An Offset-Free Nonlinear MPC scheme for systems learned by Neural NARX models

• URL: http://arxiv.org/abs/2203.16290v1
• Date: Wed, 30 Mar 2022 13:30:07 GMT
• Title: An Offset-Free Nonlinear MPC scheme for systems learned by Neural NARX models
• Authors: Fabio Bonassi, Jing Xie, Marcello Farina, Riccardo Scattolini
• Abstract summary: This paper deals with the design of nonlinear MPC controllers that provide offset-free setpoint tracking. The proposed scheme attains remarkable performances even in presence of disturbances acting on the plant.
• Score: 0.803314610321292
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper deals with the design of nonlinear MPC controllers that provide offset-free setpoint tracking for models described by Neural Nonlinear AutoRegressive eXogenous (NNARX) networks. The NNARX model is identified from input-output data collected from the plant, and can be given a state-space representation with known measurable states made by past input and output variables, so that a state observer is not required. In the training phase, the Incremental Input-to-State Stability ({\delta}ISS) property can be forced when consistent with the behavior of the plant. The {\delta}ISS property is then leveraged to augment the model with an explicit integral action on the output tracking error, which allows to achieve offset-free tracking capabilities to the designed control scheme. The proposed control architecture is numerically tested on a water heating system and the achieved results are compared to those scored by another popular offset-free MPC method, showing that the proposed scheme attains remarkable performances even in presence of disturbances acting on the plant.

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