On Recurrent Neural Networks for learning-based control: recent results and ideas for future developments
@article{Bonassi2022OnRN, title={On Recurrent Neural Networks for learning-based control: recent results and ideas for future developments}, author={Fabio Bonassi and Marcello Farina and Jing Xie and Riccardo Scattolini}, journal={ArXiv}, year={2022}, volume={abs/2111.13557} }
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An Offset-Free Nonlinear MPC scheme for systems learned by Neural NARX models
- EngineeringArXiv
- 2022
—This paper deals with the design of nonlinear MPC controllers that provide offset-free setpoint tracking for models described by Neural Nonlinear AutoRegressive eXoge- nous (NNARX) networks. The…
References
SHOWING 1-10 OF 115 REFERENCES
Recurrent Neural Network-based Internal Model Control design for stable nonlinear systems
- Computer ScienceEur. J. Control
- 2022
Stability of discrete-time feed-forward neural networks in NARX configuration
- Computer ScienceIFAC-PapersOnLine
- 2021
Tustin neural networks: a class of recurrent nets for adaptive MPC of mechanical systems
- Computer ScienceIFAC-PapersOnLine
- 2020
Nonlinear MPC for Offset-Free Tracking of systems learned by GRU Neural Networks
- Computer ScienceIFAC-PapersOnLine
- 2021
On the stability properties of Gated Recurrent Units neural networks
- Computer ScienceSyst. Control. Lett.
- 2021
Recurrent Neural Network based MPC for Process Industries
- Engineering2019 18th European Control Conference (ECC)
- 2019
This article combines data-driven modeling with MPC and investigates how to train, validate, and incorporate a special recurrent neural network (RNN) architecture into an MPC framework, designed for being scalable and applicable to a wide range of multiple-input multiple-output systems encountered in industrial applications.
Nonlinear internal model control using neural networks: application to processes with delay and design issues
- MathematicsIEEE Trans. Neural Networks Learn. Syst.
- 2000
It is shown that the design of such nonadaptive indirect control systems necessitates only the training of the inverse of the model deprived from its delay, and that the presence of the delay thus does not increase the order ofThe inverse.
Memory neuron networks for identification and control of dynamical systems
- Computer ScienceIEEE Trans. Neural Networks
- 1994
It is argued that for satisfactory modeling of dynamical systems, neural networks should be endowed with such internal memory as to identify systems whose order is unknown or systems with unknown delay.
Learning model predictive control with long short‐term memory networks
- MathematicsInternational Journal of Robust and Nonlinear Control
- 2021
This article analyzes the stability‐related properties of long short‐term memory (LSTM) networks and investigates their use as the model of the plant in the design of model predictive controllers…
On Generalization Bounds of a Family of Recurrent Neural Networks
- Computer ScienceAISTATS
- 2020
This work studies the generalization properties of vanilla RNNs as well as their variants, including Minimal Gated Unit (MGU), Long Short Term Memory (LSTM), and Convolutional (Conv) Rnns, and establishes refined generalization bounds with additional norm assumptions.