Focusing on error-dynamics-based neurodynamic networks (EDNNs) for optimal control of real-world robots, this book interrogates the application of neurodynamic methods to time-varying constrained optimization (TVCO) problems.
Time-Varying Constrained Optimization and Robot Optimal Control: A Neurodynamic Approach
with NCP Functions presents a thorough examination of EDNNs and their applications in TVCO and optimal control of robots. The authors systematically introduce the theoretical foundations, design methodologies, and robotic applications of EDNNs, emphasizing their superiority to traditional optimization solvers. In doing so, this book aims to fill gaps in the application of EDNNs to constrained optimization tasks with a focus on both serial robots (e.g., the Franka Emika Panda robot) and parallel robots (e.g., the Gough-Stewart platform). Key industrial challenges, including obstacle avoidance, joint-limit avoidance, pose control, and high-precision path tracking, are addressed with groundbreaking systematic integration of EDNNs and robot control, as validated by robot simulations and physical experiments.
This book offers practical guidance for researchers and engineers while providing accessible insights for non-specialists, such as librarians and booksellers, on the value of EDNNs in advancing robotic control practices.
Weibing Li is currently an associate professor with the School of Computer Science and Engineering, Sun Yat-sen University. His research interests include robotics and neural networks. He has published more than 130 papers in journals, including IEEE TNNLS, IEEE TMECH, IEEE TSMC, IEEE TCYB, and more.
Yehui Li is currently a postdoctoral fellow with the School of Computer Science and Engineering, Sun Yat-sen University. His research interests include robotics and neural networks. He has published more than 30 papers in journals, including IEEE TMECH, IEEE TSMC, IEEE TBME, IEEE TIM, and more.
Kai Huang is currently a full professor with the School of Computer Science and Engineering, Sun Yat-sen University. His research interests include robotics and real-time systems. He has published more than 100 papers, including Science Robotics, Nature Machine Intelligence, International Journal of Robotics Research, and more.
Yongping Pan is currently a full professor with the School of Automation, Southeast University. His research interests include automatic control and machine learning for robotics. He has authored or co-authored over 200 peer-reviewed academic papers, including IEEE TRO, IEEE TAC, IEEE TNNLS, and more.
