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Found 14 papers, 0 papers with code
Moving horizon partition-based state estimation of large-scale systems -- Revised version
This report presents three Moving Horizon Estimation (MHE) methods for discrete-time partitioned linear systems, i. e. systems decomposed into coupled subsystems with non-overlapping states.
Nonlinear MPC design for incrementally ISS systems with application to GRU networks
This brief addresses the design of a Nonlinear Model Predictive Control (NMPC) strategy for exponentially incremental Input-to-State Stable (ISS) systems.
A Hierarchical Architecture for Optimal Unit Commitment and Control of an Ensemble of Steam Generators
A hierarchical architecture for the optimal management of an ensemble of steam generators is presented.
Deep Long-Short Term Memory networks: Stability properties and Experimental validation
The aim of this work is to investigate the use of Incrementally Input-to-State Stable ($\delta$ISS) deep Long Short Term Memory networks (LSTMs) for the identification of nonlinear dynamical systems.
An incremental input-to-state stability condition for a generic class of recurrent neural networks
This paper proposes a novel sufficient condition for the incremental input-to-state stability of a generic class of recurrent neural networks (RNNs).
Robust offset-free nonlinear model predictive control for systems learned by neural nonlinear autoregressive exogenous models
This paper presents a robust Model Predictive Control (MPC) scheme that provides offset-free setpoint tracking for systems described by Neural Nonlinear AutoRegressive eXogenous (NNARX) models.
Towards lifelong learning of Recurrent Neural Networks for control design
This paper proposes a method for lifelong learning of Recurrent Neural Networks, such as NNARX, ESN, LSTM, and GRU, to be used as plant models in control system synthesis.
An Offset-Free Nonlinear MPC scheme for systems learned by Neural NARX models
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.
Virtual Reference Feedback Tuning for linear discrete-time systems with robust stability guarantees based on Set Membership
To show the generality and effectiveness of our approach, we apply it to two of the most widely used yet simple control schemes, i. e., where tracking is achieved thanks to (i) a static feedforward action and (ii) an integrator in closed-loop.
On Recurrent Neural Networks for learning-based control: recent results and ideas for future developments
This paper aims to discuss and analyze the potentialities of Recurrent Neural Networks (RNN) in control design applications.
Advanced control based on Recurrent Neural Networks learned using Virtual Reference Feedback Tuning and application to an Electronic Throttle Body (with supplementary material)
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.
Robust multi-rate predictive control using multi-step prediction models learned from data
This note extends a recently proposed algorithm for model identification and robust MPC of asymptotically stable, linear time-invariant systems subject to process and measurement disturbances.
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.
On the stability properties of Gated Recurrent Units neural networks
The goal of this paper is to provide sufficient conditions for guaranteeing the Input-to-State Stability (ISS) and the Incremental Input-to-State Stability ({\delta}ISS) of Gated Recurrent Units (GRUs) neural networks.
