자기 회귀 웨이블릿 신경망을 이용한 비선형 시스템의 터미널 슬라이딩 모드 제어

Terminal Sliding Mode Control of Nonlinear Systems Using Self-Recurrent Wavelet Neural Network

In this paper, we design a terminal sliding mode controller based on self-recurrent wavelet neural network (SRWNN) for the second-order nonlinear systems with model uncertainties. The terminal sliding mode control (TSMC) method can drive the tracking errors to zero within finite time in comparison with the classical sliding mode control (CSMC) method. In addition, the TSMC method has advantages such as the improved performance, robustness, reliability and precision. We employ the SRWNN to approximate model uncertainties. The weights of SRWNN are trained by adaptation laws induced from Lyapunov stability theorem. Finally, we carry out simulations for Duffing system and the wing rock phenomena to illustrate the effectiveness of the proposed control scheme.