Abstract
In this paper, we present modeling and control of a novel arc-shaped Shape Memory Alloy (SMA) actuator. The arc-shaped SMA actuator is developed to provide rotational motion with compliance for biologically inspired robots. We modeled the dynamics of proposed SMA actuator. Based on the dynamics structure, we have developed proportional integral derivative (PID), backstepping and integral backstepping controllers. We have tested experimentally these controllers with input, output and input-output combined disturbances. Based on tracking error, peak error, settling time and control effort, integral backstepping controller is the most suited controller for the actuator.
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Acknowledgements
This work is supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea government (MSIT) (No. 2017R1A2B4008056). Also, the first author is funded by the Korea Research Fellowship (KRF) program by the National Research Foundation (NRF) with KRF Grant (2019H1D3A1A01102998).
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Appendices
Appendix A Backstepping control—proof
System dynamics of the arc-shaped SMA actuator is defined in Eqs. (17), (18) and (20). Based on this system dynamics, we can define
where \(x_{1_d}\) is the desired trajectory. To achieve convergence for \(e_1\), we can define Lyapunov function as follows
To realize \(\dot{V}_1<0\), we choose virtual control as
To achieve \(x_2\rightarrow x_{2_d}\), we define new error
so,
Solving Eqs. (40), (41) and (43) simultaneously, we get
Next, we obtain
Now, to achieve convergence of \(e_2\), we define Lyapunov function as
Simplifying Eq. (49) using (46) and (47), we get
To realize \(\dot{V}_2 < 0\), we define virtual law as
where \(\vert f_1(x)-\hat{f}_1(x) \vert = \vert \tilde{f}_1(x) \vert \le F_1\) and \(g_1 = [1+\Delta _1]\hat{g}_1\), \(\vert \Delta _1\vert \le D_1\), \(0<D_1<1\). Next, to achieve \(x_3\rightarrow x_{3_d}\), we define new error
so,
Using Eqs. (53) and (51), we can simplify (47)
as \(g_1 = [1+\Delta _1]\hat{g}_1\), \(\vert \Delta _1\vert \le D_1\), \(0<D_1<1\) and \(\vert f_1(x)-\hat{f}_1(x) \vert = \vert \tilde{f}_1(x) \vert \le F_1\), therefore, we can write Eq. (54) as
Using Eq. (56), we can simplify (50)
where
Next, to achieve convergence of \(e_3\), we define Lyapunov function as
where
To achieve \(\dot{V}_3 < 0\), we define control law as
Using Eq. (63), we can simplify (62) as
As \(g_2 = [1+\Delta _2]\hat{g}_2\), \(\vert \Delta _2\vert \le D_2\), \(0<D_2<1\) and \(\vert f_2(x)-\hat{f}_2(x) \vert = \vert \tilde{f}_2(x) \vert \le F_2\), therefore, we can write Eq. (64)
Now, using Eq. (66), we can simplify (60) as
where
As, \(g_1 \le (1+D_1)\hat{g}_1\), we can write Eq. (67) as
where
Therefore, we can write Eq. (69) as
So, to achieve \(\dot{V}_3 < 0\), we have to
Therefore, if we shall choose \(k_1, k_2\) and \(k_3\) greater than zero and large enough to satisfy Eq. (72), we shall always have \(\dot{V}_3 < 0\).
Appendix B Integral backstepping control—proof
Just like backstepping controller, procedure to derive integral backstepping controller is also same. It starts with system dynamics of the arc-shaped SMA actuator as defined in Eqs. (17), (18) and (20). Based on the dynamics, we define
where \(x_{1_d}\) is the desired trajectory. To achieve convergence for \(e_1\), we can define Lyapunov function as follows
To realize \(\dot{V}_1<0\), we choose virtual control as
To achieve \(x_2\rightarrow x_{2_d}\), we define new error
so,
Solving Eqs. (77), (78) and (80) simultaneously, we get
Next, we obtain
Now, to achieve convergence of \(e_2\), we define Lyapunov function as
Simplifying Eq. (86) using (83) and (84), we get
To realize \(\dot{V}_2 < 0\), we define virtual law as
This Eq. (88) is the brings major difference between backstepping controller and integral backstepping controller. We can compare Eq. (51) for backstepping controller with Eq. (88). Next, we define error
as \(g_1 = [1+\Delta _1]\hat{g}_1\), \(\vert \Delta _1\vert \le D_1\), \(0<D_1<1\) and \(\vert f_1(x)-\hat{f}_1(x) \vert = \vert \tilde{f}_1(x) \vert \le F_1\), therefore, we can write Eq. (92) as
Using Eq. (95), we can simplify (87) as
where
Next, to achieve convergence of \(e_3\), we define Lyapunov function as
Here,
Then, we define
Using Eq. (103), we can simplify (102) as
as \(g_2 = [1+\Delta _2]\hat{g}_2\), \(\vert \Delta _2\vert \le D_2\), \(0<D_2<1\) and \(\vert f_2(x)-\hat{f}_2(x) \vert = \vert \tilde{f}_2(x) \vert \le F_2\), therefore, we can write Eq. (104) as
Using Eq. (105), we can simplify (100) as
where
SAs, \(g_1 \le (1+D_1)\hat{g}_1\), we can write Eq. (108) as
where
Therefore, we can write Eq. (110) as
So, to achieve \(\dot{V}_3 < 0\), we have to
Therefore, if we shall choose \(k_1, k_2\) and \(k_3\) greater than zero and large enough to satisfy Eq. (113), we shall always have \(\dot{V}_3 < 0\).
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Khan, A.M., Shin, B., Usman, M. et al. Backstepping control of novel arc-shaped SMA actuator. Microsyst Technol 28, 2191–2202 (2022). https://doi.org/10.1007/s00542-022-05250-7
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DOI: https://doi.org/10.1007/s00542-022-05250-7