RBF Neural Network Adaptive Sliding Mode Control of Rotary Stewart Platform

被引：7

作者：

Tan Van Nguyen ^{[1
]}

Ha, Cheolkeun ^{[2
]}

机构：

[1] Ulsan Univ, Dept Mech & Aerosp Engn, Ulsan, South Korea

[2] Univ Ulsan, Dept Mech Engn, Ulsan, South Korea

来源：

INTELLIGENT COMPUTING METHODOLOGIES, ICIC 2018, PT III | 2018年 / 10956卷

关键词：

Rotary Stewart Platform; Inverse kinematic; Adaptive sliding mode; Simmechanics; PARALLEL MANIPULATOR; KINEMATICS;

D O I：

10.1007/978-3-319-95957-3_17

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The stewart platform is widely applied in the industry. However, Rotary Stewart Platform (RSP) has very little research for this type. Moreover, Inverse Kinematic (IK) solution in papers published previously is complex and unclear. Therefore, in this paper, first, we design and build the mathematical model and check it in Simmechnics. Second, the robust control of the RSP proposed in this paper precisely tracks a command under the platform uncertainties. The inverse kinematic solution of the platform, derived in this paper, supports for control design of the platform. Radial Basis Function (RBF) neural network adaptive sliding mode controller is used to achieve the satisfactory tracking performance and the system stability. Stability of the system is guaranteed through Lyapunov theory. The simulation is conducted to illustrate the effectiveness of the proposed control for the RSP.

引用

页码：149 / 162

页数：14

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