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November 25, 2013, at 06:34 PM by 69.169.136.210 -
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In this lecture we review the lab assignments and cover some information on converting a linearized model to state space form. The model forms covered in this class include continuous and discrete state space and the Laplace domain. A brief tutorial on converting between these model forms in given in the video below:
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In this lecture we review the lab assignments and cover some information on converting a linearized model to state space form.
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!! MATLAB State Space and Transfer Function Models

The model forms covered in this class include continuous and discrete state space and the Laplace domain. A brief tutorial on converting between these model forms in given in the video below:
November 25, 2013, at 05:38 PM by 128.187.97.23 -
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November 25, 2013, at 05:34 PM by 128.187.97.23 -
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* %list list-page% [[Attach:cstr_example.zip | State Space Exercise Files (MATLAB)]]
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* %list list-page% [[Attach:cstr_example_files.zip | State Space Exercise Files (MATLAB)]]
November 25, 2013, at 05:32 PM by 128.187.97.23 -
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* %list list-page% [[Attach:cstr_example.zip | State Space Exercise Files (MATLAB)]]

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November 13, 2013, at 11:06 AM by 69.169.137.17 -
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* %list list-page% [[Attach:Lecture31_handout2.pdf | State Space Exercise]]
September 06, 2013, at 08:52 AM by 69.169.131.210 -
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Relate each problem in the context of the [[Main/CourseCompetencies | overall course objectives]].
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Relate each problem in the context of the [[Main/CourseCompetencies | overall course objectives]].

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November 12, 2012, at 09:55 AM by 69.169.188.188 -
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November 09, 2012, at 05:05 PM by 128.187.97.21 -
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!!! Lecture 32 - Model Predictive Control

Model Predictive Control (MPC) uses a mathematical representation of the process to predict and manipulate the future response of a system
. Instead of a feedback strategy like PID control, MPC is actively making compensating moves to stay within constraints, drive to an economic optimum, and maximize or minimize certain quantities. Lecture 32 is an introduction to MPC and multivariable control.

* %list list-page% [[Attach
:Lecture32_notes.pdf | Lecture 32 Notes]]

* %list list-page% [[Attach
:Lecture32_handout.pdf | Lecture 32 Worksheet]]
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!!! Lecture 32 - State Space Modeling

In this lecture we review the lab assignments and cover some information on converting a linearized model to state space form
. The model forms covered in this class include continuous and discrete state space and the Laplace domain. A brief tutorial on converting between these model forms in given in the video below:

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November 11, 2011, at 09:06 PM by 69.169.187.114 -
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!!! Lecture 32 - Model Predictive Control

Model Predictive Control (MPC) uses a mathematical representation of the process to predict and manipulate the future response of a system. Instead of a feedback strategy like PID control, MPC is actively making compensating moves to stay within constraints, drive to an economic optimum, and maximize or minimize certain quantities. Lecture 32 is an introduction to MPC and multivariable control.

* %list list-page% [[Attach:Lecture32_notes.pdf | Lecture 32 Notes]]

* %list list-page% [[Attach:Lecture32_handout.pdf | Lecture 32 Worksheet]]

!!!! Homework

# Course reading for next class: 12.1-12.3 (PDC)
# Assignment due by the start of Lecture #32: [[Attach:sp13.pdf|SP13]]

Relate each problem in the context of the [[Main/CourseCompetencies | overall course objectives]].