Using Neural Network Model Predictive Control for Controlling Shape Memory Alloy-Based Manipulator

被引：79

作者：

Nikdel, Nazila ^{[1
]}

Nikdel, Parisa ^{[1
]}

Badamchizadeh, Mohammad Ali ^{[1
]}

Hassanzadeh, Iraj ^{[1
]}

机构：

[1] Univ Tabriz, Fac Elect & Comp Engn, Tabriz 5166614766, Iran

来源：

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS | 2014年 / 61卷 / 03期

关键词：

Neural networks; 1-DOF robot manipulator; predictive control; shape memory alloys (SMAs); variable structure control (VSC); SMA PHENOMENOLOGICAL MODEL; ACTUATOR; STABILITY;

D O I：

10.1109/TIE.2013.2258292

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents a new setup and investigates neural model predictive and variable structure controllers designed to control the single-degree-of-freedom rotary manipulator actuated by shape memory alloy (SMA). SMAs are a special group of metallic materials and have been widely used in the robotic field because of their particular mechanical and electrical characteristics. SMA-actuated manipulators exhibit severe hysteresis, so the controllers should confront this problem and make the manipulator track the desired angle. In this paper, first, a mathematical model of the SMA-actuated robot manipulator is proposed and simulated. The controllers are then designed. The results set out the high performance of the proposed controllers. Finally, stability analysis for the closed-loop system is derived based on the dissipativity theory.

引用

页码：1394 / 1401

页数：8

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