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Số người truy cập: 107,045,165

 Adaptive neural integral full-order terminal sliding mode control for an uncertain nonlinear system
Tác giả hoặc Nhóm tác giả: Anh Tuan Vo, Hee-Jun Kang
Nơi đăng: IEEE Access, IEEE Xplore Digital Library; Số: 7;Từ->đến trang: 42238-42246;Năm: 2019
Lĩnh vực: Kỹ thuật; Loại: Bài báo khoa học; Thể loại: Quốc tế
TÓM TẮT
This paper reports the design of a control system for a class of general nonlinear second-order systems. The significant problems of singularity and chattering phenomenon, which limit the use of the conventional terminal sliding mode control (TSMC) in real applications due to the order of the sliding surface, need to be addressed. In addition, the effects of disturbances and uncertainties need to be removed, and the response rates are increased. Therefore, the integral full-order terminal sliding mode (IFOTSM) surface was proposed. To track the specified trajectories with high accuracy, a control approach is developed for the class of general nonlinear second-order systems by utilizing an IFOTSM surface and an adaptive compensator. The unknown dynamic model is derived based on a radial basis function neural network (RBFNN). Consequently, our controller provides good performance with minimum position errors, robustness against uncertainties, and work without a precise dynamic model. The simulated examples were performed to analyze the effectiveness of the control approach for position pathway tracking control of a 2-DOF parallel manipulator.
ABSTRACT
This paper reports the design of a control system for a class of general nonlinear second-order systems. The significant problems of singularity and chattering phenomenon, which limit the use of the conventional terminal sliding mode control (TSMC) in real applications due to the order of the sliding surface, need to be addressed. In addition, the effects of disturbances and uncertainties need to be removed, and the response rates are increased. Therefore, the integral full-order terminal sliding mode (IFOTSM) surface was proposed. To track the specified trajectories with high accuracy, a control approach is developed for the class of general nonlinear second-order systems by utilizing an IFOTSM surface and an adaptive compensator. The unknown dynamic model is derived based on a radial basis function neural network (RBFNN). Consequently, our controller provides good performance with minimum position errors, robustness against uncertainties, and work without a precise dynamic model. The simulated examples were performed to analyze the effectiveness of the control approach for position pathway tracking control of a 2-DOF parallel manipulator.
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