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Neural network-based offset-free model predictive control for nonlinear systems
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DOI：10.1002/aic.70141.png)
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Abstract
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This paper proposes an offset-free model predictive control (MPC) framework for nonlinear systems modeled using neural network-based nonlinear autoregressive models with exogenous inputs (NARX). To address plant-model mismatch and ensure offset-free tracking, the NARX model is augmented with an integrating disturbance model, resulting in an extended state-space suitable for MPC. A nonlinear observer is developed to estimate both system and disturbance states in real time. The impact of training data quality on control performance is examined through two modeling scenarios: one with rich excitation data and another with limited excitation data, reflecting practical constraints. For both cases, offset-free MPC controllers are designed using the proposed framework. The approach is validated through simulations on a nonlinear chemical reactor and compared with a benchmark NARX-based offset-free MPC method employing bias correction from output prediction errors. Results show that the proposed method improves tracking performance, particularly when training data are limited.
Keywords:
model predictive control (mpc)
neural networks
nonlinear systems
offset-free control
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