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TCLab Instructor Resources
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This is an overview of instructor resources for teaching Process Dynamics & Control. The 2015 NSF-sponsored report “Chemical Engineering Academia-Industry Alignment: Expectations about New Graduates” identified a strong industrial need for practical understanding of process control and system dynamics. Industry feedback also suggests more focus is needed on translating process control theory into practice. At many universities, this need is met by integrating laboratory experiences into process control courses. However, increased enrollments in chemical engineering strain the available laboratory resources and make these labs difficult to schedule and manage. For these reasons, we have developed small, inexpensive, and portable process control lab designed to reinforce process dynamics and control theory.
Thanks to Jeffrey Kantor, Abe Martin, Carl Sandrock, Nigel Reuel, Brian Douglas, and 40 other instructors for adopting and developing this community resource.
Thanks to Jeffrey Kantor, Abe Martin, Carl Sandrock, Nigel Reuel, Brian Douglas, and 40 other instructors for adopting and developing this community resource.
TCLab Instructor Resources
Teaching Dynamics and Control with Arduino-based TCLab
Teaching Dynamics and Control with Arduino Based TCLab
Teaching Dynamics and Control with Arduino-based TCLab
Model Predictive Control in Simulink with Arduino TCLab
TCLab: Control Stability and Oscillation
Teaching Chemical Engineering with MATLAB, Simulink, and TCLab
Pocket-Sized Lab for Dynamics and Control
Arduino TCLab for Engineering Education
TCLab PID Control
Control theory and applications laboratory: video 5b TCLab identification
SimTune ModelID Walkthrough (with TCLab)
TCLab PID Control Tuning in Simulink
TCLab: Simulate ODE Step Test
TCLab EN 7 ( In English )
TCLab D: System Identification
Matlab 👩💻 Course with Arduino TCLab
TCLab 1 EN ( In English )
TCLab PID Control Tuning
TCLab Second Order Regression
TCLab: 2nd-Order Real-Time Regression
PID Tuning for TcLab from Scratch 1/3
TCLab B: MIMO Deep Learning
TCLab Moving Horizon Estimation
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