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<p>Preface xxi</p> <p><b>1 AI in Mechatronics 1<br /> </b><i>Vansh Gehlot and Prashant Singh Rana</i></p> <p>1.1 Introduction to AI Techniques for Mechatronics 2</p> <p>1.2 Machine Learning for Mechatronic Systems 5</p> <p>1.3 Computer Vision for Mechatronic Perception 9</p> <p>1.4 Soft Computing Techniques 13</p> <p>1.5 AI Planning and Decision-Making 16</p> <p>1.6 Natural Language Interaction 19</p> <p>1.7 AI in Mechatronic System Design 21</p> <p>1.8 Challenges and Future Outlook 26</p> <p>1.9 Artificial General Intelligence (AGI) 30</p> <p>1.10 Conclusion 35</p> <p>References 38</p> <p><b>2 Thermodynamics for Mechatronics 41<br /> </b><i>Yadav Krishnakumar Rajnath, Shrikant Tiwari and Virendra Kumar</i></p> <p>2.1 Introduction 42</p> <p>2.2 Defining Mechatronics and Its Interdisciplinary Nature 43</p> <p>2.3 Fundamentals of Thermodynamics for Mechatronics 46</p> <p>2.4 Enhancing Efficiency in Mechatronics Through Thermodynamics 52</p> <p>2.5 Sustainability and Thermodynamics in Mechatronics 58</p> <p>2.6 Innovative Applications and Future Trends 66</p> <p>2.7 Educational and Professional Implications 72</p> <p>References 79</p> <p><b>3 Role of Data Acquisition, Sensors, and Actuators in Mechatronics Industry 83<br /> </b><i>Harpreet Kaur Channi</i></p> <p>3.1 Introduction 84</p> <p>3.2 Literature Survey 86</p> <p>3.3 Fundamentals of Data Acquisition 87</p> <p>3.4 Coordination and Synchronization in Mechatronic Systems 94</p> <p>3.5 Industrial Automation and Robotics 95</p> <p>3.6 Technical Challenges in Integration and Compatibility 97</p> <p>3.7 Future Trends and Implications 100</p> <p>3.8 Conclusion 102</p> <p>References 103</p> <p><b>4 Optimization Techniques for Mechatronics: A Comprehensive Review and Future Directions 109<br /> </b><i>Ikvinderpal Singh and Sapandeep Kaur Dhillon</i></p> <p>4.1 Introduction 110</p> <p>4.2 Related Work 111</p> <p>4.3 Optimization in Mechatronics Design 113</p> <p>4.4 Optimization in Mechatronics Control 116</p> <p>4.5 Optimization in Mechatronics Manufacturing 118</p> <p>4.6 Multi-Objective Optimization in Mechatronics 121</p> <p>4.7 Real-Time Optimization for Mechatronics 123</p> <p>4.8 Challenges in Optimization for Mechatronics 126</p> <p>4.9 Opportunities in Optimization for Mechatronics 127</p> <p>4.10 Future Directions in Optimization for Mechatronics 128</p> <p>4.11 Conclusion 130</p> <p>References 132</p> <p><b>5 Reinforcement Learning for Adaptive Mechatronics Systems 135<br /> </b><i>D. Sathya, G. Saravanan and R. Thangamani</i></p> <p>5.1 Introduction to Adaptive Mechatronics Systems 136</p> <p>5.2 Fundamentals of Reinforcement Learning 139</p> <p>5.3 Reinforcement Learning Algorithms for Mechatronics 142</p> <p>5.4 Adaptive Control Strategies in Mechatronics 144</p> <p>5.5 Autonomous Decision-Making in Mechatronics 147</p> <p>5.6 Optimization and Energy Efficiency in Mechatronics 149</p> <p>5.7 Safety and Robustness in Reinforcement Learning 153</p> <p>5.8 Real-World Applications and Case Studies 155</p> <p>5.9 Challenges and Future Directions 174</p> <p>5.10 Ethical and Societal Implications 176</p> <p>5.11 Conclusion 178</p> <p>References 179</p> <p>Further Reading 181</p> <p><b>6 Application of PLC in the Mechatronics Industry 185<br /> </b><i>Harpreet Kaur Channi, Pulkit Kumar and Arvind Dhingra</i></p> <p>6.1 Introduction 186</p> <p>6.2 Role of PLC in Mechatronics System Integration 191</p> <p>6.3 PLC Applications in Mechatronics Industry 195</p> <p>6.4 PLC in Mechatronics System Design 197</p> <p>6.5 Safety in Mechatronics Systems 199</p> <p>6.6 Case Studies for Mechatronics Systems Using PLCs 202</p> <p>6.7 Challenges and Future Trends 204</p> <p>6.8 Conclusion 206</p> <p>References 207</p> <p><b>7 Fuzzy Logic and Its Applications in Mechatronic Control Systems 211<br /> </b><i>D. Sathya, G. Saravanan and R. Thangamani</i></p> <p>7.1 Introduction 212</p> <p>7.2 Fuzzy Control Systems 215</p> <p>7.3 Fuzzy Logic Applications in Mechatronic Control Systems 220</p> <p>7.4 Fuzzy Expert Systems in Mechatronics 221</p> <p>7.5 Fuzzy Logic and Machine Learning in Mechatronics 223</p> <p>7.6 Fuzzy Control in Multivariable Mechatronic Systems 227</p> <p>7.7 Industrial Automation and Fuzzy Logic 230</p> <p>7.8 Challenges and Future Directions 233</p> <p>7.9 Conclusion 235</p> <p>References 236</p> <p>Further Reading 237</p> <p><b>8 Drones and Autonomous Robotics Incorporating Computational Intelligence 243<br /> </b><i>R. Thangamani, R. K. Suguna and G. K. Kamalam</i></p> <p>8.1 Introduction 244</p> <p>8.2 Literature Review 248</p> <p>8.3 Navigation and Path Planning 252</p> <p>8.4 Perception and Object Detection 258</p> <p>8.5 Adaptive Control and Decision-Making 265</p> <p>8.6 Swarm Robotics and Multi-Agent Systems 266</p> <p>8.7 Autonomous Drone Delivery Systems 270</p> <p>8.8 Human–Robot Interaction and Collaboration 277</p> <p>8.9 Future Trends and Challenges 284</p> <p>8.10 Ethical Implications of Autonomous Robotics and Drones 289</p> <p>8.11 Conclusion 293</p> <p>References 294</p> <p><b>9 Exploring the Convergence of Artificial Intelligence and Mechatronics in Autonomous Driving 297<br /> </b><i>Ritika Wason, Parul Arora, Vishal Jain, Devansh Arora and M. N. Hoda</i></p> <p>9.1 Introduction 297</p> <p>9.2 Key Components of Advanced Driver Systems 301</p> <p>9.3 Current State of AI-Enabled Self-Driving Mechatronics 303</p> <p>9.4 Challenges in Self-Driving Mechatronics 305</p> <p>9.5 Advantages of Self-Driving Mechatronics 307</p> <p>9.6 Self-Driving and Environmental Sustainability 308</p> <p>9.7 Legal and Safety Issues in Autonomous Driving 310</p> <p>9.8 Conclusion 310</p> <p>9.9 Future Directions in Self-Driving Mechatronics 313</p> <p>References 313</p> <p><b>10 Improving Power Quality for Industry Control Using Mechatronics Devices 317<br /> </b><i>Pulkit Kumar, Harpreet Kaur Channi and Surbhi Gupta</i></p> <p>10.1 Introduction 318</p> <p>10.2 Power Quality in Industrial Settings 322</p> <p>10.3 Mechatronics Devices for Power Quality Improvement 324</p> <p>10.4 Case Studies of Mechatronics Devices in Industry Control 330</p> <p>10.5 Integration of Mechatronics Devices in Industrial Control Systems 333</p> <p>10.6 Future Trends and Innovations in Mechatronics for Power Quality Improvement 337</p> <p>10.7 Conclusion 342</p> <p>References 342</p> <p><b>11 Study on Integrated Neural Networks and Fuzzy Logic Control for Autonomous Electric Vehicles 347<br /> </b><i>S. Boopathi</i></p> <p>11.1 Introduction 348</p> <p>11.2 Fundamentals of Neural Networks and Fuzzy Logic 351</p> <p>11.3 Autonomous Electric Vehicles: Challenges and Control Requirements 354</p> <p>11.4 Neural Network–Based Control for Autonomous Electric Vehicles 357</p> <p>11.5 Fuzzy Logic Control for Energy-Efficient Driving 361</p> <p>11.6 Integration of Neural Networks and Fuzzy Logic for Enhanced Autonomy 367</p> <p>11.7 Case Studies and Applications 372</p> <p>11.8 Future Prospects and Challenges 374</p> <p>11.9 Conclusions 375</p> <p>List of Abbreviations 375</p> <p>References 375</p> <p><b>12 Advancing Mechatronics Through Artificial Intelligence 381<br /> </b><i>Pawan Whig, Jhansi Bharathi Madavarapu, Venugopal Reddy Modhugu, Balaram Yadav Kasula and Ashima Bhatnagar Bhatia</i></p> <p>12.1 Introduction 381</p> <p>12.2 Foundations of Mechatronics and Artificial Intelligence 386</p> <p>12.3 Synergies Between Artificial Intelligence and Mechatronics 388</p> <p>12.4 Case Studies: AI-Driven Advances in Mechatronics 390</p> <p>12.5 Challenges and Opportunities 392</p> <p>12.6 Future Directions and Trends 395</p> <p>12.7 Conclusion 397</p> <p>12.8 Future Scope 398</p> <p>References 398</p> <p><b>13 Computational Intelligent Techniques in Mechatronics: Emerging Trends and Case Studies 401<br /> </b><i>Anita Mohanty, Ambarish G. Mohapatra, Subrat Kumar Mohanty, Bright Keswani and Sasmita Nayak</i></p> <p>13.1 Introduction to Mechatronics and Computational Intelligence 402</p> <p>13.2 Artificial Neural Networks (ANNs) in Mechatronics 403</p> <p>13.3 Reinforcement Learning in Mechatronics 407</p> <p>13.4 Evolutionary Algorithms for Mechatronic System Design 412</p> <p>13.5 Emerging Trends in Mechatronics with Computational Intelligence 419</p> <p>13.6 Real-World Case Studies 427</p> <p>13.7 Conclusion 439</p> <p>References 441</p> <p><b>14 Advanced Sensing Systems in Automobiles: Computational Intelligence Approach 445<br /> </b><i>Mamta B. Savadatti and Ajay Sudhir Bale</i></p> <p>14.1 Introduction 445</p> <p>14.2 Computational Intelligence Approach 447</p> <p>14.3 Methodology 463</p> <p>14.4 Conclusions 466</p> <p>References 467</p> <p><b>15 Design of Arduino UNO–Based Novel Multi-Featured Robot 471<br /> </b><i>Jaspinder Kaur, Rohit Anand, Nidhi Sindhwani, Ajay Kumar Sharma and Vishal Jain</i></p> <p>15.1 Introduction 472</p> <p>15.2 Design Implementation 473</p> <p>15.3 Proposed Model 477</p> <p>15.4 Process and Working Methodology 478</p> <p>15.5 Experiment and Applications 482</p> <p>15.6 Conclusion 484</p> <p>15.7 Future Scope 485</p> <p>Acknowledgments 485</p> <p>References 485</p> <p><b>16 Integrating Mechatronics in Autonomous Agricultural Machinery: A Case Study 491<br /> </b><i>N. V. Suresh, Ananth Selvakumar, Gajalakshmi Sridhar and Vishal Jain</i></p> <p>16.1 Introduction 492</p> <p>16.2 Case Background 493</p> <p>16.3 Literature Review 495</p> <p>16.4 Methodology 496</p> <p>16.5 Implementation 498</p> <p>16.6 Findings 501</p> <p>16.7 Suggestion 502</p> <p>16.8 Conclusion 504</p> <p>References 505</p> <p>Index 509</p>

<p><b>Kolla Bhanu Prakash, PhD</b>, is a professor and associate dean and R & D head for A.I. & Data Science Research Group at K L University, Vijayawada, Andhra Pradesh, India. He is also an adjunct professor atTaylors University, Malaysia. He has published 150+ research papers in international and national journals and conferences. He has authored two and edited 12 books as well as published 15 patents. His research interests include deep learning, data science, and quantum computing. <p><b>Satish Kumar Peddapelli, PhD</b>, is the Director at the Rajiv Gandhi University of Knowledge Technologies, IIIT-Basara, and Professor of Electrical Engineering, University College of Engineering, Osmania University, Hyderabad, India. His areas of interest are power electronics, drives, multi-level inverters, special machines and renewable energy systems. <p><b>Ivan C.K. Tam, PhD,</b> is an associate professor in the Dept. of Marine Engineering Design & Technology, as well as the Director of Innovation & Engagement at the University of Newcastle in Singapore. He has a wealth of experience in multi-disciplinary research and a strong track record of leading innovative projects. His research interests are in the clean fuel combustion process, exhaust emission control, energy management and renewable energy technology. <p><b>Wai Lok Woo, PhD,</b> received his doctorate in statistical machine learning from Newcastle University, UK. Prof Woo currently holds the Chair in Machine Learning with Northumbria University, UK. He is the Faculty Director of Research (Engineering and Environment), and Head of Research for Data Science and Artificial Intelligence. He was previously the Director of Research for Newcastle Research and Innovation Institute, and Director of Operations of Newcastle University. His major research is in mathematical theory and algorithms for data science and analytics. <p><b>Vishal Jain, PhD</b>, is an associate professor in the Department of Computer Science and Engineering, Sharda School of Engineering and Technology, Sharda University, Greater Noida, India. He has more than 16 years of experience in academics and has authored more than 100 research papers in reputed journals and conferences as well as edited several books with the Wiley-Scrivener imprint.

<p><b>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.</b> <p>Mechatronics has been a revolutionary force in engineering and medical robotics over the past decade. It will lead to a major industrial revolution and affect research in every field of engineering. This book covers the basics of mechatronics, computational intelligence approaches, simulation and modeling concepts, architectures, nanotechnology, real-time monitoring and control, different actuators, and sensors. The book explains clearly and comprehensively the engineering design process at different stages. As the historical divisions between the various branches of engineering and computer science become less clearly defined, mechatronics may provide a roadmap for nontraditional engineering students studying within the traditional university structure. This book covers all the algorithms and techniques found in mechatronics engineering, well explained with real-time examples, especially lab experiments that will be very informative to students and scholars. <p><b>Audience</b> <p>This resource is important for R & D departments in academia, government, and industry. It will appeal to mechanical engineers, electronics engineers, computer scientists, robotics engineers, professionals in manufacturing, automation and related industries, as well as innovators and entrepreneurs.

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