## Main.LectureNotes32 History

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!!!! ~~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]].

# Course reading for next class

# 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]].

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!!!! Additional Material

* [[http://ctms.engin.umich.edu/CTMS/index.php?example=Introduction§ion=SystemModeling | Tutorial on Dynamic Modeling]]

* [[http://ctms.engin.umich.edu/CTMS/index.php?example=Introduction§ion=SystemModeling | Tutorial on Dynamic Modeling]]

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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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<iframe width="560" height="315" src="~~http:~~//www.youtube.com/embed/~~IGMGsSYLvMQ~~" frameborder="0" allowfullscreen></iframe>

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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:

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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)]]

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* %list list-page% [[Attach:cstr_example.zip | State Space Exercise Files (MATLAB)]]

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* %list list-page% [[Attach:Lecture31_handout2.pdf | State Space Exercise]]

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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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!!! 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]]~~

Model Predictive Control (MPC) uses a mathematical representation of the process to predict and manipulate the future response of a system

* %list list-page% [[Attach

* %list list-page% [[Attach

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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:

(:html:)

<iframe width="560" height="315" src="http://www.youtube.com/embed/ADPsPBxfwXE" frameborder="0" allowfullscreen></iframe>

(:endhtml:)

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:

(:html:)

<iframe width="560" height="315" src="http://www.youtube.com/embed/ADPsPBxfwXE" frameborder="0" allowfullscreen></iframe>

(:endhtml:)

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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]].

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]].