Open AccessCompensation of hysteresis in a shape memory alloy wire system using linear parameter‐varying gain scheduling control
Author(s) -
Kilicarslan Atilla,
Grigoriadis Karolos,
Song Gangbing
Publication year - 2014
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2013.0856
Subject(s) - control theory (sociology) , gain scheduling , parametric statistics , sma* , actuator , shape memory alloy , hysteresis , controller (irrigation) , computer science , control engineering , control system , engineering , mathematics , algorithm , control (management) , physics , artificial intelligence , agronomy , statistics , quantum mechanics , electrical engineering , biology
Robust tracking control of shape memory alloy (SMA) systems is a challenge because of their highly non‐linear response, non‐local memory and lack of differentiability. In this work, a linear parameter‐varying (LPV) control method is proposed to compensate for the hysteretic behaviour of an SMA actuator. To provide information on the scheduling variable, the Preisach model of hysteresis is utilised. Parameter‐dependent controller is updated based on the real‐time computation of the instantaneous scheduling variable from the model. An H ∞ controller is also synthesised by representing the hysteresis as a parametric uncertainty. The controllers are implemented on an SMA actuated experimental system. Experimental validation is conducted for various types of reference inputs which can dramatically change the system's response. These reference signals are designed to excite major, minor and frequency dependent hysteresis loops. Trajectory tracking and disturbance rejection results show that the LPV controller has an improved response and the hysteresis is compensated for the full range of the scheduling variable. The tracking performances of both controllers are also compared when the system is under partial and persistent plant disturbances. For these cases, the LPV controller results in a more robust tracking response because of its less conservative structure.