Sage Journals: Discover world-class research

Abstract

This article tackles the problem of online state estimation and parameter identification for SMA-actuated flexible structures intended to be precisely controlled within a multivariable adaptive model-based framework. Using a joint state-parameter formulation, a non-linear recursive scheme has been developed that is capable of simultaneously producing online estimates of the states and the uncertain parameters from the noisy measurements at hand. The scheme employs an embedded model derived from reduced-order finite element modeling of the structure and phenomenological SMA modeling to incorporate the whole available knowledge about the nature of the system (non-linearity and hysteresis) and its uncertainties (uncertain parameters and stochastic noises). The unscented Kalman filtering algorithm is utilized to improve accuracy and simplify the implementation. The numerical functionality of the proposed scheme is validated via a simulation example; while its practical versatility is challenged by an experimental case study involving the SMA-actuated flexible tail of a bio-inspired ornithopter. Results demonstrate successfulness of the scheme for online estimation and identification purposes, as well as promising applicability to adaptive nonlinear model-based control.

Keywords

shape memory alloy state estimation parameter identification non-linearity and hysteresis unscented Kalman filtering adaptive model-based control.

Get full access to this article

View all access options for this article.

References

Ahn, K.K. and Kha, N.B. 2008. ‘‘Modeling and Control of Shape Memory Alloy Actuators Using Preisach Model, Genetic Algorithm and Fuzzy Logic,’’ Mechatronics, 18:141-152.

Brinson, L.C. 1993. ‘‘One-dimensional Constitutive Behavior of Shape Memory Alloys: Thermomechanical Derivation with Non-constant Material Functions and Redefined Martensite Internal Variable,’’ Journal of Intelligent Material, Systems and Structures, 4:229-242.

Candy, J.V. 2006. Model-based Signal Processing, IEEE Press, John Wiley & Sons, Inc., New Jersey .

Choi, S. and Cheong, C.C. 1996. ‘‘Vibration Control of Flexible Beams Using Shape Memory Actuators,’’ Journal of Guidance, Control, and Dynamics, 19:1178-1180.

Crassidis, J.L. and Junkins, J.L. 2004. Optimal Estimation of Dynamic Systems, CRC Press LLC, Boca Raton, FL.

Elahinia, M.H. , Ahmadian, M. and Ashrafiuon, H. 2004. ‘‘Design of a Kalman Filter for Rotary Shape Memory Alloy Actuators,’’ Smart Materials and Structures, 13: 691-697.

Furuya, Y. and Shimada, H. 1990. ‘‘Shape Memory Actuators for Robotic Applications,’’ In: Duering, T.W. , Melton, K.N. , Stockel, D. and Wayman, C.M. (eds), Engineering Aspects of Shape Memory Alloys, pp. 338-355, Butterworth-Heinemann, London.

Gawronski, W.K. 2004. Advanced Structural Dynamics and Active Control of Structures , Springer-Verlag, New York.

Jayender, J. , Patel, R.V. , Nikumb, S. and Ostojic, M. 2008. ‘‘Modeling and Control of Shape Memory Alloy Actuators,’’ IEEE Transactions on Control Systems Technology, 16: 279-287.

10.

Julier, S.J. and Uhlmann, J.K. 1996. ‘‘A General Method for Approximating Nonlinear Transformations of Probability Distributions,’’ Technical Report, RRG, Department of Engineering Science, University of Oxford.

11.

Julier, S.J. , Uhlmann, J.K. and Durrant-Whyte, H.F. 1995. ‘‘A New Approach for Filtering Nonlinear Systems,’’ In: American Control Conference , Seattle, WA, pp. 1628-1632.

12.

Kannan, S. , Giraud-Audine, C. and Patoor, E. 2008. ‘‘Identification of Dynamics and Hysteresis of Shape Memory Alloy (SMA) Actuator using Laguerre Filters,’’ In: 2008 IEEE International Symposium on Industrial Electronics, Cambridge, pp. 1388-1393.

13.

Kohl, M. , Krevet, B. and Just, E. 2002. ‘‘SMA Microgripper System,’’ Sensors and Actuators, 97-98:645-652.

14.

Léchevin, N. , Rabbath, C.A. , Wong, F. and Boissonneault, O. 2007. ‘‘Synthesis and Experimental Validation of Two-step Variable-structure Control of a Micro-actuated Flow Effector,’’ In: Proceedings of the 2007 American Control Conference , New York City, USA, pp. 3210-3215.

15.

Liang, C. and Rogers, C.A. 1990. ‘‘One-dimensional Thermomechanical Constitutive Relations for Shape Memory Materials,’’ Journal of Intelligent Material Systems and Structures, 1:207-234.

16.

Ljung, L. 2008. Perspectives on System Identification, In: Chung, M.J. and Misra, P. (eds.), Plenary Papers, Milestone Reports & Selected Survey Papers, 17th IFAC World Congress, July 2008, Seoul, pp. 47-59.

17.

Moallem, M. and Lu, J. 2005. ‘‘Application of Shape Memory Alloy Actuators for Flexure Control: Theory and Experiments,’’ IEEE/ ASME Transactions on Mechatronics, 10:495-501.

18.

Rezaeeian, A. , Yousefi-Koma, A. , Shasti, B. and Doosthoseini, A. 2008. ‘‘ANFIS Modeling and Feedforward Control of Shape Memory Alloy Actuators,’’ International Journal of Mathematical Models and Methods in Applied Sciences, 2:228-235.

19.

Tanaka, K. and Nagaki, S. 1982. ‘‘A Thermomechanical Description of Materials with Internal Variables in the Process of Phase Transformation, ’’ Ingenieur-Archive, 51:287-299.

20.

van der Wijst, M. 1998. ‘‘Shape Control of Structures and Materials with Shape Memory Alloys,’’ PhD Thesis, Eindhoven University of Technology, The Netherlands.

21.

Wan, E.A. and van der Merwe, R. 2001. ‘‘The Unscented Kalman Filter,’’ In: Haykin, S. (ed.), Kalman Filtering and Neural Networks, John Wiley & Sons, Inc., New York, pp. 221-280.

22.

Wang, Y.-F. , Hu, Y.-M. , Su, C.-Y. and Hong, H. 2007. ‘‘Modeling and Compensation for Hysteresis of Shape Memory Alloy Actuators with the Preisach Representation,’’ In: 2007 IEEE International Conference on Control and Automation, Guangzhou, China, pp. 239-244.

Online Estimation and Identification of Shape Memory Alloy-Actuated Flexible Structures Through Unscented Kalman Filtering

Abstract

Keywords

Get full access to this article

References