5 research outputs found
Tustin neural networks: a class of recurrent nets for adaptive MPC of mechanical systems
The use of recurrent neural networks to represent the dynamics of unstable
systems is difficult due to the need to properly initialize their internal
states, which in most of the cases do not have any physical meaning, consequent
to the non-smoothness of the optimization problem. For this reason, in this
paper focus is placed on mechanical systems characterized by a number of
degrees of freedom, each one represented by two states, namely position and
velocity. For these systems, a new recurrent neural network is proposed:
Tustin-Net. Inspired by second-order dynamics, the network hidden states can be
straightforwardly estimated, as their differential relationships with the
measured states are hardcoded in the forward pass. The proposed structure is
used to model a double inverted pendulum and for model-based Reinforcement
Learning, where an adaptive Model Predictive Controller scheme using the
Unscented Kalman Filter is proposed to deal with parameter changes in the
system.Comment: Under revie
Stability of discrete-time feed-forward neural networks in NARX configuration
The idea of using Feed-Forward Neural Networks (FFNNs) as regression
functions for Nonlinear AutoRegressive eXogenous (NARX) models, leading to
models herein named Neural NARXs (NNARXs), has been quite popular in the early
days of machine learning applied to nonlinear system identification, owing to
their simple structure and ease of application to control design. Nonetheless,
few theoretical results are available concerning the stability properties of
these models. In this paper we address this problem, providing a sufficient
condition under which NNARX models are guaranteed to enjoy the Input-to-State
Stability (ISS) and the Incremental Input-to-State Stability ({\delta}ISS)
properties. This condition, which is an inequality on the weights of the
underlying FFNN, can be enforced during the training procedure to ensure the
stability of the model. The proposed model, along with this stability
condition, are tested on the pH neutralization process benchmark, showing
satisfactory results.Comment: This work has been submitted to IFAC for possible publicatio
Advanced control based on Recurrent Neural Networks learned using Virtual Reference Feedback Tuning and application to an Electronic Throttle Body (with supplementary material)
In this paper the application of Virtual Reference Feedback Tuning (VRFT) for
control of nonlinear systems with regulators defined by Echo State Networks
(ESN) and Long Short Term Memory (LSTM) networks is investigated. The
capability of this class of regulators of constraining the control variable is
pointed out and an advanced control scheme that allows to achieve zero
steady-state error is presented. The developed algorithms are validated on a
benchmark example that consists of an electronic throttle body (ETB)
Nonlinear MPC for Offset-Free Tracking of systems learned by GRU Neural Networks
The use of Recurrent Neural Networks (RNNs) for system identification has
recently gathered increasing attention, thanks to their black-box modeling
capabilities.Albeit RNNs have been fruitfully adopted in many applications,
only few works are devoted to provide rigorous theoretical foundations that
justify their use for control purposes. The aim of this paper is to describe
how stable Gated Recurrent Units (GRUs), a particular RNN architecture, can be
trained and employed in a Nonlinear MPC framework to perform offset-free
tracking of constant references with guaranteed closed-loop stability. The
proposed approach is tested on a pH neutralization process benchmark, showing
remarkable performances.Comment: This work is the extended version of the article accepted at the
Third IFAC Conference on Modelling, Identification and Control of Nonlinear
Systems (MICNON 2021) for publication under a Creative Commons Licence
CC-BY-NC-N