Self-Recurrent Wavelet Neural Network Based Terminal Sliding Mode Control of Nonlinear Systems with Uncertainties

불확실성을 갖는 비선형 시스템의 자기 회귀 웨이블릿 신경망 기반 터미널 슬라이딩 모드 제어

In this paper, we design a terminal sliding mode controller based on neural network for nonlinear systems with uncertainties. Terminal sliding mode control (TSMC) method can drive the tracking errors to zero within finite time. Also, TSMC has the advantages such as improved performance, robustness, reliability and precision by contrast with classical sliding mode control. For the control of nonlinear system with uncertainties, we employ the self-recurrent wavelet neural network(SRWNN) which is used for the prediction of uncertainties. The weights of SRWNN are trained by adaptive laws based on Lyapunov stability theorem. Finally, we carry out simulations to illustrate the effectiveness of the proposed control.