An Adaptive Neuro-Fuzzy Model for Attitude Estimation and Control of a 3 DOF System

Abstract

In recent decades, one of the scientists’ main concerns has been to improve the accuracy of satellite attitude, regardless of the expense. The obvious result is that a large number of control strategies have been used to address this problem. In this study, an adaptive neuro-fuzzy integrated system (ANFIS) for satellite attitude estimation and control was developed. The controller was trained with the data provided by an optimal controller. Furthermore, a pulse modulator was used to generate the right ON/OFF commands of the thruster actuator. To evaluate the performance of the proposed controller in closed-loop simulation, an ANFIS observer was also used to estimate the attitude and angular velocities of the satellite using magnetometer, sun sensor, and data gyro data. However, a new ANFIS system was proposed that can jointly control and estimate the system attitude. The performance of the proposed controller was compared to the optimal PID controller in a Monte Carlo simulation with different initial conditions, disturbance, and noise. The results show that the proposed controller can surpass the optimal PID controller in several aspects including time and smoothness. In addition, the ANFIS estimator was examined and the results demonstrate the high ability of this designated observer. Consequently, evaluating the performance of PID and the proposed controller revealed that the proposed controller consumed less control effort for satellite attitude estimation under noise and uncertainty.

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Research on the Control Problem of Autonomous Underwater Vehicles Based on Strongly Coupled Radial Basis Function Conditions

“…The research object of adaptive control is a system whose parameters are not entirely determined, and the environment contains unknown and random factors. However, adaptive control is more complicated and costly than conventional control [14][15][16][17]. Model predictive control (MPC) is an optimization-based time domain control method [18].…”

Uyarlamali Ağ Tabanli Bulanik Çikarim Si̇stemi̇ Kullanarak Bi̇r Mekani̇k Ji̇roskopun Yalpalama Kesti̇ri̇mi̇

“…(2009) bir mekanik jiroskoptaki rastgele bozucu ve gürültüler nedeni ile oluşan hataları kestirmek üzere otomatik gerilemeli (AR), otomatik gerilemeli hareketli ortalama (ARMA), uyarlamalı ağ tabanlı bulanık çıkarım sistemi (ANFIS) ve nonlineer otomatik gerilemeli (NAR) yöntemlerinin kullanımını araştırmıştır [2]. Manyetometre, güneş sensörü ve jiroskop verilerini kullanarak bir uydunun durumu ve açısal hızları ANFIS gözlemcisi ile kapalı döngü kestirimi yapılmıştır [3]. Niu ve Cui (2017) kontrol moment jiroskop (CMG) sisteminin bulanık Takagi-Sugeno yöntemi ile kontrol stratejisi tasarlamıştır [4].…”