Robust offset-free nonlinear model predictive control for systems learned by neural nonlinear autoregressive exogenous models

被引:3
作者
Xie, Jing [1 ,2 ]
Bonassi, Fabio [1 ]
Farina, Marcello [1 ]
Scattolini, Riccardo [1 ]
机构
[1] Politecn Milan, Dipartimento Elettron Informaz & Bioingn, Milan, Italy
[2] Politecn Milan, Dipartimento Elettron Informaz & Bioingn, Via Ponzio 34-5, I-20133 Milan, Italy
关键词
learning-based control; neural networks; nonlinear model predictive control; offset-free tracking; robust control; IDENTIFICATION; NETWORKS; FEEDBACK; DESIGN;
D O I
10.1002/rnc.6883
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper presents a robust model predictive control (MPC) scheme that provides offset-free setpoint tracking for systems described by neural nonlinear autoregressive exogenous (NNARX) models. To this end, a NNARX model that learns the dynamics of the plant from input-output data is augmented with an explicit integral action on the output tracking error. A robust tube-based MPC is finally designed, leveraging the unique structure of the model, to ensure robust offset-free convergence to constant reference signals even in case of plant-model mismatch. Numerical simulations on a water heating system show the effectiveness of the proposed control algorithm.
引用
收藏
页码:9992 / 10009
页数:18
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