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First, a mechanism model was formulated on the basis of the architecture of the SMA actuator. To improve the system\u2019s dynamic tracking error performance, a novel performance function based on the prescribed performance control method was introduced. Concurrently, the convergence of the transformed error function is ensured by combining the integral sliding mode backstepping controller, guaranteeing that the tracking error converges to the predefined envelope range of the performance function. Moreover, an extended state observer (ESO) is utilized to observe and compensate for uncertainties and disturbances arising from system parameter fluctuations or modeling inaccuracies. For SMA material parameters that cannot be measured directly, an experimental setup was constructed to identify and validate these parameters. In conclusion, numerical simulations are conducted to compare the proposed control method with sliding mode controllers (SMCs) and proportional\u2013integral (PI) controllers, assessing the dynamic behavior and response speed of the SMA actuator under various target angle inputs. The findings revealed that the proposed control strategy achieves the fastest convergence times and the lowest root mean square errors (RMSEs) across three different target angle tracking scenarios, with values of 1.003e-3\u00b0, 5.244e-4\u00b0, and 3.367e-5\u00b0, respectively. These results represent improvements of 54.11% and 55.84% over those of the SMC and PI controllers, respectively. Consequently, the proposed control methodology demonstrates rapid responsiveness, high control precision, and robust performance.<\/jats:p>","DOI":"10.1177\/01423312251322218","type":"journal-article","created":{"date-parts":[[2025,6,9]],"date-time":"2025-06-09T09:21:32Z","timestamp":1749460892000},"page":"1627-1640","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Integral sliding mode backstepping control strategy for shape memory alloy actuator based on prescribed performance control"],"prefix":"10.1177","volume":"48","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-1114-0967","authenticated-orcid":false,"given":"Xiangyu","family":"Guan","sequence":"first","affiliation":[{"name":"College of Information Engineering, Shenyang University of Chemical Technology, China"},{"name":"State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinguo","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuangqing","family":"Yu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, China"},{"name":"University of Chinese Academy of Sciences, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guojie","family":"Li","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Shenyang University of Chemical Technology, China"},{"name":"State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qinghui","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Shenyang University of Chemical Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,6,9]]},"reference":[{"key":"e_1_3_3_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijsolstr.2021.111264"},{"key":"e_1_3_3_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2008.929402"},{"key":"e_1_3_3_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2011.2157399"},{"key":"e_1_3_3_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.mtcomm.2022.103728"},{"key":"e_1_3_3_6_1","doi-asserted-by":"publisher","DOI":"10.1088\/0964-1726\/4\/4\/005"},{"key":"e_1_3_3_7_1","doi-asserted-by":"publisher","DOI":"10.1088\/0964-1726\/11\/3\/312"},{"key":"e_1_3_3_8_1","doi-asserted-by":"publisher","DOI":"10.1177\/1045389X9300400213"},{"issue":"2","key":"e_1_3_3_9_1","first-page":"273","article-title":"Prescribed performance control of underactuated surface vessels\u2019 trajectory using a neural network and integral time- delay sliding mode","volume":"59","author":"Chen Y","year":"2023","unstructured":"Chen Y, Chen H (2023) Prescribed performance control of underactuated surface vessels\u2019 trajectory using a neural network and integral time- delay sliding mode. 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