A feedback min-max MPC algorithm for LPV systems subject to bounded rates of change of parameters

A novel closed-loop model-based predictive control (MPC) strategy for input-saturated polytopic linear parameter varying (LPV) discrete-time systems is proposed. It is postulated that the plant belongs to a polytopic family of linear systems, each member of which being parameterized by the value tha...

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Bibliographic Details
Published in:IEEE transactions on automatic control Vol. 47; no. 7; pp. 1147 - 1153
Main Authors: Casavola, A., Famularo, D., Franze, G.
Format: Journal Article
Language:English
Published: New York IEEE 01.07.2002
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Automatic control
Control systems
Control theory
Feedback
Horizon
Job shop scheduling
Linear systems
Mathematical models
On-line systems
Predictive control
Predictive models
Robust control
Robustness
Stability
Strategy
Studies
Vectors
ISSN:0018-9286, 1558-2523
Online Access:Get full text
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Summary:A novel closed-loop model-based predictive control (MPC) strategy for input-saturated polytopic linear parameter varying (LPV) discrete-time systems is proposed. It is postulated that the plant belongs to a polytopic family of linear systems, each member of which being parameterized by the value that a parameter vector assumes in the unit simplex. Such a parameter can be measured online and used for feedback while a bound on its rate of change is known and exploited for predictions. The paper extends the MPC scheme presented by Lu et al. (2000) for the restricting case of 1-step long control horizons to the general case of control horizons of arbitrary length N. This is done by suitably modifying the robust MPC scheme presented by Casavola et al. (2000) for uncertain polytopic systems. The feasibility and closed-loop stability of this strategy are proved and a numerical example is also presented in order to show how the freedom of extending the control horizon and knowledge of the parameter is significant in order to improve the performance of the control strategy.
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ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2002.800662