Page 112 - Read Online

Kulkarni et al. Soft Sci. 2025, 5, 12 https://dx.doi.org/10.20517/ss.2023.51 Page 33 of 35

233. Jumet, B.; Bell, M. D.; Sanchez, V.; Preston, D. J. A data-driven review of soft robotics. Adv. Intell. Syst. 2022, 4, 2100163. DOI
234. Liu, B.; Sha, L.; Huang, K.; Zhang, W.; Yang, H. A topology optimization method for collaborative robot lightweight design based
on orthogonal experiment and its applications. Int. J. Adv. Robot. Syst. 2022, 19, 17298814211056143. DOI
235. Zheng, Y.; Cao, L.; Qian, Z.; Chen, A.; Zhang, W. Topology optimization of a fully compliant prosthetic finger: design and testing.
In: 2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob); Singapore, Singapore. IEEE;
2016. pp. 1029-34. DOI
236. Li, D.; Chen, S.; Song, Z.; Liang, J.; Zhu, X.; Chen, F. Tailoring the in-plane and out-of-plane stiffness of soft fingers by
endoskeleton topology optimization for stable grasping. Sci. China. Technol. Sci. 2023, 66, 3080-9. DOI
237. Sun, Y.; Zong, C.; Pancheri, F.; Chen, T.; Lueth, T. C. Design of topology optimized compliant legs for bio-inspired quadruped
robots. Sci. Rep. 2023, 13, 4875. DOI PubMed PMC
238. Al-Tamimi, A. A.; Peach, C.; Fernandes, P. R.; Cseke, A.; Bartolo, P. J. Topology optimization to reduce the stress shielding effect
for orthopedic applications. Procedia. CIRP. 2017, 65, 202-6. DOI
239. Sato, Y.; Kobayashi, H.; Yuhn, C.; Kawamoto, A.; Nomura, T.; Kikuchi, N. Topology optimization of locomoting soft bodies using
material point method. Struct. Multidisc. Optim. 2023, 66, 3502. DOI
240. Gillespie, M. T.; Best, C. M.; Townsend, E. C.; Wingate, D.; Killpack, M. D. Learning nonlinear dynamic models of soft robots for
model predictive control with neural networks. In: 2018 IEEE International Conference on Soft Robotics (RoboSoft); Livorno, Italy.
IEEE; 2018. pp. 39-45. DOI
241. Goodwin, G. C.; Graebe, S. F.; Salgado, M. E. Control system design. Available from: https://www.academia.edu/23184065/
CONTROL_SYSTEM_DESIGN. [Last accessed on 13 Jan 2025].
242. Runciman, M.; Avery, J.; Darzi, A.; Mylonas, G. Open loop position control of soft hydraulic actuators for minimally invasive
surgery. Appl. Sci. 2021, 11, 7391. DOI
243. Cianchetti, M.; Laschi, C.; Menciassi, A.; Dario, P. Biomedical applications of soft robotics. Nat. Rev. Mater. 2018, 3, 143-53. DOI
244. Beatty, R.; Mendez, K. L.; Schreiber, L. H. J.; et al. Soft robot-mediated autonomous adaptation to fibrotic capsule formation for
improved drug delivery. Sci. Robot. 2023, 8, eabq4821. DOI
245. Grube, M.; Wieck, J. C.; Seifried, R. Comparison of modern control methods for soft robots. Sensors 2022, 22, 9464. DOI PubMed
PMC
246. Tonkens, S.; Lorenzetti, J.; Pavone, M. Soft robot optimal control via reduced order finite element models. In: 2021 IEEE
International Conference on Robotics and Automation (ICRA); Xi’an, China. IEEE; 2021. pp. 12010-6. DOI
247. Ding, L.; Niu, L.; Su, Y.; et al. Dynamic finite element modeling and simulation of soft robots. Chin. J. Mech. Eng. 2022, 35, 701.
DOI
248. Youssef, S. M.; Soliman, M.; Saleh, M. A.; Mousa, M. A.; Elsamanty, M.; Radwan, A. G. Underwater soft robotics: a review of
bioinspiration in design, actuation, modeling, and control. Micromachines 2022, 13, 110. DOI PubMed PMC
249. Vikas, V.; Grover, P.; Trimmer, B. Model-free control framework for multi-limb soft robots. In: 2015 IEEE/RSJ International
Conference on Intelligent Robots and Systems (IROS); Hamburg, Germany. IEEE; 2015. pp. 1111-6. DOI
250. Li, Y.; Ang, K. H.; Chong, G. C. Y. PID control system analysis and design. IEEE. Control. Syst. May. 2006, 26, 32-41. DOI
251. Isidro, I. A.; Pais, D. A. M.; Alves, P. M.; Carrondo, M. J. T. 2.64 - Online control strategies. In: Comprehensive Biotechnology.
Elsevier; 2019. pp. 943-51. DOI
252. Landau, I. D.; Lozano, R.; M’Saad, M.; Karimi, A. Adaptive control: algorithms, analysis and applications. 2nd edition. London:
Springer; 2011. DOI
253. Zhang, D.; Wei, B. A review on model reference adaptive control of robotic manipulators. Annu. Rev. Control. 2017, 43, 188-98.
DOI
254. Singh, R.; Bhushan, B. Reinforcement learning-based model-free controller for feedback stabilization of robotic systems. IEEE.
Trans. Neural. Netw. Learn. Syst. 2023, 34, 7059-73. DOI
255. Lewis, F. L.; Vrabie, D. Reinforcement learning and adaptive dynamic programming for feedback control. IEEE. Circuits. Syst. Mag.
2009, 9, 32-50. DOI
256. Li, G.; Shintake, J.; Hayashibe, M. Deep reinforcement learning framework for underwater locomotion of soft robot. In: 2021 IEEE
International Conference on Robotics and Automation (ICRA); Xi’an, China. IEEE; 2021. pp. 12033-9. DOI
257. Thuruthel, T. G.; Falotico, E.; Manti, M.; Laschi, C. Stable open loop control of soft robotic manipulators. IEEE. Robot. Autom. Lett.
2018, 3, 1292-8. DOI
258. Fei, Y.; Xu, H. Modeling and motion control of a soft robot. IEEE. Trans. Ind. Electron. 2017, 64, 1737-42. DOI
259. Precup, R.; Radac, M.; Roman, R.; Petriu, E. M. Model-free sliding mode control of nonlinear systems: algorithms and experiments.
Inform. Sci. 2017, 381, 176-92. DOI
260. Vikas, V.; Cohen, E.; Grassi, R.; Sozer, C.; Trimmer, B. Design and locomotion control of a soft robot using friction manipulation
and motor–tendon actuation. IEEE. Trans. Robot. 2016, 32, 949-59. DOI
261. Li, M.; Kang, R.; Branson, D. T.; Dai, J. S. Model-free control for continuum robots based on an adaptive Kalman filter. IEEE/
ASME. Trans. Mechatron. 2018, 23, 286-97. DOI
262. Patterson, Z. J.; Santina, C. D. R. D.; Modeling, In: 2024 IEEE International Conference on Robotics and Automation (ICRA);
Yokohama, Japan. IEEE; 2024, pp. 14995-5001. DOI
263. Zheng, Z.; Kumar, P.; Chen, Y.; et al. Model-based control of planar piezoelectric inchworm soft robot for crawling in constrained