Open AccessRobust model predictive control of constrained non‐linear systems: adopting the non‐squared integrand objective function
Author(s) -
Liu Xiaotao,
Constantinescu Daniela,
Shi Yang
Publication year - 2015
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2013.1078
Subject(s) - control theory (sociology) , model predictive control , weighting , mathematics , jacobian matrix and determinant , robust control , mathematical optimization , linear system , lyapunov function , control system , computer science , control (management) , nonlinear system , engineering , medicine , artificial intelligence , electrical engineering , radiology , mathematical analysis , physics , quantum mechanics
This study presents a novel robust model predictive control (MPC) method for constrained non‐linear systems with control constraints and external disturbances. The control signal is obtained by optimising an objective function consisting of two terms: an integral non‐squared stage cost and a non‐squared terminal cost. The terminal weighting matrix is designed appropriately such that: (i) the terminal cost serves as a control Lyapunov function; and (ii) the resultant finite horizon cost can be treated as a quasi‐infinite horizon cost. Provided that the Jacobian linearisation of the system to be controlled is stabilisable and the optimisation is initially feasible, sufficient conditions ensuring the recursive feasibility of the optimisation and the robust stability of the closed‐loop system are established. It is shown that the conditions rely on an appropriate design of the sampling interval with respect to a certain given disturbance level. The effectiveness of the proposed method is illustrated through a numerical example.