Adaptive Control Using Support Vector Regression for Hypersonic Aircraft Control System

نویسندگان

  • Jongho Shin
  • H. Jin Kim
  • Youdan Kim
  • Min-Jea Tahk
  • JONGHO SHIN
  • H. JIN KIM
  • YOUDAN KIM
  • MIN-JEA TAHK
چکیده

This paper presents support vector regression (SVR)-based adaptive controller for the longitudinal dynamics of a generic hypersonic aircraft. SVR has been proven to generate global solutions contrary to neural networks, because SVR basically solves quadratic programming (QP) problems. With this advantage, the nominal dynamics of the input-output feedback-linearized hypersonic airplane is trained off-line. In order to compensate the offline-training error and unknown uncertainties in the control process, and to avoid the controller singularity problem, an adaptation algorithm of the offline-trained SVR is proposed using the concept of virtual control input. Stability of the overall system is analyzed by the Lyapunov stability theory. Numerical simulations validate the performance of the proposed approach.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Prediction of soil cation exchange capacity using support vector regression optimized by genetic algorithm and adaptive network-based fuzzy inference system

Soil cation exchange capacity (CEC) is a parameter that represents soil fertility. Being difficult to measure, pedotransfer functions (PTFs) can be routinely applied for prediction of CEC by soil physicochemical properties that can be easily measured. This study developed the support vector regression (SVR) combined with genetic algorithm (GA) together with the adaptive network-based fuzzy infe...

متن کامل

Designing and Modeling a Control System for Aircraft in the Presence of Wind Disturbance (TECHNICAL NOTE)

This paper proposes a switching adaptive control for trajectory tracking of unmanned aircraft systems. The switching adaptive control method is designed to overcome the wind disturbance and achieve a proper tracking performance for control systems. In the suggested system, the wind disturbance is regarded as a finite set of uncertainties; a controller is designed for each uncertainty, and a per...

متن کامل

Near Space Hypersonic Unmanned Aerial Vehicle Dynamic Surface Backstepping Control Design

Compared with traditional aircraft, the near space hypersonic unmanned aerial vehicle control system design must deal with the extra prominent dynamics characters, which are differ from the traditional aircrafts control system design. A new robust adaptive control design method is proposed for one hypersonic unmanned aerial vehicle (HSUAV) uncertain MIMO nonaffine block control system by using ...

متن کامل

Prediction of Fe-Co-Mn/MgO Catalytic Activity in Fischer-Tropsch Synthesis Using Nu-support Vector Regression

Support vector regression (SVR) is a learning method based on the support vector machine (SVM) that can be used for curve fitting and function estimation. In this paper, the ability of the nu-SVR to predict the catalytic activity of the Fischer-Tropsch (FT) reaction is evaluated and the result is compared with two other prediction techniques including: multilayer perceptron (MLP) and subtractiv...

متن کامل

Identification and Adaptive Position and Speed Control of Permanent Magnet DC Motor with Dead Zone Characteristics Based on Support Vector Machines

In this paper a new type of neural networks known as Least Squares Support Vector Machines which gained a huge fame during the recent years for identification of nonlinear systems has been used to identify DC motor with nonlinear dead zone characteristics. The identified system after linearization in each time span, in an online manner provide the model data for Model Predictive Controller of p...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2010