Sparsity-Exploiting Anytime Algorithms for Model Predictive Control: A Relaxed Barrier Approach

We present and analyze a novel class of stabilizing and numerically efficient model predictive control (MPC) algorithms for discrete-time linear systems subject to polytopic input and state constraints. The proposed approach combines the previously presented concept of relaxed barrier function-based...

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Published inIEEE transactions on control systems technology Vol. 28; no. 2; pp. 425 - 435
Main Authors Feller, Christian, Ebenbauer, Christian
Format Journal Article
LanguageEnglish
Published New York IEEE 01.03.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Anytime algorithms
barrier functions
Closed loop systems
Cost function
Discrete time systems
Feedback control
Linear systems
Mathematical models
model predictive control
Numerical prediction
Numerical stability
Optimal control
Prediction algorithms
Predictive control
real-time optimization
Sparsity
Stability analysis
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Abstract We present and analyze a novel class of stabilizing and numerically efficient model predictive control (MPC) algorithms for discrete-time linear systems subject to polytopic input and state constraints. The proposed approach combines the previously presented concept of relaxed barrier function-based MPC with suitable warm-starting and sparsity-exploiting factorization techniques and allows to rigorously prove important stability and constraint satisfaction properties of the resulting closed-loop system independently of the number of performed Newton iterations. The effectiveness of the proposed approach is demonstrated by means of a numerical benchmark example.
AbstractList We present and analyze a novel class of stabilizing and numerically efficient model predictive control (MPC) algorithms for discrete-time linear systems subject to polytopic input and state constraints. The proposed approach combines the previously presented concept of relaxed barrier function-based MPC with suitable warm-starting and sparsity-exploiting factorization techniques and allows to rigorously prove important stability and constraint satisfaction properties of the resulting closed-loop system independently of the number of performed Newton iterations. The effectiveness of the proposed approach is demonstrated by means of a numerical benchmark example.
Author Feller, Christian
Ebenbauer, Christian
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Snippet We present and analyze a novel class of stabilizing and numerically efficient model predictive control (MPC) algorithms for discrete-time linear systems...
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SubjectTerms Algorithms
Anytime algorithms
barrier functions
Closed loop systems
Cost function
Discrete time systems
Feedback control
Linear systems
Mathematical models
model predictive control
Numerical prediction
Numerical stability
Optimal control
Prediction algorithms
Predictive control
real-time optimization
Sparsity
Stability analysis
Title Sparsity-Exploiting Anytime Algorithms for Model Predictive Control: A Relaxed Barrier Approach
URI https://ieeexplore.ieee.org/document/8571172
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Volume 28
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