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Found 18 papers, 1 papers with code
A Learning-based Model Predictive Control Scheme with Application to Temperature Control Units
Temperature control is a complex task due to its often unknown dynamics and disturbances.
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.
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.
Physics-informed Neural Network Modelling and Predictive Control of District Heating Systems
This paper addresses the data-based modelling and optimal control of District Heating Systems (DHSs).
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.
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.
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.
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.
Robust Tube-based Model Predictive Control with Koopman Operators--Extended Version
The finite data-driven approximation of Koopman operators results in a class of linear predictors, useful for formulating linear model predictive control (MPC) of nonlinear dynamical systems with reduced computational complexity.
Recurrent Neural Network-based Internal Model Control design for stable nonlinear systems
Owing to their superior modeling capabilities, gated Recurrent Neural Networks, such as Gated Recurrent Units (GRUs) and Long Short-Term Memory networks (LSTMs), have become popular tools for learning dynamical systems.
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.
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.
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.
Hierarchical Control in Islanded DC Microgrids with Flexible Structures
Hierarchical architectures stacking primary, secondary, and tertiary layers are widely employed for the operation and control of islanded DC microgrids (DCmGs), composed of Distribution Generation Units (DGUs), loads, and power lines.
