School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China.
Department of Mechanical Engineering, Politecnico di Milano, 20156 Milan, Italy.
Sensors (Basel). 2022 Jul 13;22(14):5253. doi: 10.3390/s22145253.
This paper is devoted to studying the passivity-based sliding mode control for nonlinear systems and its application to dock cranes through an adaptive neural network approach, where the system suffers from time-varying delay, external disturbance and unknown nonlinearity. First, relying on the generalized Lagrange formula, the mathematical model for the crane system is established. Second, by virtue of an integral-type sliding surface function and the equivalent control theory, a sliding mode dynamic system can be obtained with a satisfactory dynamic property. Third, based on the RBF neural network approach, an adaptive control law is designed to ensure the finite-time existence of sliding motion in the face of unknown nonlinearity. Fourth, feasible easy-checking linear matrix inequality conditions are developed to analyze passification performance of the resulting sliding motion. Finally, a simulation study is provided to confirm the validity of the proposed method.
本文致力于研究非线性系统的基于无源的滑模控制及其通过自适应神经网络方法在码头起重机中的应用，其中系统受到时变延迟、外部干扰和未知非线性的影响。首先，基于广义拉格朗日公式，建立了起重机系统的数学模型。其次，利用积分型滑动面函数和等效控制理论，可以得到具有满意动态性能的滑模动态系统。第三，基于 RBF 神经网络方法，设计了一个自适应控制律，以确保在未知非线性情况下滑动运动的有限时间存在。第四，提出了可行的易于检查的线性矩阵不等式条件，以分析所得滑动运动的无源化性能。最后，进行了仿真研究，以验证所提出方法的有效性。
