Page 113 - Read Online
P. 113

Page 34 of 35                        Kulkarni et al. Soft Sci. 2025, 5, 12  https://dx.doi.org/10.20517/ss.2023.51

                    environments. In: 2022 IEEE 5th International Conference on Soft Robotics (RoboSoft); Edinburgh, United Kingdom. IEEE; 2022.
                    pp. 693-8.  DOI
               264.      Li, Y.; Ang, K. H.; Chong, G. C. Y. PID control system analysis and design. IEEE. Control. Syst. May. 2006, 26, 32-41.  DOI
               265.      Campo, A. B. PID control design. In: Katsikis V, editor. MATLAB - A Fundamental Tool for Scientific Computing and Engineering
                    Applications - Volume 1. InTech; 2012. Available from: http://www.intechopen.com/books/matlab-a-fundamental-tool-for-scientific-
                    computing-and-engineering-applications-volume-1/pid-control-design. [Last accessed on 14 Jan 2025].
               266.      Borase, R. P.; Maghade, D. K.; Sondkar, S. Y.; Pawar, S. N. A review of PID control, tuning methods and applications. Int. J.
                    Dynam. Control. 2021, 9, 818-27.  DOI
               267.      Plum, F.; Labisch, S.; Dirks, J. H. SAUV-A bio-inspired soft-robotic autonomous underwater vehicle. Front. Neurorobot. 2020, 14,
                    8.  DOI  PubMed  PMC
               268.      Li, H.; Xu, Y.; Zhang, C.; Yang, H. Kinematic modeling and control of a novel pneumatic soft robotic arm. Chin. J. Aeronaut. 2022,
                    35, 310-9.  DOI
               269.      Dumont, G. A.; Huzmezan, M. Concepts, methods and techniques in adaptive control. In: Proceedings of the 2002 American Control
                    Conference (IEEE Cat. No.CH37301); Anchorage, USA. IEEE; 2002. pp. 1137-50.  DOI
               270.      Kaufman, H.; Bar-Kana, I.; Sobel, K. Direct adaptive control algorithms: theory and applications. New York, NY: Springer US;
                    1994.  DOI
               271.      Tonietti, G.; Bicchi, A. Adaptive simultaneous position and stiffness control for a soft robot arm. In: IEEE/RSJ International
                    Conference on Intelligent Robots and System; Lausanne, Switzerland. IEEE; 2002. pp. 1992-7.  DOI
               272.      Xu, F.; Wang, H.; Chen, W.; Wang, J. Adaptive visual servoing control for an underwater soft robot. Assem. Autom. 2018, 38, 669-
                    77.  DOI
               273.      Arulkumaran, K.; Deisenroth, M. P.; Brundage, M.; Bharath, A. A. Deep reinforcement learning: a brief survey. IEEE. Signal.
                    Process. Mag. 2017, 34, 26-38.  DOI
               274.      Joshi, D. J.; Kale, I.; Gandewar, S.; Korate, O.; Patwari, D.; Patil, S. Reinforcement learning: a survey. In: Swain D, Pattnaik PK,
                    Athawale T, editors. Machine learning and information processing. Singapore: Springer; 2021. pp. 297-308.  DOI
               275.      Ishige, M.; Umedachi, T.; Taniguchi, T.; Kawahara, Y. Exploring behaviors of caterpillar-like soft robots with a central pattern
                    generator-based controller and reinforcement learning. Soft. Robot. 2019, 6, 579-94.  DOI  PubMed  PMC
               276.      Wang, J.; Chortos, A. Control strategies for soft robot systems. Adv. Intell. Syst. 2022, 4, 2100165.  DOI
               277.      Mavrovouniotis, M.; Chang, S. Hierarchical neural networks. Comput. Chem. Eng. 1992, 16, 347-69.  DOI
               278.      Jain, L. C.; Seera, M.; Lim, C. P.; Balasubramaniam, P. A review of online learning in supervised neural networks. Neural. Comput.
                    Appl. 2014, 25, 491-509.  DOI
               279.      Psichogios, D. C.; Ungar, L. H. Direct and indirect model based control using artificial neural networks. Ind. Eng. Chem. Res. 1991,
                    30, 2564-73.  DOI
               280.      Narendra, K. S.; Mukhopadhyay, S. Adaptive control using neural networks and approximate models. IEEE. Trans. Neural. Netw.
                    1997, 8, 475-85.  DOI  PubMed
               281.      Hecht-Nielsen, R. III.3 - Theory of the Backpropagation Neural Network. In: Neural networks for perception. Elsevier; 1992. pp. 65-
                    93.  DOI
               282.      Red’ko, V. G.; Prokhorov, D. V.; Burtsev, M. S. Theory of functional systems, adaptive critics and neural networks. In: 2004 IEEE
                    International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541); Budapest, Hungary. IEEE; 2004. pp. 1787-92.  DOI
               283.      Slotine, J. E.; Sanner, R. M. Neural networks for adaptive control and recursive identification: a theoretical framework. In:
                    Trentelman HL, Willems JC, editors. Essays on control. Boston: Birkhäuser; 1993. pp. 381-436.  DOI
               284.      Low, J. H.; Khin, P. M.; Han, Q. Q.; et al. Sensorized reconfigurable soft robotic gripper system for automated food handling. IEEE/
                    ASME. Trans. Mechatronics. 2022, 27, 3232-43.  DOI
               285.      Somnox. Somnox, Breathe, relax, sleep. Available from: https://somnox.com/. [Last accessed on 14 Jan 2025].
               286.      Awad, L. N.; Esquenazi, A.; Francisco, G. E.; Nolan, K. J.; Jayaraman, A. The ReWalk ReStore™ soft robotic exosuit: a multi-site
                    clinical trial of the safety, reliability, and feasibility of exosuit-augmented post-stroke gait rehabilitation. J. Neuroeng. Rehabil. 2020,
                    17, 80.  DOI  PubMed  PMC
               287.      Squishy Robotics. Life-saving, Cost-saving information in real time. Available from: https://squishy-robotics.com/. [Last accessed on
                    14 Jan 2025].
               288.      Mitchell, S. K.; Wang, X.; Acome, E.; et al. An easy-to-implement toolkit to create versatile and high-performance HASEL actuators
                    for untethered soft robots. Adv. Sci. 2019, 6, 1900178.  DOI  PubMed  PMC
               289.      Amend, J.; Cheng, N.; Fakhouri, S.; Culley, B. Soft robotics commercialization: jamming grippers from research to product. Soft.
                    Robot. 2016, 3, 213-22.  DOI  PubMed  PMC
               290.      Olkkola, L. S.; Townsend, V. M.; Quigley, C. Defining potential market growth of innovative applications for military and
                    commercial use in soft robotics. Defense Technical Information Center. 2022. Available from: https://apps.dtic.mil/sti/citations/
                    AD1160075. [Last accessed on 14 Jan 2025].
               291.      Wang, Y.; Wang, G.; Ge, W.; Duan, J.; Chen, Z.; Wen, L. Perceived safety assessment of interactive motions in human-soft robot
                    interaction. Biomimetics 2024, 9, 58.  DOI  PubMed  PMC
               292.      Sundaravadivel, P.; Ghosh, P. K.; Suwal, B. IoT-enabled soft robotics for electrical engineers. In: Proceedings of the Great Lakes
                    Symposium on VLSI 2022. Irvine CA USA: ACM; 2022. pp. 329-32.  DOI
   108   109   110   111   112   113   114   115   116   117   118