contributor author | Chen, Kaian | |
contributor author | Zhang, Kaixiang | |
contributor author | Li, Zhaojian | |
contributor author | Wang, Yan | |
contributor author | Wu, Kai | |
contributor author | Kalabić, Uroš V. | |
date accessioned | 2022-05-08T09:04:52Z | |
date available | 2022-05-08T09:04:52Z | |
date copyright | 3/18/2022 12:00:00 AM | |
date issued | 2022 | |
identifier issn | 0022-0434 | |
identifier other | ds_144_06_061005.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4284706 | |
description abstract | This paper presents an efficient stochastic model predictive control (SMPC) framework for quasi-linear parameter varying (qLPV) systems. The framework applies to general nonlinear systems that are driven by stochastic additive disturbances and subject to chance constraints. The qLPV form is featured by a composition of a set of linear time-invariant (LTI) models with state-/control-dependent scheduling variables, which can be obtained by the spatial–temporal filtering-based system identification approach developed in our earlier work. The overall framework can then be transformed into a tube-based MPC optimization problem which can be efficiently handled by a series of quadratic programing (QP) problems. A case study on automotive engine control is presented as a pilot demonstration of the proposed qLPV–SMPC where we show its advantage over the zone-based MPC, much greater computational efficiency than nonlinear MPC (NMPC) and less conservativeness of the proposed method as compared to its robust MPC (RMPC) counterpart. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | Stochastic Model Predictive Control for Quasi-Linear Parameter Varying Systems: Case Study on Automotive Engine Control | |
type | Journal Paper | |
journal volume | 144 | |
journal issue | 6 | |
journal title | Journal of Dynamic Systems, Measurement, and Control | |
identifier doi | 10.1115/1.4053887 | |
journal fristpage | 61005-1 | |
journal lastpage | 61005-9 | |
page | 9 | |
tree | Journal of Dynamic Systems, Measurement, and Control:;2022:;volume( 144 ):;issue: 006 | |
contenttype | Fulltext | |