Robust adaptive model predictive control: performance and parameter estimation

Journal article

Abstract:: For systems with uncertain linear models, bounded additive disturbances and state and control constraints, a robust model predictive control (MPC) algorithm incorporating online model adaptation is proposed. Sets of model parameters are identified online and employed in a robust tube MPC strategy with a nominal cost. The algorithm is shown to be recursively feasible and input‐to‐state stable. Computational tractability is ensured by using polytopic sets of fixed complexity to bound parameter sets and predicted states. Convex conditions for persistence of excitation are derived and are related to probabilistic rates of convergence and asymptotic bounds on parameter set estimates. We discuss how to balance conflicting requirements on control signals for achieving good tracking performance and parameter set estimate accuracy. Conditions for convergence of the estimated parameter set are discussed for the case of fixed complexity parameter set estimates, inexact disturbance bounds, and noisy measurements.

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Publisher:: Wiley
Journal:: International Journal of Robust and Nonlinear Control More from this journal
Volume:: 31
Issue:: 18
Pages:: 8703-8724
Publication date:: 2020-08-20
Acceptance date:: 2020-07-14
DOI:: https://doi.org/10.1002/rnc.5175
EISSN:: 1099-1239
ISSN:: 1049-8923

Language:: English
Keywords:: uncertain linear systems

parameter set estimation

adaptive control

input‐to‐state stability

receding horizon control

FFR
Pubs id:: 1071417
Local pid:: pubs:1071417
Deposit date:: 2020-07-17

Copyright holder:: Lu et al.
Rights statement:: © 2020 The Authors. International Journal of Robust and Nonlinear Control published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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