A tutorial review of neural network modeling approaches for model predictive control
An overview of the recent developments of time-series neural network modeling is
presented along with its use in model predictive control (MPC). A tutorial on the construction …
presented along with its use in model predictive control (MPC). A tutorial on the construction …
[HTML][HTML] Comparing LSTM and GRU models to predict the condition of a pulp paper press
The accuracy of a predictive system is critical for predictive maintenance and to support the
right decisions at the right times. Statistical models, such as ARIMA and SARIMA, are unable …
right decisions at the right times. Statistical models, such as ARIMA and SARIMA, are unable …
Deep reinforcement learning approaches for process control
SPK Spielberg, RB Gopaluni… - 2017 6th international …, 2017 - ieeexplore.ieee.org
In this work, we have extended the current success of deep learning and reinforcement
learning to process control problems. We have shown that if reward hypothesis functions are …
learning to process control problems. We have shown that if reward hypothesis functions are …
Nonlinear model predictive control based on a self-organizing recurrent neural network
HG Han, L Zhang, Y Hou… - IEEE transactions on …, 2015 - ieeexplore.ieee.org
A nonlinear model predictive control (NMPC) scheme is developed in this paper based on a
self-organizing recurrent radial basis function (SR-RBF) neural network, whose structure …
self-organizing recurrent radial basis function (SR-RBF) neural network, whose structure …
Cyber-physical control for energy-saving vehicle following with connectivity
This article aims to develop an optimal look-ahead control framework to maximize car-
following fuel economy, while fulfilling requirements of intervehicle safety. Three original …
following fuel economy, while fulfilling requirements of intervehicle safety. Three original …
Computationally efficient model predictive control algorithms
M Ławryńczuk - A Neural Network Approach, Studies in Systems …, 2014 - Springer
In the Proportional-Integral-Derivative (PID) controllers the control signal is a linear function
of: the current control error (the proportional part), the past errors (the integral part) and the …
of: the current control error (the proportional part), the past errors (the integral part) and the …
Nonlinear model-predictive control for industrial processes: An application to wastewater treatment process
H Han, J Qiao - IEEE Transactions on Industrial Electronics, 2013 - ieeexplore.ieee.org
Because of their complex behavior, wastewater treatment processes (WWTPs) are very
difficult to control. In this paper, the design and implementation of a nonlinear model …
difficult to control. In this paper, the design and implementation of a nonlinear model …
A critical review of the most popular types of neuro control
M Mohammadzaheri, L Chen… - Asian Journal of …, 2012 - Wiley Online Library
In this review article, the most popular types of neural network control systems are briefly
introduced and their main features are reviewed. Neuro control systems are defined as …
introduced and their main features are reviewed. Neuro control systems are defined as …
[HTML][HTML] Neural network based model predictive control for a quadrotor UAV
A dynamic model that considers both linear and complex nonlinear effects extensively
benefits the model-based controller development. However, predicting a detailed …
benefits the model-based controller development. However, predicting a detailed …
Model predictive control of nonlinear processes using neural ordinary differential equation models
Abstract Neural Ordinary Differential Equation (NODE) is a recently proposed family of deep
learning models that can perform a continuous approximation of a linear/nonlinear dynamic …
learning models that can perform a continuous approximation of a linear/nonlinear dynamic …