Neuroevolutive Control of Industrial Processes Through Mapping Elites
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Date
2021
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Abstract
Classical model-based control techniques used in process control applications present a tradeoff between performance and computational load, especially when using complex nonlinear methods. Learning-based techniques that allow the controller to learn policies from data represent an appealing alternative with potential to reduce the computational burden of real-time optimization. This article presents an efficient learning-based neural controller, optimized using evolutionary algorithms, designed especially for maintaining diversity of individuals. The search of solutions is conducted in the parameter space of a population of deep neural networks, which are efficiently encoded with a novel compression algorithm. Evaluation against strong baselines demonstrates that the proposed controller achieves better performance in most of the chosen evaluation metrics. Results suggest that learning-based controllers are a promising option for next-generation process control in the context of Industry 4.0.
Description
Keywords
Process control, Sociology, Statistics, Optimization, Computational modeling, Approximation algorithms, Evolutionary computation