Identification of a dynamic model for shape memory alloy actuator using Hammerstein-Wiener gray box and mutable smart bee algorithm

Purpose – The purpose of the current investigation is to design a robust and reliable computational framework to effectively identify the nonlinear behavior of shape memory alloy (SMA) actuators, as one of the most applicable types of actuators in engineering and industry. The motivation of proposing such an intelligent paradigm emanates in the pursuit of fulfilling the necessity of devising a simple yet effective identification system capable of modeling the hysteric dynamical respond of SMA actuators. Design/methodology/approach – To address the requirements of designing a pragmatic identification system, the authors integrate a set of fast yet reliable intelligent methodologies and provide a predictive tool capable of realizing the nonlinear hysteric behavior of SMA actuators in a computationally efficient fashion. First, the authors utilize the governing equations to design a gray box Hammerstein-Wiener identifier model. At the next step, they adopt a computationally efficient metaheuristic algorithm ...

[1]  Nasser L. Azad,et al.  On the efficacy of chaos-enhanced heuristic walks with nature-based controllers for robust and accurate intelligent search, part A: an experimental analysis , 2015, J. Exp. Theor. Artif. Intell..

[2]  Ahmad Mozaffari,et al.  Vector optimization of laser solid freeform fabrication system using a hierarchical mutable smart bee-fuzzy inference system and hybrid NSGA-II/self-organizing map , 2014, J. Intell. Manuf..

[3]  S. Aramaki,et al.  Continuous system modeling of shape memory alloy (SMA) for control analysis , 1994, 1994 5th International Symposium on Micro Machine and Human Science Proceedings.

[4]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[5]  Craig A. Rogers,et al.  Active vibration and structural acoustic control of shape memory alloy hybrid composites: Experimental results , 1990 .

[6]  Kumar S. Ray,et al.  Pattern classification using fuzzy relation and genetic algorithm , 2012, Int. J. Intell. Comput. Cybern..

[7]  A. Kurdila,et al.  Hysteresis Modeling of SMA Actuators for Control Applications , 1998 .

[8]  Jonathan E. Rowe,et al.  A version of Geiringer-like theorem for decision making in the environments with randomness and incomplete information , 2012, Int. J. Intell. Comput. Cybern..

[9]  Mehdi Ahmadian,et al.  An enhanced SMA phenomenological model: II. The experimental study , 2005 .

[10]  Alireza Fathi,et al.  Aeroelastic characteristics of cantilever wing with embedded shape memory alloys , 2012 .

[11]  Saeed Behzadipour,et al.  An evolvable self-organizing neuro-fuzzy multilayered classifier with group method data handling and grammar-based bio-inspired supervisors for fault diagnosis of hydraulic systems , 2014, Int. J. Intell. Comput. Cybern..

[12]  Ahmad Mozaffari,et al.  Optimising maximum power output and minimum entropy generation of Atkinson cycle using mutable smart bees algorithm , 2012, Int. J. Comput. Sci. Eng..

[13]  Haibin Duan,et al.  Artificial Bee Colony approach to information granulation-based fuzzy radial basis function neural networks for image fusion , 2013 .

[14]  Georges Dumont,et al.  Finite element simulation for design optimisation of shape memory alloy spring actuators , 2005 .

[15]  Marc D Feldman,et al.  Coronary stents: a materials perspective. , 2007, Biomaterials.

[16]  Juan A. Gómez-Pulido,et al.  Multiobjective metaheuristics for frequency assignment problem in mobile networks with large‐scale real‐world instances , 2012 .

[17]  Eugenio Dragoni,et al.  Design equations for binary shape memory actuators under dissipative forces , 2009 .

[18]  Xin-She Yang,et al.  Bat algorithm: a novel approach for global engineering optimization , 2012, 1211.6663.

[19]  A. Fathi,et al.  EXPERIMENTAL MODEL OF SHAPE MEMORY ALLOY ACTUATORS USING MODIFIED PRANDTL-ISHLINSKII MODEL , 2011 .

[20]  Ahmad Mozaffari,et al.  Vector mutable smart bee algorithm for engineering optimisation , 2015, Int. J. Comput. Sci. Eng..

[21]  Riccardo Poli,et al.  Particle Swarm Optimisation , 2011 .

[22]  Theodore Eliades,et al.  Orthodontic materials research and applications: part 2. Current status and projected future developments in materials and biocompatibility. , 2007, American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics.

[23]  Alireza Fathi,et al.  Clad height control in laser solid freeform fabrication using a feedforward PID controller , 2007 .

[24]  J. Wen,et al.  Preisach modeling of piezoceramic and shape memory alloy hysteresis , 1995, Proceedings of International Conference on Control Applications.

[25]  Klaus Kuhnen,et al.  Modeling, Identification and Compensation of Complex Hysteretic Nonlinearities: A Modified Prandtl - Ishlinskii Approach , 2003, Eur. J. Control.

[26]  J. Elman.  Learning and development in neural networks: the importance of starting small , 1993, Cognition.

[27]  Ahmad Mozaffari,et al.  Optimal design of classic Atkinson engine with dynamic specific heat using adaptive neuro-fuzzy inference system and mutable smart bee algorithm , 2013, Swarm Evol. Comput..

[28]  Martin Middendorf,et al.  Particle swarm optimization for finding RNA secondary structures , 2011, Int. J. Intell. Comput. Cybern..

[29]  Tadahiro Hasegawa,et al.  Modeling of shape memory alloy actuator and tracking control system with the model , 2001, IEEE Trans. Control. Syst. Technol..

[30]  Amor Jnifene,et al.  An Experimental Study on a SMA Driven Pressurized Hyper-redundant Manipulator , 2008 .

[31]  Nasser L. Azad,et al.  International Journal of Intelligent Computing and Cybernetics Regularized machine learning through constraint swarm and evolutionary computation applied to regression problems , 2015 .

[32]  E. Mendes,et al.  Dynamic Hysteresis Modeling Based on , 2002 .

[33]  Wei Wang,et al.  Neural network hysteresis modeling with an improved Preisach model for piezoelectric actuators , 2012 .

[34]  Ahmad Mozaffari,et al.  Comprehensive preference optimization of an irreversible thermal engine using pareto based mutable smart bee algorithm and generalized regression neural network , 2013, Swarm Evol. Comput..

[35]  Alberto Bellini,et al.  Mechatronic Design of a Shape Memory Alloy Actuator for Automotive Tumble Flaps: A Case Study , 2009, IEEE Transactions on Industrial Electronics.

[36]  Mohd Jailani Mohd Nor,et al.  A new concept of a linear smart actuator , 2007 .

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

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

[39]  Nasser L. Azad,et al.  Simultaneous knowledge-based identification and optimization of PHEV fuel economy using hyper-level Pareto-based chaotic Lamarckian immune algorithm, MSBA and fuzzy programming , 2015, Int. J. Intell. Comput. Cybern..

[40]  Seung-Bok Choi,et al.  Vibration and Position Tracking Control of a Flexible Beam Using SMA Wire Actuators , 2009 .

[41]  R. Kusy,et al.  Thermal and mechanical characteristics of stainless steel, titanium-molybdenum, and nickel-titanium archwires. , 2007, American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics.

[42]  Dominiek Reynaerts,et al.  Design aspects of shape memory actuators , 1998 .

[43]  Dervis Karaboga,et al.  A modified Artificial Bee Colony algorithm for real-parameter optimization , 2012, Inf. Sci..

[44]  Michael D. Vose,et al.  Modeling genetic algorithms with Markov chains , 1992, Annals of Mathematics and Artificial Intelligence.

[45]  Xin-She Yang,et al.  Nature-Inspired Metaheuristic Algorithms , 2008 .

[46]  Amir Khajepour,et al.  Optimal design of laser solid freeform fabrication system and real-time prediction of melt pool geometry using intelligent evolutionary algorithms , 2013, Appl. Soft Comput..

[47]  Dorin Sabin Copaci,et al.  High-displacement flexible Shape Memory Alloy actuator for soft wearable robots , 2015, Robotics Auton. Syst..

[48]  Fan Yang,et al.  Nonlinear system modeling and identification of small helicopter based on genetic algorithm , 2013, Int. J. Intell. Comput. Cybern..

[49]  Saeid Bashash,et al.  Recursive Memory-based Hysteresis Modeling for Solid-state Smart Actuators , 2009 .

[50]  Harry Robertshaw,et al.  Experimental studies of structural acoustic control for a shape memory alloy composite beam , 1990 .

[51]  D. Karaboga,et al.  On the performance of artificial bee colony (ABC) algorithm , 2008, Appl. Soft Comput..

[52]  Ahmad Mozaffari,et al.  Modeling a shape memory alloy actuator using an evolvable recursive black-box and hybrid heuristic algorithms inspired based on the annual migration of salmons in nature , 2014, Appl. Soft Comput..

[53]  Akbar A. Javadi,et al.  Design and optimization of microstructure of auxetic materials , 2012 .

[54]  Ahmad Mozaffari,et al.  Optimal design of constraint engineering systems: application of mutable smart bee algorithm , 2012, Int. J. Bio Inspired Comput..

[55]  Ahmad Mozaffari,et al.  Identifying the behaviour of laser solid freeform fabrication system using aggregated neural network and the great salmon run optimisation algorithm , 2012, Int. J. Bio Inspired Comput..

[56]  Philippe Bidaud,et al.  An Active Tubular Polyarticulated Micro-System for Flexible Endoscope , 2000, ISER.