Artificial cognitive control with self-x capabilities: A case study of a micro-manufacturing process

Nowadays, even though cognitive control architectures form an important area of research, there are many constraints on the broad application of cognitive control at an industrial level and very few systematic approaches truly inspired by biological processes, from the perspective of control engineering. Thus, our main purpose here is the emulation of human socio-cognitive skills, so as to approach control engineering problems in an effective way at an industrial level. The artificial cognitive control architecture that we propose, based on the shared circuits model of socio-cognitive skills, seeks to overcome limitations from the perspectives of computer science, neuroscience and systems engineering. The design and implementation of artificial cognitive control architecture is focused on four key areas: (i) self-optimization and self-leaning capabilities by estimation of distribution and reinforcement-learning mechanisms; (ii) portability and scalability based on low-cost computing platforms; (iii) connectivity based on middleware; and (iv) model-driven approaches. The results of simulation and real-time application to force control of micro-manufacturing processes are presented as a proof of concept. The proof of concept of force control yields good transient responses, short settling times and acceptable steady-state error. The artificial cognitive control architecture built into a low-cost computing platform demonstrates the suitability of its implementation in an industrial setup

Diseño e implementación de estrategias de auto-optimización y autoadaptación para sistemas distribuidos a gran escala

Large scale distributed software systems are complex systems that need to be able to adapt to a highly dynamic environment and changing user needs. In this context, the main objective of this project is the development of new selfadaptive strategies, along with the methodologies and tools required for their analysis and design. In this work, we design and implement a self-adaptive architecture inspired by IBM's Monitor-Analyze-Plan-Act over a Knowledge base architecture, and we develop new self-adaptive strategies specific for wireless sensor networks following a methodology borrowed from control engineering. More in detail, we start this work by designing in UML a software library for the development of self-adaptive capabilities, which will be implemented as a Java package. After that, we model two distributed software systems using an actor oriented approach in Ptolemy II. Next, we develop the self-adaptive strategies based on fuzzy inference systems and introduce them in the models as new actors. Finally, we are able to execute a simulation of the system, which allows us to perform an automatic optimization of the parameters of the sytem with the cross-entropy method and to test the suitability of the designed strategies. Based on the simulation results, we have assessed the good results yielded by the strategies and the potential of the modeling tool for the design and simulation of distributed software systems. But more importantly, this work demonstrates the usefulness of a control engineering approach to solve problems related to the dynamic behavior of software systems.Los sistemas distribuidos a gran escala son sistemas complejos que necesitan adaptarse a un entorno altamente dinámico y a las distintas necesidades del usuario. En este contexto, el objetivo principal de este proyecto es el desarrollo de nuevas estrategias de auto-adaptación, a la vez que las metodologías y herramientas necesarias para su análisis y diseño. En este trabajo, diseñamos e implementamos una arquitectura para capacidades autoadaptativas en sistemas software insipirada en la arquitectura Monitor- Analyze-Plan-Act over a Knowledge base de IBM, y desarrollamos nuevas estrategias de auto-adaptación específicas para redes de sensores inhalámbricas siguiendo una metodología tomada de la ingeniería de control. Más concretamente, comenzamos este trabajo diseñando en UML una librería software para el desarrollo de capacidades auto-adaptativas, que luego implementamos como un paquete Java. A continuación, modelamos dos sistemas distribuidos usando un enfoque orientado a actores en Ptolemy II. Posteriormente, desarrollamos estrategias auto-adaptativas basadas en sistemas de inferencia difusa y las insertamos en los modelos como nuevos actores. Finalmente, ejecutamos varias simulaciones del sistema, lo cual nos permite realizar una optimización automática de los parámetros del sistema mediante el uso del método de entropía cruzada y, además, probar el desempeño de las estrategias diseñadas. Basándonos en los resultados de estas simulaciones, hemos podido comprobar los buenos resultados que ofrecen las estrategias de auto-adaptación implementadas y el potencial de la herramienta de modelado para el diseño y la simulación de sistemas distribuidos. Pero lo más importante es que este trabajo demuestra la utilidad de enfocar desde la ingeniería de control la resolución de problemas relacionados con el comportamiento dinámico de sistemas software

Métodos clásicos y de soft-computing en la optimización de procesos complejos: Aplicación a un proceso de fabricación

Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, febrero de 201

Artificial cognitive architecture with self-learning and self-optimization capabilities. Case studies in micromachining processes

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de lectura : 22-09-201