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DiffTune-MPC: Closed-Loop Learning for Model Predictive Control
Ran Tao
, Sheng Cheng
, Xiaofeng Wang
,
Shenlong Wang
,
Naira Hovakimyan
Siebel School of Computing and Data Science
Electrical and Computer Engineering
Coordinated Science Lab
National Center for Supercomputing Applications (NCSA)
Mechanical Science and Engineering
Aerospace Engineering
Information Trust Institute
Beckman Institute for Advanced Science and Technology
Research output
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Contribution to journal
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Article
›
peer-review
Overview
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Keyphrases
Model Predictive Control
100%
Closed-loop Learning
100%
Cost Function
27%
Parameter Space
9%
Learning Methods
9%
Analytical Gradients
9%
Performance Evaluation
9%
High Dimension
9%
Control Parameters
9%
Closed-loop
9%
Controller
9%
Performance Metrics
9%
Potential Difference
9%
Autonomous Systems
9%
Robotic System
9%
Learning Model
9%
Learnability
9%
Planning Horizon
9%
Closed-loop Performance
9%
Future Behavior
9%
Control Sets
9%
Auxiliary Problem
9%
Generalization Ability
9%
Sequential Quadratic Programming
9%
Metric Function
9%
Nonlinear MPC
9%
Engineering
Closed Loop
100%
Predictive Control Model
100%
Cost Function
27%
Simulation Result
9%
Parameter Space
9%
Open Loop
9%
Metrics
9%
Potential Difference
9%
Control Parameter
9%
Autonomous System
9%
Robotic System
9%
Planning Horizon
9%
Auxiliary Problem
9%
Chemical Engineering
Predictive Control Model
100%
Auxiliaries
9%