Adaptive Control Using Support Vector Regression for Hypersonic Aircraft Control System
نویسندگان
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.
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