Type-2 fuzzy logic controller for position control of shape memory alloy wire actuator

Abstract

The main aim of this article is to present the usage of type-2 fuzzy logic controller to control a shape memory actuator. One of the main advantages of the fuzzy systems is that they do not require creating any mathematical model of the controlled plant which simplifies the control task. However, a dynamic system model was created to adjust the proportional–integral–derivative controller parameters. Different types of controllers were tested experimentally by controlling the position of the commercially available shape memory alloy actuator NM70 made by Miga Motor company. Despite its small size, it distinguishes itself by its strength. To enhance real-time performance, simplified interval fuzzy sets were used. The algorithm was implemented in the ATmega32 microcontroller. The dedicated PC application was also built. The obtained results confirmed that type-2 fuzzy controller performed efficiently with a nonlinear plant which is difficult to control. The research also proved that interval type-2 controllers, which are a simplified version of the general type-2 controllers, are very efficient. They can handle uncertainties without increasing drastically the computational complexity. Experimental data comparison of the proportional–integral–derivative reference with the fuzzy logic controller type-2 and type-1 clearly indicates the superiority of the former, especially in reducing overshooting.

Modeling of self-excited stress measurement system

“…where L is the inductance of the stack. Substitution of equation ( 7) into equation (5) gives a dependence between the mechanical input force f and the voltage change for the output v in the sensorial operation of the piezo-stack where parameter e describes the strength of the negative resistance of the lambda diode used in electrical equivalent model of the auto-oscillating system, b describes curvature at the inflection point, and C is the capacitance provided by the piezoelectric transducer and its cables.…”

“…The self-excited systems are widely known in technics 3 and in energy-harvesting devices. 5–7 The self-excitation is used also in devices called auto-oscillators.…”

Accurate sensorless displacement control based on the electrical resistance of the shape memory actuator

“…There are different methods to control SMA actuators using an external displacement sensor (Dominik, 2016; Kha and Ahn, 2006; Moallem and Tabrizi, 2008; Song, 2002; Troisfontaine et al, 1998). Direct control with the use of a displacement laser sensor is presented in Song et al (2000), control with the use of temperature as a feedback signal in Troisfontaine et al (1998), and indirect control displacement based on the SMA electrical resistance as a feedback signal in the control loop in Raparelli et al (2002), Lan and Fan (2010), Malukhin and Ehmann (2008), Liu et al (2010), and Takeda et al (2008).…”

Q-Learning Neural Controller for Steam Generator Station in Micro Cogeneration Systems

“…A whole class of controller solutions for such systems is the use of fuzzy logic [33][34][35][36][37][38]. Fuzzy logic based controllers are so widely used because they provide a scientific methodology to replicate the behavior of human inference for imprecise and incomplete information in feedback.…”