Adaptive control of a shape memory alloy actuator using neural-network feedforward and RISE feedback

A This paper presents a position tracking control system for a shape memory alloy (SMA) actuator using neural network (NN) feedforward and robust integral of signum of error (RISE) feedback. Nonlinear control of SMA actuators is difficult due to model uncertainties and unknown disturbances. Discontinuous control techniques such as sliding mode control have conventionally been used to achieve asymptotic tracking in the presence of model uncertainties. However, such discontinuous controllers usually result in increased power loss due to high frequency switching. With the recent development of the continuous RISE feedback control, semi-global asymptotic tracking can be achieved. Furthermore, the NN-RISE control leads to better tracking performance and lower power losses caused by input signal switching/chattering when compared to discontinuous controllers. In order to apply the NN-RISE, a state-space model of the SMA actuator is derived, which has been overlooked in many previous works, using Taylor series expansion and exploiting the nature of SMA dynamics. Experimental results show that the proposed control system works well even in the absence of an accurate model of the SMA actuator.

[1]  Dimitris C. Lagoudas,et al.  On the role of thermoelectric heat transfer in the design of SMA actuators: theoretical modeling and experiment , 1995 .

[2]  Warren E. Dixon,et al.  Asymptotic Tracking for Aircraft via Robust and Adaptive Dynamic Inversion Methods , 2010, IEEE Transactions on Control Systems Technology.

[3]  James G. Boyd,et al.  A thermodynamical constitutive model for shape memory materials. Part II. The SMA composite material , 1996 .

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

[5]  Mehdi Ahmadian,et al.  An enhanced SMA phenomenological model: I. The shortcomings of the existing models , 2005 .

[6]  Jordi Ortín,et al.  Preisach modeling of hysteresis for a pseudoelastic Cu-Zn-Al single crystal , 1992 .

[7]  Frank L. Lewis,et al.  Deadzone compensation in motion control systems using neural networks , 2000, IEEE Trans. Autom. Control..

[8]  David W. L. Wang,et al.  Modeling and L2-stability of a shape memory alloy position control system , 1998, IEEE Trans. Control. Syst. Technol..

[9]  Constantinos Mavroidis,et al.  B-Spline Based Adaptive Control of Shape Memory Alloy Actuated Robotic Systems , 2002 .

[10]  J. K. Knowles,et al.  Kinetic relations and the propagation of phase boundaries in solids , 1991 .

[11]  H. Jin Kim,et al.  Autonomous Flight of the Rotorcraft-Based UAV Using RISE Feedback and NN Feedforward Terms , 2012, IEEE Transactions on Control Systems Technology.

[12]  L. Brinson.  One-Dimensional Constitutive Behavior of Shape Memory Alloys: Thermomechanical Derivation with Non-Constant Material Functions and Redefined Martensite Internal Variable , 1993 .

[13]  M. Sreekumar,et al.  Recent advances in nonlinear control technologies for shape memory alloy actuators , 2007 .

[14]  F. Ghorbel,et al.  Differential hysteresis modeling of a shape memory alloy wire actuator , 2005, IEEE/ASME Transactions on Mechatronics.

[15]  Yves Bellouard,et al.  Shape memory alloys for microsystems: A review from a material research perspective , 2008 .

[16]  C. Lexcellent,et al.  A general macroscopic description of the thermomechanical behavior of shape memory alloys , 1996 .

[17]  Jordi Ortín,et al.  Thermodynamics of Thermoelastic Martensitic Transformations , 1989 .

[18]  Craig A. Rogers,et al.  One-Dimensional Thermomechanical Constitutive Relations for Shape Memory Materials , 1990 .

[19]  Seung-Bok Choi.  Position control of a single-link mechanism activated by shape memory alloy springs: experimental results , 2006 .

[20]  Warren E. Dixon,et al.  Asymptotic Tracking for Uncertain Dynamic Systems Via a Multilayer Neural Network Feedforward and RISE Feedback Control Structure , 2008, IEEE Transactions on Automatic Control.

[21]  Kenton Conrad Kirkpatrick,et al.  Reinforcement Learning for Characterizing Hysteresis Behavior of Shape Memory Alloys , 2007, J. Aerosp. Comput. Inf. Commun..

[22]  S. Hirose,et al.  Mathematical model and experimental verification of shape memory alloy for designing micro actuator , 1991, [1991] Proceedings. IEEE Micro Electro Mechanical Systems.

[23]  K. Tanaka.  A THERMOMECHANICAL SKETCH OF SHAPE MEMORY EFFECT: ONE-DIMENSIONAL TENSILE BEHAVIOR , 1986 .

[24]  Mehdi Ahmadian,et al.  Nonlinear Stress-Based Control of a Rotary SMA-Actuated Manipulator , 2004 .

[25]  Gangbing Song,et al.  Robust control of a shape memory alloy wire actuated flap , 2007 .

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

[27]  D. Lagoudas,et al.  A thermodynamical constitutive model for shape memory materials. Part I. The monolithic shape memory alloy , 1996 .

[28]  Qingping Sun,et al.  Micromechanics modelling for the constitutive behavior of polycrystalline shape memory alloys. I: Derivation of general relations , 1993 .

[29]  Nguyen Trong Tai,et al.  Output Feedback Direct Adaptive Controller for a SMA Actuator With a Kalman Filter , 2012, IEEE Transactions on Control Systems Technology.

[30]  Wei Lin,et al.  Inverse hysteresis control for shape memory alloy micro-actuators based flap positioning system , 2010, 49th IEEE Conference on Decision and Control (CDC).

[31]  M. Moallem,et al.  Tracking Control of an Antagonistic Shape Memory Alloy Actuator Pair , 2009, IEEE Transactions on Control Systems Technology.

[32]  Hyo Jik Lee,et al.  Time delay control of a shape memory alloy actuator , 2004 .

[33]  Gangbing Song,et al.  Position control of shape memory alloy actuators with internal electrical resistance feedback using neural networks , 2004 .

[34]  Jian Chen,et al.  A continuous asymptotic tracking control strategy for uncertain nonlinear systems , 2004, IEEE Transactions on Automatic Control.