Abstract
The present paper proposes an approach for the development of a non-linear model-based predictive controller (NMPC) using a non-linear process model based on Artificial Neural Networks (ANNs). This work exploits recent trends on ANN literature using a TensorFlow implementation and shows how they can be efficiently used as support for closed-loop control systems. Furthermore, it evaluates how the generalization capability problems of neural networks can be efficiently overcome when the model that supports the control algorithm is used outside of its initial training conditions. The process’s transient response performance and steady-state error are parameters under focus and will be evaluated using a MATLAB’s Simulink implementation of a Coupled Tank Liquid Level controller and a Yeast Fermentation Reaction Temperature controller, two well-known benchmark systems for non-linear control problems.
Highlights
Nowadays, control systems have an important role in the automation industry due to the increasingly tight requirements posed over precision, performance, efficiency, and safety metrics of automatic systems
It is frequently to use the mean squared error as cost function, as defined by J in Equation (1), and our goal is to find the set of parameters that minimize its value: N
In the Yeast Fermentation Reactor Temperature model case, the lowest value in Table 4 occurs in the simulation number 3 with 10 neurons
Summary
Control systems have an important role in the automation industry due to the increasingly tight requirements posed over precision, performance, efficiency, and safety metrics of automatic systems They become ubiquitously present in many aspects of our daily life such as in housing heating systems, household appliances, among many other “intelligent” products that we rely on every day. While this “intelligence” is still far from accomplishing the same challenges humans can, the closed coupling between advanced control algorithms and good domain-specific process models has significantly expanded the application scenarios of autonomous computational systems. It is a set of control techniques (such as Dynamic Matrix Control (DMC) [2]
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