In this paper, a novel data-driven model-free adaptive fractional-order sliding mode controller with prescribed performance is proposed for the shape memory alloy (SMA) actuator. Due to the strong asymmetric saturated hysteresis nonlinear characteristics of the SMA actuators, it is not easy to establish an accurate model and develop an effective controller. Therefore, we present a controller without using the model of the SMA actuators. In other words, the proposed controller depends merely on the input/output (I/O) data of the SMA actuators. To obtain the reasonable compensation for hysteresis, enhance the noise robustness of the controller, and reduce the chattering, a fractional-order sliding mode controller with memory characteristics is employed to improve the performance of the controller. In addition, the prescribed performance control (PPC) strategy is introduced in our work to guarantee the tracking errors converge to a sufficiently small boundary and the convergence rate is not less than a predetermined value which are significant and considerable in practical engineering applications of the SMA actuator. Finally, experiments are carried out, and results reveal the effectiveness and success of the proposed controller. Comparisons with the classical Proportional Integral Differential (PID), model-free adaptive control (MFAC), and model-free adaptive sliding mode control (MFAC-SMC) are also performed.
SimonsMFDigumartiKMLeNH, et al.Bionic glove: a soft smart wearable sensory feedback device for upper limb robotic prostheses. IEEE Robot Automat Lett2021; 6(2): 3311–3316.
2.
ShaoSSunBDingQ, et al.Design, modeling, and control of a compact SMA-actuated MR-conditional steerable neurosurgical robot. IEEE Robot Automat Lett2020; 5(2): 1381–1388.
3.
BenaRNguyenXTCalderonAA, et al.Smarti: a 60-mg steerable robot driven by high-frequency shape memory alloy actuation. IEEE Robot Automat Lett2021; 4(3): 1.
4.
LiJPiY. Fuzzy time delay algorithms for position control of soft robot actuated by shape memory alloy. Int J Control Automat Syst2021; 19(1): 2203–2212.
5.
AsamuraKNagasawaS. MEMS fabrication of compliant sheet for micro hexapod robots. Jpn J Appl Phys2020; 59(SI): SIIL03.
6.
KenSKeiIYukiI, et al.Miniaturized rotary actuators using shape memory alloy for insect-type MEMS microrobot. Micromachines2016; 7(4): 58.
7.
JaniJMLearyMSubicA, et al.A review of shape memory alloy research, applications and opportunities. Mater Des (1980-2015)2014; 56(1): 1078–1113.
8.
LiXZhangBZhangD, et al.Disturbance compensation-based output feedback adaptive control for shape memory alloy actuator system. Int J Adv Robot Syst2021; 18(1): 1–13.
9.
ZhangDZhaoXHanJ, et al.Active modeling and control for shape memory alloy actuators. IEEE Access2019; 7(1): 162549–162558.
10.
SuzukiYKagawaY. Dynamic tracking control of an SMA wire actuator based on model matching. Sensors Actuators A: Phys2019; 292(1): 129–136.
11.
LeeSHKimSW. Improved position control of shape memory alloy actuator using the self-sensing model. Sensors Actuators A: Phys2019; 297(1): 111529.
12.
TrongTNKwanAK. A RBF neural network sliding mode controller for SMA actuator. Int J Control Automat Syst2010; 8(6): 1296–1305.
13.
TaiNTAhnKK. Output feedback direct adaptive controller for a SMA actuator with a Kalman’s filter. IEEE Trans Control Syst Technol2012; 20(4): 1081–1091.
14.
PanYGuoZLiX, et al.Output-feedback adaptive neural control of a compliant differential SMA actuator. IEEE Trans Control Syst Technol2017; 25(6): 2202–2210.
15.
ZhangBZhaoXLiX, et al.Robust indirect adaptive control for a class of nonlinear systems and its application to shape memory alloy actuators. IEEE Access2018; 6(1): 35809–35823.
16.
LiJTianH. Position control of SMA actuator based on inverse empirical model and SMA-RBF compensation. Mech Syst Signal Process2018; 108(1): 203–215.
17.
ZhangJYinYZhuJ. Sigmoid-based hysteresis modeling and high-speed tracking control of SMA-artificial muscle. Sensors Actuators A: Phys2013; 201(1): 264–273.
18.
SayyaadiHZakerzadehMR. Position control of shape memory alloy actuator based on the generalized Prandtl–Ishlinskii inverse model. Mechatronics2012; 22(7): 945–957.
19.
HoseiniSFMirMohammadSadeghiSAFathiA, et al.Adaptive predictive control of a novel shape memory alloy rod actuator. Proc Inst Mech Eng Part J Syst Control Eng2021; 235(3): 291–301.
20.
ShakibaSYousefi-KomaAAyatiM. Tracking control of an SMA-driven actuator with rate-dependent behavior using an inverse model of hysteresis. J Braz Soc Mech Sci Eng2020; 42(8): 418.
21.
AlfonsoGERobertoCSIonLE, et al.Neural network direct control with online learning for shape memory alloy manipulators. Sensors2019; 19(11): 2576.
22.
LiYLiuKZhouX, et al.Design and control of a novel rotary shape memory alloy actuator for two degree of freedom underwater vectored thruster. Smart Mater Struct2020; 30(1): 015012.
23.
AliHFKhanAMBaekH, et al.Modeling and control of a finger-like mechanism using bending shape memory alloys. Microsystem Tech2021; 1(3): 1–12.
24.
VillosladaÁEscuderoNMartínF, et al.Position control of a shape memory alloy actuator using a four-term bilinear pid controller. Sensors Actuators A: Phys2015; 236(1): 257–272.
25.
BechlioulisCPRovithakisGA. Robust adaptive control of feedback linearizable MIMO nonlinear systems with prescribed performance. IEEE Trans Automat Control2008; 53(9): 2090–2099.
26.
HouZ. The parameter identification, adaptive control and model free learning adaptive control for nonlinear systems. Shenyang: North-eastern University, 1994.
27.
HouZJinS. A novel data-driven control approach for a class of discrete-time nonlinear systems. IEEE Trans Control Syst Technol2011; 19(6): 1549–1558.
28.
HouZJinS. Data-driven model-free adaptive control for a class of MIMO nonlinear discrete-time systems. IEEE Trans Neural Networks2011; 22(12): 2173–2188.
29.
HouZXiongS. On model-free adaptive control and its stability analysis. IEEE Trans Automat Control2019; 64(11): 4555–4569.
BuXHouZZhangH. Data-driven multiagent systems consensus tracking using model free adaptive control. IEEE Trans Neural Networks Learn Syst2018; 29(5): 1514–1524.
32.
XuDShiYJiZ. Model-free adaptive discrete-time integral sliding-mode-constrained-control for autonomous 4WMV parking systems. IEEE Trans Ind Electro2018; 65(1): 834–843.
33.
LiuSHouZTianT, et al.A novel dual successive projection-based model-free adaptive control method and application to an autonomous car. IEEE Trans Neural Networks Learn Syst2019; 30(11): 3444–3457.
34.
ZhangYZhangQZhangJ, et al.Sliding mode control for fuzzy singular systems with time delay based on vector integral sliding mode surface. IEEE Trans Fuzzy Syst2020; 28(4): 768–782.
35.
LiuDYangGH. Performance-based data-driven model-free adaptive sliding mode control for a class of discrete-time nonlinear processes. J Process Control2018; 68(1): 186–194.
36.
LiuDYangG. Data-driven adaptive sliding mode control of nonlinear discrete-time systems with prescribed performance. IEEE Trans Syst Man, Cybernetics: Syst2019; 49(12): 2598–2604.
37.
LiuDYangG. Prescribed performance model-free adaptive integral sliding mode control for discrete-time nonlinear systems. IEEE Trans Neural Net Learn Syst2019; 30(7): 2222–2230.
38.
ZhangWXuDJiangB, et al.Prescribed performance based model-free adaptive sliding mode constrained control for a class of nonlinear systems. Inf Sci2021; 544(1): 97–116.
39.
WangYGuLXuY, et al.Practical tracking control of robot manipulators with continuous fractional-order nonsingular terminal sliding mode. IEEE Trans Ind Electro2016; 63(10): 6194–6204.
40.
SunGWuLKuangZ, et al.Practical tracking control of linear motor via fractional-order sliding mode. Automatica2018; 94(1): 221–235.
41.
WangJWenYGouY, et al.Fractional-order gradient descent learning of bp neural networks with caputo derivative. Neural Networks2017; 89(4): 19–30.
42.
LiSDuHYuX. Discrete-time terminal sliding mode control systems based on euler’s discretization. IEEE Trans Automat Control2014; 59(2): 546–552.
43.
SunGMaZYuJ. Discrete-time fractional order terminal sliding mode tracking control for linear motor. IEEE Trans Ind Electro2018; 65(4): 3386–3394.
