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Sparse Identification of Nonlinear Dynamics for Model Predictive Control 

Steve Brunton
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This lecture shows how to use sparse identification of nonlinear dynamics with control (SINDYc) with model predictive control to control nonlinear systems purely from data.
Sparse identification of nonlinear dynamics for model predictive control in the low-data limit.
E. Kaiser, J. N. Kutz, and S. L. Brunton, arxiv 2017.
arxiv.org/abs/1711.05501
Code: github.com/eurika-kaiser/SIND...
www.eigensteve.com/
This video was produced at the University of Washington

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15 июл 2024

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Комментарии : 6   
@oncedidactic
@oncedidactic 2 года назад
SINDY is going to be standing here waiting while we hit the next bitter lesson plateau. super cool stuff!
@yinggling
@yinggling 3 года назад
Hi Steve, great lecture again. I really enjoyed it! I have a small question if you don't mind answering. Does the SINDYC method assume the knowledge of the form of the measurement relations (e.g. differential equations)?
@ericwu142
@ericwu142 3 года назад
Hi professor Steve, awesome lectures. I have a general question. If the system I am trying to identify is a black-box, I don’t really know what gives a full state vector, or I might not be able to measure some of them, do I just guess it out or do I try sINDy with a reduced order state vector or does it mean I can’t use sINDy?
@peter0702
@peter0702 2 года назад
I will say for a system that has some independent states are unobservable, you really cannot control it properly right?
@debu478
@debu478 4 года назад
Sir kindly show us a matlab example using model predictive control
@SergioPertuz
@SergioPertuz 3 года назад
I support this request
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