Internal Model Control design for systems learned by Control Affine Neural Nonlinear Autoregressive Exogenous Models
<|reference_start|>Internal Model Control design for systems learned by Control Affine Neural Nonlinear Autoregressive Exogenous Models: This paper explores the use of Control Affine Neural Nonlinear AutoRegressive eXogenous (CA-NNARX) models for nonlinear system identification and model-based control design. The idea behind this architecture is to match the known control-affine structure of the system to achieve improved performance. Coherently with recent literature of neural networks for data-driven control, we first analyze the stability properties of CA-NNARX models, devising sufficient conditions for their incremental Input-to-State Stability ($\delta$ISS) that can be enforced at the model training stage. The model's stability property is then leveraged to design a stable Internal Model Control (IMC) architecture. The proposed control scheme is tested on a simulated Quadruple Tank benchmark system to address the output reference tracking problem. The results achieved show that (i) the modeling accuracy of CA-NNARX is superior to the one of a standard NNARX model for given weight size and training epochs, and (ii) the proposed IMC law provides performance comparable to the ones of a standard Model Predictive Controller (MPC) at a significantly lower computational burden.<|reference_end|>
@article{xie2024internal,
    title={Internal Model Control design for systems learned by Control Affine
  Neural Nonlinear Autoregressive Exogenous Models},
    author={Jing Xie, Fabio Bonassi, Riccardo Scattolini},
    journal={arXiv preprint arXiv:2402.05607},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.05607},
    primaryClass={eess.SY cs.SY}
}