Reference Governors

In this paper, we show the implementation of deep neural networks applied in process control. In our approach, we based the training of the neural network on model predictive control. Model predictive control is popular for its ability to be tuned by the weighting matrices and by the fact that it respects the constraints.We present the neural network that can approximate the behavior of the MPC in the way of mimicking the control input trajectory while the constraints on states and control input remain unimpaired of the value of the weighting matrices.This approach is demonstrated in a simulation case study involving a continuous stirred tank reactor, where multi-component chemical reaction takes place.

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Reference Governors

Cited by 3 publications

References 19 publications

Enhancing Closed-Loop Performance in Manufacturing Processes Using Universal Controller Tuning for Industrial Practice

Enhancing Closed-Loop Performance in Manufacturing Processes Using Universal Controller Tuning for Industrial Practice

A Feasibility Condition for the Governor-based Tuning of Explicit MPC: Application to a Hydraulic Plant

Neural network based explicit MPC for chemical reactor control

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