Question 4: Analysis Inspect your state figures, discuss any performance issues. How could you ensure that the constraints are met? Model Predictive Control We now refine our infinite horizon cost function into a finite horizon cost function using the state and control action costs from above as inspiration, and directly include the constraints. In a finite horizon setting we achieve infinite horizon operation by using the receeding horizon principle. This leads to Model Predictive Control. Question 5: Design Analysis Explore how to improve the performance of the control design now using constrained Model Predictive Control. Consider different stage costs, constraints, and time horizon length. You will need to choose the cost function weights, prediction horizon, and constraint vectors. Comment on any computational impacts of your choices.
Question 4: Analysis Inspect your state figures, discuss any performance issues. How could you ensure that the constraints are met? Model Predictive Control We now refine our infinite horizon cost function into a finite horizon cost function using the state and control action costs from above as inspiration, and directly include the constraints. In a finite horizon setting we achieve infinite horizon operation by using the receeding horizon principle. This leads to Model Predictive Control. Question 5: Design Analysis Explore how to improve the performance of the control design now using constrained Model Predictive Control. Consider different stage costs, constraints, and time horizon length. You will need to choose the cost function weights, prediction horizon, and constraint vectors. Comment on any computational impacts of your choices.
Advanced Engineering Mathematics
10th Edition
ISBN:9780470458365
Author:Erwin Kreyszig
Publisher:Erwin Kreyszig
Chapter2: Second-order Linear Odes
Section: Chapter Questions
Problem 1RQ
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Transcribed Image Text:Question 4: Analysis
Inspect your state figures, discuss any performance issues.
How could you ensure that the constraints are met?
<Answer here>
Model Predictive Control
We now refine our infinite horizon cost function into a finite horizon cost function using the state and control
action costs from above as inspiration, and directly include the constraints. In a finite horizon setting we achieve
infinite horizon operation by using the receeding horizon principle. This leads to Model Predictive Control.
Question 5: Design Analysis
Explore how to improve the performance of the control design now using constrained Model Predictive Control.
Consider different stage costs, constraints, and time horizon length. You will need to choose the cost function
weights, prediction horizon, and constraint vectors.
Comment on any computational impacts of your choices.
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