# Model-Free Tracking Control with Prescribed Performance for a Shape Memory Alloy-Based Robotic Hand
## 6 Conclusions  
Conventional robotic hands, which involve rigid components, such as linkages, gears and motors, have a large volume and heavy mass. Therefore, conventional robotic hands are unsafe and exhibit poor adaptability when they interact with humans or the surrounding environment. SMA as a novel smart material with the characteristics of a simple structure, small volume and light weight can be easily integrated into a robotic hand without requiring a large amount of space and complex gearing mechanisms. Moreover, the inherently high energy density makes SMA a natural choice for robotic hands. In this paper, the SMA-based robotic hand can be applied in a wide range of fields. For example, the robotic hand can serve people and help to grasp objects for a safe interaction. Moreover, in the field of agriculture, the robotic hand can pick the fruits complianly. However, SMA has the characteristic of nonlinearity, due to the \"shape memory effect\" of SMA between a low-temperature martensite phase and a high temperature austenite phase, which leads to the complexity and difficulty of controlling the SMA-based robotic hand. Therefore, this paper focuses on the control method of the SMA-based robotic hand. A model-free  
Figure 11: Tracking error under the time-varying signal. Figure 10: Tracking results under the time-varying signal. adaptive control method with a prescribed performance is proposed for the SMA-based robotic hand. Combined with the Taylor series and Differential mean value theorem, a novel dynamic linearization technique is used to describe the system of the robotic hand. By applying PPC, the constrained tracking error can be converted into an unconstrained form to ensure the tracking error within a prescribed zone. Finally, experiments on the SMA-based robotic hand are conducted to verify the performance of the proposed control method. The results indicate that the proposed control method can ensure that the robotic hand tracks the reference signals and limits the tracking error in a predefined boundary. Literature search, software, data curation and writing--original draft, L.H. ; writing--review and editing, J.X. and W.L. All authors have read and agreed to the published version of the manuscript. This work was supported by National Natural Science Foundation of China under grant number 62073063. Not applicable. Data Availability StatementData is contained within the article. The authors declare no conflict of interest. * _[PERSON] and [PERSON] (2014)_ [PERSON]; [PERSON] Indirect Intelligent Sliding Mode Control of Antagonistic Shape Memory Alloy Actuators Using Hysteretic Recurrent Neural Networks. _IEEE Trans. Control Syst. Technol._**2014**, _22_, 921-929. [CrossRef]
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