Neuroevolutive Control of Industrial Processes Through Mapping Elites

Langarica, Saul; Nunez, Felipe


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.

Más información

Título según WOS: Neuroevolutive Control of Industrial Processes Through Mapping Elites
Título de la Revista: IEEE Transactions on Industrial Informatics
Volumen: 17
Número: 5
Fecha de publicación: 2021
Página de inicio: 3703
Página final: 3713


Notas: ISI