Implementación de artificial Bee Colony para controlador bilineal en actuadores SMA

Este trabajo consiste en la implementación del algoritmo ABC (Artificial Bee Colony) para buscar las mejores ganancias de un controlador PID Bilineal para actuadores SMA (Shape memory alloy). A continuación se realiza una breve descripción de los puntos más importantes de este trabajo. El algoritmo que voy a utilizar en este trabajo se denomina algoritmo ABC. Sus siglas traducidas al español significan colonia artificial de abejas. Como su nombre indica, su desarrollo se basa en el comportamiento de búsqueda de alimento de las abejas comunes. Es un algoritmo que se ha desarrollado recientemente y su uso no es tan común, aunque en estos últimos años muchos investigadores han empezado a utilizarlo por su precisión y fiabilidad. El controlador PID bilineal consiste en un regulador clásico PID al cual se le añade un componente bilineal. Este tipo de reguladores se usa en control de sistemas no lineales para compensar la no-linealidad de una planta. De este modo, facilita el modelado y el control de una función de transferencia no lineal de un sistema, que en el caso que aquí ocupa va a ser un actuador SMA. De acuerdo a esto, las ganancias que debe encontrar el algoritmo ABC para el controlador son: Kp, Ki, Kd (por parte del PID) y Kb (por parte del bilineal). El actuador SMA (Shape Memory Alloy) es un dispositivo compuesto por una aleación metálica de níquel y titanio que se caracteriza por la propiedad de que puede “recordar” y volver a su forma original tras una deformación. Hasta el momento es un material no muy conocido y su uso no está tan extendido, aunque se espera que en un futuro cercano esté implementado en los aparatos de los distintos sectores, como el biomédico o el aeronáutico. Los resultados experimentales muestran que se ha implementado satisfactoriamente el algoritmo y el optimizador es capaz de encontrar parámetros adecuados para el control bilineal cuando este se aplica para un modelo de planta que simula el comportamiento de actuador SMA.This work consists of the implementation of the ABC (Artificial Bee Colony) algorithm in order to optimize the parameters of a Bilinear PID controller for SMA (Shape Memory Alloy) actuators. A brief description about the most important aspects of the project is given in the following paragraphs. The algorithm that is the core of the optimization method is the ABC. It is based on the behavior of the common bees when searching for food. This technique is quite new and not very common among researchers. However, in the last years, its use has been increased due to its good performance regarding accuracy and reliability. The Bilinear PID controller is based on a classic PID regulator with a bilinear term. This type of controller is used in non-linear system to linearize the non-linear plant. After that, it is easier to model and control the linearized system. In this case, the non-linear model is a SMA actuator. The gains to be optimized by the ABC-based technique are Kp, Ki, Kd (PID) and Kb (bilinear term). The SMA actuator is a device formed by a metal alloy of nickel and titanium which is characterized by the property that it can "remember" and return to its original shape after deformation. The use of this material is not widespread, although it is expected that in the near future it will be implemented in devices of different sectors such as biomedical and aeronautics. The experimental results show that the algorithm has been successfully implemented and the optimizer computes adequate parameters for the bilinear controller when it is applied to a plant model that simulates the behavior of a SMA actuator.Ingeniería Electrónica Industrial y Automátic

A PHENOMENOLOGICAL MODEL OF SHAPE MEMORY ALLOYS INCLUDING TIME-VARYING STRESS

Shape memory alloys (SMAs) are metallic materials, which have two main stable crystalline phases: austenite, a high temperature phase and martensite, a low temperature phase. Austenite and martensite each have unique physical and mechanical properties, and transformation between these phases enables two effects known as the shape memory effect (SME) and superelasticity. When a material that displays the SME is plastically deformed at low temperature, a heat input will cause the SMA to return to its original shape before the deformation. At higher temperatures, the material displays an effect called superelasticity, where strains of up to 10% are recoverable. These characteristics of SMA allow for significant amounts of strain recovery, and enable the design of SMA actuators. The temperature in an SMA actuator is generally controlled by resistive heating, also know as joule heating, and the strain recovery capabilities are used to do work on a load, thereby creating an electro-mechanical actuator. SMA actuators have attractive properties such as high energy density, smooth and silent actuation, reduced part counts compared to traditional alternatives, and scalability down to the micromechanical level. The phase transformation in SMA actuators, however, is highly non-linear. Therefore, the use of SMA as actuators, for example in positioning systems, benefits from the development of good models to predict and control the materials. The goals of this work are to develop a model suitable for real-time implementation, and that reproduces the observed behaviour of SMA actuators. The model is then inverted and used to develop a model-based controller, used in conjunction with traditional PID control to improve the precision and robustness of SMA actuators. The modelling portion of this work consists of the development of a phenomenological SMA model. The forward model is split into three blocks: a heating block, a phase kinetics block and a mechanical block. Since joule heating is commonly used in SMA actuators to bring about an increase in temperature, the heating block presents equations to convert a current input into the temperature of the wire. The phase kinetics block equations convert the calculated temperature and applied stress to the fraction of martensite present in the SMA. Finally, the mechanical model calculates the strain in the material from the martensite fraction and the applied stress. Once the model equations are presented, experimental verification tests are shown to compare physical SMA behaviour with that predicted by the model. Each of the blocks of the forward model are then inverted in order to be used as a feedforward linearizing controller. The control section of this thesis deals with the response of two common types of SMA actuators: a constant force SMA actuator and a spring-biased SMA actuator. The response of the system to step and sinusoidal signals with period of 5 seconds is investigated using two types of controllers: a traditional PI controller and the inverse-model controller in feedforward with a PI controller in feedback. Additionally, the robustness of the system is investigated through the response of the system to transient and sinusoidal stress disturbances. The disturbance rejection is investigated on a constant force actuator both with and without the presence of a force sensor