Lecture Notes 32
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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.
In this lecture we review the lab assignments and cover some information on converting a linearized model to state space form.
dx/dt = A x + B u y = C x + D u
Homework
- Course reading for next class: 12.1-12.3 (PDC)
- Assignment due by the start of Lecture #32: SP13
Relate each problem in the context of the overall course objectives.
Additional Material
- Tutorial on Dynamic 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:
In this lecture we review the lab assignments and cover some information on converting a linearized model to state space form.
<iframe width="560" height="315" src="https://www.youtube.com/embed/IGMGsSYLvMQ" frameborder="0" allowfullscreen></iframe>
<iframe width="560" height="315" src="//www.youtube.com/embed/lGMGsSYLvMQ" frameborder="0" allowfullscreen></iframe>
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:
<iframe width="560" height="315" src="https://www.youtube.com/embed/ADPsPBxfwXE" frameborder="0" allowfullscreen></iframe>
<iframe width="560" height="315" src="https://www.youtube.com/embed/IGMGsSYLvMQ" frameborder="0" allowfullscreen></iframe>
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Relate each problem in the context of the overall course objectives.
Relate each problem in the context of the 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.
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="https://www.youtube.com/embed/ADPsPBxfwXE" frameborder="0" allowfullscreen></iframe> (:endhtml:)
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.
Homework
- Course reading for next class: 12.1-12.3 (PDC)
- Assignment due by the start of Lecture #32: SP13
Relate each problem in the context of the overall course objectives.