An Hammerstein Model Based Control Method for Shape Memory Alloy Actuators

Shape memory alloy (SMA) is a promising smart metallic material, which has the ability to recover its shape when heated. This characteristic enables SMA to serve as an alternative to replace conventional actuators. However, there also exist hysteresis nonlinearities and parameter uncertainties, which make SMA actuators difficult to model and control. This paper develops a gray-box identification and control approach for SMA actuators. To copy with hysteresis nonlinearities, this plant is described as an Hammerstein model, which is a cascade connection of a nonlinear function followed by a linear sub-system. Then the parameter adaptation is performed based on a robust recursive estimator, and the control law compensates the modeling error through incorporating the unmodeled dynamics estimation. The system stability is ensured and the experimental results verify the effectiveness of the proposed method.

[1]  Hashem Ashrafiuon,et al.  Sliding Mode Control of Mechanical Systems Actuated by Shape Memory Alloy , 2009 .

[2]  Zhizhong Mao,et al.  Adaptive control of stochastic Hammerstein–Wiener nonlinear systems with measurement noise , 2016, Int. J. Syst. Sci..

[3]  Andrew H. Jazwinski,et al.  Adaptive filtering , 1969, Autom..

[4]  Nguyen Trong Tai,et al.  A RBF neural network sliding mode controller for SMA actuator , 2010 .

[5]  Graham C. Goodwin,et al.  Adaptive filtering prediction and control , 1984 .

[6]  M. Omizo,et al.  Modeling , 1983, Encyclopedic Dictionary of Archaeology.

[7]  E. Bai,et al.  Block Oriented Nonlinear System Identification , 2010 .

[8]  Jianda Han,et al.  Modeling and control of shape memory alloy actuator using feedback linearization , 2017, 2017 36th Chinese Control Conference (CCC).

[9]  Wen-Xiao Zhao,et al.  Adaptive tracking and recursive identification for Hammerstein systems , 2009, Autom..

[10]  Eduardo A. Tannuri,et al.  Modeling, control and experimental validation of a novel actuator based on shape memory alloys , 2009 .

[11]  Haoyong Yu,et al.  Output-Feedback Adaptive Neural Control of a Compliant Differential SMA Actuator , 2017, IEEE Transactions on Control Systems Technology.

[12]  Gregory D. Buckner,et al.  Indirect Intelligent Sliding Mode Control of Antagonistic Shape Memory Alloy Actuators Using Hysteretic Recurrent Neural Networks , 2014, IEEE Transactions on Control Systems Technology.

[13]  Jin‐Xi Zhang,et al.  Adaptive weighted suboptimal control for linear dynamic systems having a polynomial input , 1987 .

[14]  M. Ahmadian,et al.  A Temperature-based Controller for a Shape Memory Alloy Actuator , 2005 .

[15]  Ansgar Trächtler,et al.  Model-based precision position and force control of SMA actuators with a clamping application , 2017 .