Restricted structure polynomial systems approach to LPV generalized predictive control

Grimble, M. and Alotaibi, S. and Majecki, P. (2021) Restricted structure polynomial systems approach to LPV generalized predictive control. In: 7th IFAC Conference on Nonlinear Model Predictive Control, 2021-07-11 - 2021-07-14.

[thumbnail of Grimble-etal-NMPC-2021-Restricted-structure-polynomial-systems-approach-to-LPV]
Preview
 Text. Filename: Grimble_etal_NMPC_2021_Restricted_structure_polynomial_systems_approach_to_LPV.pdf
Accepted Author Manuscript
Download (1MB)| Preview

Abstract

A new polynomial systems method is defined for the restricted structure nonlinear predictive controllers design. The control algorithm is established for a discrete-time nonlinear system, described in the polynomial matrix linear parameter varying form. The low-order restricted structure controller is parameterized in terms of discrete transfer operators set that the designer chooses multiplied by a set of optimized gains. The controller within the feedback loop can have a general linear parameter varying form or something as simple as a PI structure. The predictive control multi-step cost-index, which is minimized, comprises weighted error, control signal costing, and gain magnitude terms. A simulation of an automotive example is used to evaluate the nonlinear restricted structure controller performance.

ORCID iDs

Grimble, M., Alotaibi, S. ORCID logoORCID: https://orcid.org/0000-0003-1996-7978 and Majecki, P.;
CORE (COnnecting REpositories)