Computational Intelligent Techniques in Mechatronics
This book, set against the backdrop of huge advancements in artificial intelligence and machine learning within mechatronic systems, serves as a comprehensive guide to navigating the intricacies of mechatronics and harnessing its transformative potential. Mechatronics has been a revolutionary force...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Alemán |
| Publicado: |
Hoboken, NJ :
Scrivener Publishing LLC
[2024]
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| Edición: | First edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009852337106719 |
Tabla de Contenidos:
- Cover
- Series Page
- Title Page
- Copyright Page
- Contents
- Preface
- Chapter 1 AI in Mechatronics
- 1.1 Introduction to AI Techniques for Mechatronics
- 1.1.1 Overview of Key AI Approaches
- 1.1.2 Benefits of Integrating AI in Mechatronic Systems
- 1.2 Machine Learning for Mechatronic Systems
- 1.2.1 Supervised, Unsupervised, and Reinforcement Learning Techniques
- 1.2.2 Applications in Control, Prediction, Optimization, and Diagnostics
- 1.2.3 Case Studies of Machine Learning in Robotics, Vehicles, and Automation
- 1.3 Computer Vision for Mechatronic Perception
- 1.3.1 Image Processing and Computer Vision Techniques
- 1.3.2 Enabling Environmental Perception and Scene Understanding
- 1.3.3 Vision-Based Control, Inspection, and Monitoring
- 1.4 Soft Computing Techniques
- 1.4.1 Fuzzy Logic Systems for Knowledge Representation and Control
- 1.4.2 Bio-Inspired Algorithms Like Neural Networks and Genetic Algorithms
- 1.4.3 Hybrid Intelligent Systems
- 1.5 AI Planning and Decision-Making
- 1.5.1 Automated Planning Algorithms for Sequencing Actions
- 1.5.2 Decision-Making Under Uncertainty
- 1.5.3 Applications in Navigation, Manufacturing Automation, Etc.
- 1.6 Natural Language Interaction
- 1.6.1 Speech Recognition and Natural Language Processing
- 1.6.2 Enabling Intuitive Human-Machine Interaction
- 1.6.3 Use Cases in Service Robots, Intelligent Agents, Human-Robot Collaboration
- 1.7 AI in Mechatronic System Design
- 1.7.1 Simulation of AI-Based Controllers and Behaviors
- 1.7.2 Tools for Virtual Prototyping of Intelligent Mechatronics
- 1.7.3 AI-Driven Design Optimization
- 1.8 Challenges and Future Outlook
- 1.8.1 Current Limitations in Applying AI to Mechatronics
- 1.8.2 Safety, Security, and Robustness Considerations
- 1.8.3 Emerging Trends and Opportunities
- 1.9 Artificial General Intelligence (AGI).
- 1.9.1 AGI and Narrow AI
- 1.9.2 Historical Development of AGI
- 1.9.3 State of AGI in Mechatronics Today
- 1.9.4 Future Possibilities
- 1.10 Conclusion
- 1.10.1 Insights Into AGI and Mechatronics Education
- 1.10.2 Motivating Message
- References
- Chapter 2 Thermodynamics for Mechatronics
- 2.1 Introduction
- 2.2 Defining Mechatronics and Its Interdisciplinary Nature
- 2.2.1 The Role of Thermodynamics in Engineering Innovation
- 2.2.2 Significance of Integrating Thermodynamics in Mechatronics
- 2.3 Fundamentals of Thermodynamics for Mechatronics
- 2.3.1 Laws of Thermodynamics: Concepts and Implications
- 2.3.2 Heat Transfer Mechanisms and Applications in Mechatronics
- 2.3.3 Energy Conversion Principles and Efficiency Considerations
- 2.4 Enhancing Efficiency in Mechatronics Through Thermodynamics
- 2.4.1 Thermodynamics-Driven Design Optimization for Mechatronic Systems
- 2.4.2 Thermal Management Strategies: Heat Dissipation and Regulation
- 2.4.3 Energy Efficiency Techniques and Heat Recovery in Mechatronics
- 2.5 Sustainability and Thermodynamics in Mechatronics
- 2.5.1 Mechatronics as a Catalyst for Sustainable Engineering
- 2.5.2 Environmental Benefits of Energy-Efficient Mechatronics
- 2.5.3 Utilizing Thermodynamics for Sustainable Resource Management
- 2.6 Innovative Applications and Future Trends
- 2.6.1 Harnessing Waste Heat: Thermoelectric Generators in Mechatronics
- 2.6.2 Embracing Energy-Frugal Systems: Future Trends and Challenges
- 2.6.3 Challenges in Implementing Future Trends
- 2.7 Educational and Professional Implications
- 2.7.1 Emphasizing the Importance of Incorporating Thermodynamics Education in Mechatronics Programs
- 2.7.2 Encouraging Interdisciplinary Collaboration Among Engineers to Optimize Energy-Frugal Mechatronic Systems.
- 2.7.3 Conclusion: Leveraging Thermodynamics for Energy-Efficient Mechatronic Designs
- References
- Chapter 3 Role of Data Acquisition, Sensors, and Actuators in Mechatronics Industry
- 3.1 Introduction
- 3.2 Literature Survey
- 3.3 Fundamentals of Data Acquisition
- 3.3.1 Types of Data Acquisition Systems
- 3.3.2 Analog-to-Digital Conversion
- 3.3.3 Sampling and Signal Conditioning
- 3.3.4 Sensors in Mechatronics
- 3.3.5 Actuators in Mechatronics
- 3.4 Coordination and Synchronization in Mechatronic Systems
- 3.4.1 Interplay Between Data Acquisition, Sensors, and Actuators
- 3.5 Industrial Automation and Robotics
- 3.5.1 Automotive and Transportation Systems
- 3.5.2 Healthcare and Biomedical Applications
- 3.6 Technical Challenges in Integration and Compatibility
- 3.6.1 Innovations Driving Mechatronics Advancements
- 3.6.2 Mechatronics Industry and Industry 4.0
- 3.7 Future Trends and Implications
- 3.7.1 Advancements in Sensor Technology
- 3.7.2 Integration of AI and IoT in Mechatronic Systems
- 3.8 Conclusion
- References
- Chapter 4 Optimization Techniques for Mechatronics: A Comprehensive Review and Future Directions
- 4.1 Introduction
- 4.1.1 Key Components of Mechatronics
- 4.2 Related Work
- 4.3 Optimization in Mechatronics Design
- 4.4 Optimization in Mechatronics Control
- 4.5 Optimization in Mechatronics Manufacturing
- 4.6 Multi-Objective Optimization in Mechatronics
- 4.7 Real-Time Optimization for Mechatronics
- 4.8 Challenges in Optimization for Mechatronics
- 4.9 Opportunities in Optimization for Mechatronics
- 4.10 Future Directions in Optimization for Mechatronics
- 4.11 Conclusion
- Declarations
- Conflict of Interest
- Ethics Approval and Consent to Participate
- Consent for Publication
- Competing Interests
- Open Access
- Funding Statement
- References.
- Chapter 5 Reinforcement Learning for Adaptive Mechatronics Systems
- 5.1 Introduction to Adaptive Mechatronics Systems
- 5.2 Fundamentals of Reinforcement Learning
- 5.3 Reinforcement Learning Algorithms for Mechatronics
- 5.4 Adaptive Control Strategies in Mechatronics
- 5.5 Autonomous Decision-Making in Mechatronics
- 5.6 Optimization and Energy Efficiency in Mechatronics
- 5.7 Safety and Robustness in Reinforcement Learning
- 5.8 Real-World Applications and Case Studies
- 5.9 Challenges and Future Directions
- 5.10 Ethical and Societal Implications
- 5.11 Conclusion
- References
- Further Reading
- Chapter 6 Application of PLC in the Mechatronics Industry
- 6.1 Introduction
- 6.1.1 History and Evolution of PLCs
- 6.1.2 Literature Review
- 6.1.3 Scope and Objectives
- 6.2 Role of PLC in Mechatronics System Integration
- 6.2.1 Integration of PLC with Mechanical Systems
- 6.2.2 Integration of PLC with Electrical Systems
- 6.2.3 Integration of PLC with Computing Systems
- 6.3 PLC Applications in Mechatronics Industry
- 6.3.1 Programming and Implementation of PLC in Mechatronics
- 6.4 PLC in Mechatronics System Design
- 6.4.1 Integration of PLCs in Mechatronics Systems
- 6.4.2 Mechatronics System Components
- 6.4.3 PLC Hardware Selection
- 6.5 Safety in Mechatronics Systems
- 6.5.1 Safety Standards and Regulations
- 6.5.2 Safety Interlocks and Emergency Stop Systems
- 6.5.3 Fault Detection and Tolerance
- 6.6 Case Studies for Mechatronics Systems Using PLCs
- 6.6.1 Automotive Manufacturing
- 6.6.2 Bottling and Packaging Industry
- 6.6.3 Aircraft Landing Gear Control
- 6.6.4 E-Commerce Warehouse Automation
- 6.6.5 CNC Machining Centers
- 6.6.6 Precision Agriculture
- 6.7 Challenges and Future Trends
- 6.7.1 Challenges in Implementing Mechatronics Systems
- 6.7.2 Emerging Technologies and Trends in Mechatronics.
- 6.8 Conclusion
- References
- Chapter 7 Fuzzy Logic and Its Applications in Mechatronic Control Systems
- 7.1 Introduction
- 7.1.1 Applications of Fuzzy Logic in Mechatronic Control Systems
- 7.2 Fuzzy Control Systems
- 7.2.1 Bridging Precision and Flexibility
- 7.2.2 Understanding Fuzzy Control Systems
- 7.2.3 Applications of Fuzzy Control Systems
- 7.2.4 Benefits and Challenges
- 7.2.5 Advantages of Fuzzy Logic in Mechatronic Control
- 7.3 Fuzzy Logic Applications in Mechatronic Control Systems
- 7.4 Fuzzy Expert Systems in Mechatronics
- 7.4.1 Enhancing Decision-Making and Control
- 7.4.2 Understanding Fuzzy Expert Systems
- 7.4.3 Applications in Mechatronics
- 7.4.4 Benefits and Challenges
- 7.5 Fuzzy Logic and Machine Learning in Mechatronics
- 7.5.1 A Synergistic Approach to Intelligent Control
- 7.5.2 Fuzzy Logic: Handling Uncertainty and Complex Relationships
- 7.5.3 Machine Learning: Data-Driven Adaptability
- 7.5.4 Applications in Mechatronics
- 7.5.5 Benefits and Challenges
- 7.6 Fuzzy Control in Multivariable Mechatronic Systems
- 7.6.1 Navigating Complexity with Adaptability
- 7.6.2 Challenges in Multivariable Mechatronic Systems
- 7.6.3 Fuzzy Control: A Multivariable Solution
- 7.6.4 Applications and Benefits
- 7.6.5 Benefits in Specific Applications
- 7.6.6 Challenges and Considerations
- 7.7 Industrial Automation and Fuzzy Logic
- 7.7.1 Enhancing Precision and Adaptability
- 7.7.2 Challenges in Industrial Automation
- 7.7.3 Fuzzy Logic: A Solution for Industrial Automation
- 7.7.4 Benefits and Considerations
- 7.8 Challenges and Future Directions
- 7.8.1 Challenges
- 7.8.2 Future Directions
- 7.9 Conclusion
- References
- Further Reading
- Chapter 8 Drones and Autonomous Robotics Incorporating Computational Intelligence
- 8.1 Introduction
- 8.2 Literature Review.
- 8.3 Navigation and Path Planning.
