Overview
- Nominated as an outstanding thesis by the Intelligent Control Group of of the Comité Español de Automática (CEA)
- Reports on the design and implementation of a bio-inspired artificial cognitive architecture for control purposes
- Merges knowledge gained in different fields, from AI to signal processing and control engineering
- Describes an amazing of variety of micromaching tests performed to validate the developed architecture
Part of the book series: Springer Theses (Springer Theses)
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Table of contents (4 chapters)
Keywords
- Self-learning
- Self-optimization
- Micromachining Processes
- Computational Intelligence Models
- Self-adaptive control
- Q-learning Algorithm
- Multi-objective Cross-entropy
- Roughness Surface Model
- Sensors
- Expert Systems
- Cyber-physical Systems
- Self-decision-making
- Force Signal Processing
- Vibration Analysis
- Raspberry Implementation
- Distributed Control Architecture
- Fuzzy Controllers
- Predictive Models
- Industrial Use Case
About this book
This book introduces three key issues: (i) development of a gradient-free method to enable multi-objective self-optimization; (ii) development of a reinforcement learning strategy to carry out self-learning and finally, (iii) experimental evaluation and validation in two micromachining processes (i.e., micro-milling and micro-drilling). The computational architecture (modular, network and reconfigurable for real-time monitoring and control) takes into account the analysis of different types of sensors, processing strategies and methodologies for extracting behavior patterns from representative process’ signals. The reconfiguration capability and portability of this architecture are supported by two major levels: the cognitive level (core) and the executive level (direct data exchange with the process). At the same time, the architecture includes different operating modes that interact with the process to be monitored and/or controlled. The cognitive level includes three fundamentalmodes such as modeling, optimization and learning, which are necessary for decision-making (in the form of control signals) and for the real-time experimental characterization of complex processes. In the specific case of the micromachining processes, a series of models based on linear regression, nonlinear regression and artificial intelligence techniques were obtained. On the other hand, the executive level has a constant interaction with the process to be monitored and/or controlled. This level receives the configuration and parameterization from the cognitive level to perform the desired monitoring and control tasks.
Authors and Affiliations
Bibliographic Information
Book Title: Artificial Cognitive Architecture with Self-Learning and Self-Optimization Capabilities
Book Subtitle: Case Studies in Micromachining Processes
Authors: Gerardo Beruvides
Series Title: Springer Theses
DOI: https://doi.org/10.1007/978-3-030-03949-3
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Hardcover ISBN: 978-3-030-03948-6Published: 31 January 2019
eBook ISBN: 978-3-030-03949-3Published: 14 December 2018
Series ISSN: 2190-5053
Series E-ISSN: 2190-5061
Edition Number: 1
Number of Pages: XXIX, 195
Topics: Control and Systems Theory, Computational Intelligence, Manufacturing, Machines, Tools, Processes, Artificial Intelligence