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 …
[HTML][HTML] Physics-informed recurrent neural network modeling for predictive control of nonlinear processes
In this work, we present a physics-informed recurrent neural network (PIRNN) modeling
approach, and a PIRNN-based predictive control scheme for a general class of nonlinear …
approach, and a PIRNN-based predictive control scheme for a general class of nonlinear …
[HTML][HTML] LSTM and GRU neural networks as models of dynamical processes used in predictive control: A comparison of models developed for two chemical reactors
K Zarzycki, M Ławryńczuk - Sensors, 2021 - mdpi.com
This work thoroughly compares the efficiency of Long Short-Term Memory Networks
(LSTMs) and Gated Recurrent Unit (GRU) neural networks as models of the dynamical …
(LSTMs) and Gated Recurrent Unit (GRU) neural networks as models of the dynamical …
Statistical machine‐learning–based predictive control of uncertain nonlinear processes
In this study, we present machine‐learning–based predictive control schemes for nonlinear
processes subject to disturbances, and establish closed‐loop system stability properties …
processes subject to disturbances, and establish closed‐loop system stability properties …