Parameterized Tube Model Predictive Control

This paper develops a parameterized tube model predictive control (MPC) synthesis method. The most relevant novel feature of our proposal is the online use of a single tractable linear program that optimizes parameterized, Minkowski decomposable, state and control tubes and an associated, fully sepa...

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Bibliographic Details
Published in:IEEE transactions on automatic control Vol. 57; no. 11; pp. 2746 - 2761
Main Authors: Rakovic, S. V., Kouvaritakis, B., Cannon, M., Panos, C., Findeisen, R.
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.11.2012
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptative systems
Applied sciences
Automatic control
Computer science; control theory; systems
Constrained control
Control theory. Systems
convex programming
Dynamical systems
Electron tubes
Exact sciences and technology
Linearity
Mathematical models
Nonlinearity
Optimal control
Optimization
Policies
Predictive control
Proposals
robust control
Robustness
set-dynamics
System theory
tube model predictive control
Tubes
set-dynamics
Minkowski metric
Non linear control
Systems theory
Affine transformation
Convex programming
Constrained optimization
Robust control
tube model predictive control
Model predictive control
Control constraint
Constrained control
Feasibility
Dynamic programming
ISSN:0018-9286, 1558-2523
Online Access:Get full text
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Description
Summary:This paper develops a parameterized tube model predictive control (MPC) synthesis method. The most relevant novel feature of our proposal is the online use of a single tractable linear program that optimizes parameterized, Minkowski decomposable, state and control tubes and an associated, fully separable, nonlinear, control policy. The induced control policy enjoys a higher degree of nonlinearity than existing tube MPC and robust MPC using disturbance affine control policy. Our proposal offers greater generality than the state of the art robust MPC methods. It is conjectured, and also established in three cases, that our proposal is equivalent, feasibility-wise, to dynamic programming (DP). It is also shown that, under natural assumptions, our method is computationally efficient while it possesses rather strong system theoretic properties.
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ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2012.2191174