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      저자 : Baolin Hou (검색결과 2 건)
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        Trajectory tracking control of a 2-DOF manipulator using computed torque control combined with an implicit lyapunov function method

        Xi Wang,Baolin Hou 대한기계학회 2018 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.32 No.6

        A combined control method based on computed torque control and an implicit Lyapunov function method for trajectory tracking controlling of a 2-DOF manipulator was investigated. The manipulator works under condition of random base vibration and payload uncertainty. Base vibration acts on the manipulator in two directions: in vertical direction and in pitching direction. The computed torque control employed here aims to linearize the strongly nonlinear coupling manipulator system, and also to decouple it. Implicit Lyapunov function control method is one type of continuous feedback control. Its control gains are differentiable function of system error variables, and as the system error variables tend to zero, control gains will turn to infinite. Even so, this method can guarantee the control forces bounded in norm through the control process. The combined method is analyzed based on analytical models of manipulator system, which are established via second kind Lagrange equation. Specially, the analytical models are established according to base vibration in two directions. Numerical simulation results show that the combined control method has strong robustness against random base vibration in both direction and payload uncertainty. Besides, under all conditions considered in this paper, the method can always have fast convergence and high tracking accuracy.

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        Fault identification of a chain conveyor based on functional data feature engineering and optimized multi-layer kernel extreme learning machine

        Hao Wen,Baolin Hou,Xin Jin 대한기계학회 2023 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.37 No.5

        The functional time series signals generated during the operation of electromechanical systems contain fault characteristic information. This study proposes a fault identification method for electromechanical systems based on functional data feature engineering and multi-layer kernel extreme learning machine (MLKELM) optimized by sparrow search algorithm (SSA). First, multiple time series signals under different fault conditions are functionalized under the B-spline basis function system, and the feature reduction space is constructed by functional principal component analysis (FPCA) and principal differential analysis (PDA) to extract fault features. Second, the minimum redundancy and maximum relevance (mRMR) method is performed on the initial feature set for feature selection. In addition, the size of the optimal feature subset is determined by the class separability of feature subset (CSFS) criterion. Finally, deep feature learning and fault identification are implemented by MLKELM and the pre-defined parameters are optimized based on the SSA in this process to improve its performance. The experimental results show that the proposed method can effectively extract the fault features of function time series signals, and then accurately identify the faults of electromechanical systems.

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