Adaptive Control of Bio-Inspired Manufacturing Systems (Research on Intelligent Manufacturing)

✍ Scribed by Dunbing Tang, Kun Zheng, Wenbin Gu

• Publisher: Springer
• Year: 2020
• Tongue: English
• Leaves: 134
• Edition: 1st ed. 2020
• Category: Library

✦ Synopsis

This book introduces state-of-the-art models and methods based on the neuroendocrine-immune-inspired approaches in the field of manufacturing control systems. It develops various bio-inspired intelligent approaches for multiple applications in order to efficiently generate production plans and control solutions and agilely deal with the frequent unexpected disturbances at the shop floor level. It also provides an introduction to bio-inspired manufacturing systems with intelligent control structures and the latest technologies. Further, the book describes recent advances in the bio-inspired methodology for a high-level adaptability in manufacturing systems, including the bio-inspired control architecture and the implementation of intelligent and adaptive control approaches based on neuroendocrine-immune mechanisms and hormone-regulation principles.
It offers a valuable resource for graduate students, researchers and engineers in the fields of production management, manufacturing system control and related areas.

✦ Table of Contents

Acknowledgements
Contents
1 Bio-Inspired Manufacturing System Model
1.1 Introduction and Synopsis
1.2 The Biological Background of BIMS
1.2.1 Nervous System
1.2.2 Endocrine System
1.2.3 Immune System
1.2.4 Neuroendocrine-Immune System
1.3 Bio-Inspired Manufacturing System (BIMS)
1.4 Control Model of BIMS
1.4.1 Biologic Hormone Regulation Mechanism
1.4.2 Hormone Regulation Model of BIMS
1.5 Conclusion
References
2 Hormone Regulation Based Algorithms for Production Scheduling Optimization
2.1 Introduction and Synopsis
2.2 The Job-Shop Scheduling Problem Model
2.3 Hormone Modulation Mechanism
2.4 An IAPSO for Job-Shop Scheduling Problem
2.4.1 Traditional PSO
2.4.2 IAPSO Based on the Hormone Regulation Mechanism
2.5 An IAGA for Job-Shop Scheduling Problem
2.5.1 Traditional GA
2.5.2 An IAGA for Job-Shop Scheduling Problem
2.6 Application of Neuroendocrine-Inspired Optimization Algorithms for Production Scheduling
2.6.1 Application of the IAPSO for the JSP
2.6.2 The Application of the IAGA for JSSP
2.7 Conclusion
References
3 Hormone Regulation Based Approach for Distributed and On-line Scheduling of Machines and AGVs
3.1 Introduction and Synopsis
3.2 On-line Scheduling Model
3.2.1 On-line Scheduling Approach
3.2.2 Information Processing Mechanism in Endocrine System
3.2.3 On-line Scheduling Model Inspired by the Principle of Hormone Diffusion and Reaction
3.3 Allocation Mechanism Based on Hormone Regulation Mechanism
3.3.1 Hormone Regulation Mechanism Background
3.3.2 Time Parameters in Scheduling
3.3.3 Allocation Mechanism
3.4 Distributed Cooperation Mechanism for On-line Scheduling
3.5 Experimental Study
3.6 Conclusions
References
4 Production Control Strategy Inspired by Neuroendocrine Regulation
4.1 Introduction and Synopsis
4.2 Literature Review
4.3 General Principle of Neuroendocrine System
4.3.1 Negative Feedback Mechanism of Hormone Regulation
4.3.2 Hill Functions of Hormone Regulation
4.4 Control Model of Production System
4.4.1 Hormone Regulation Model of Production System
4.4.2 Design of Controllers Based on Hill Function
4.5 Performance Analysis with Numerical Example
4.5.1 Operation of the Control Model
4.5.2 Analysis of the Control Model Under Normal State
4.5.3 Analysis of the Control Model Under Extreme State
4.6 Conclusions and Future Work
References
5 Neuroendocrine-Immune Regulation Based Approach for Disturbance Handling
5.1 Introduction and Synopsis
5.2 Disturbance Handling of BIMS
5.2.1 Disturbance Handling Mechanism of BIMS
5.2.2 Monitoring and Scheduling Functions of BIMC
5.2.3 Disturbance Handling Processes of BIMC
5.3 Disturbance Detection and Diagnosis of BIMS
5.3.1 Disturbance Detection
5.3.2 Diagnosis of Disturbances
5.4 Disturbance Handling Strategies of BIMS
5.5 Case Study
5.5.1 Experimental Description
5.5.2 Experiment Analysis
5.5.3 Performance Indicator Analysis
5.6 Conclusion
References
6 Development of Simulation Platform for BIMS
6.1 Introduction and Synopsis
6.2 Simulation Platform Architecture
6.3 Physical Simulation Platform
6.3.1 Physical Simulation Platform Architecture
6.3.2 Quasi-hormone Communication Protocols
6.3.3 Physical Simulation Platform
6.4 Software Simulation Platform
6.4.1 Software Simulation Platform Architecture
6.4.2 Function Modules of Software Simulation Platform
6.5 Conclusion
References