states remain independent? Hazard discussion and operating response
Reproducibility Can another person run the model with documented versions, data, seeds, and steps? Fresh run, source record, and clear instructions

Student AI-use record

Ask students to report the tool, product, and model, date and version, local or cloud deployment, files or context shared, important prompts or a concise interaction summary, suggestions accepted, changed, and rejected, independent calculations, tests, sources, and remaining limitations. Students remain responsible for the engineering work they submit.

More collections and professional context

Additional teaching collections and process systems communities
Resource Best use Notes
Awesome Control Theory Directory of books, courses, software, datasets, and communities CC0 directory. Review each linked resource separately.
Resourcium Broad collection of control and automation learning resources Directory rather than a course. Check access and rights for each item.
IFAC Technical Committee on Control Education Control education events, professional context, and community connections The committee's older teaching repository links are not dependable. Use this as professional context, not a ready teaching collection.
IEEE Control Systems Society Technical Committee on Control Education Control education activities and professional connections Professional context, not a ready course.
BYU PRISM Process systems research, modeling, optimization, and student projects Research center site. Project availability changes.
McMaster Advanced Control Consortium Industry and academic context for process automation research Research consortium, not a teaching package.
Center for Advanced Process Decision-making Research context for process synthesis, optimization, control, safety, and reliability Advanced research context.
Texas-Wisconsin-California Control Consortium Research context for modeling, monitoring, diagnosis, and nonlinear MPC Research consortium. Public teaching materials vary.

Suggest a resource or correction

When suggesting a resource, include its audience, prerequisites, topic, expected time, setup needs, software, data size, license, available assignments, and known limits.

Send feedback to Dr. John Hedengren at Brigham Young University or Dr. Luke Landherr at Northeastern University.

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