Adaptive Control of 3-DOF Delta Parallel Robot

被引：2

作者：

Aguilar-Mejia, O. ^{[1
]}

Escorcia-Hernandez, J. M. ^{[2
]}

Tapia-Olvera, R. ^{[3
]}

Minor-Popocatl, H. ^{[4
]}

Valderrabano-Gonzalez, A. ^{[5
]}

机构：

[1] UPAEP Univ, Dept Invest & Posgrad, Puebla, Mexico

[2] Univ Politecn Tulancingo, Tulancingo, Hidalgo, Mexico

[3] Univ Nacl Autonoma Mexico, Cd De Mexico, Mexico

[4] UPAEP Univ, Puebla, Mexico

[5] Univ Panamer, Fac Ingn, Zapopan, Jalisco, Mexico

来源：

2019 IEEE INTERNATIONAL AUTUMN MEETING ON POWER, ELECTRONICS AND COMPUTING (ROPEC 2019) | 2019年

关键词：

Delta parallel robot; B-Spline controller; Trajectory tracking;

D O I：

10.1109/ropec48299.2019.9057075

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

In this paper an adaptive neural network controller is used to solve the problem of tracking trajectories of a delta parallel robot (DPR) with three degrees of freedom. This controller used an adaptive artificial B-Spline neural network (BSNN) for online training. The BSNN improves the performance of DPR on a closed loop and update the parameters of control scheme online. This algorithm sets the control signal without using a detailed mathematical model nor exact values of the parameters of the DPR. The proposed adaptive controller was compared with a traditional control based a PD+G contoller. Analytical and numerical results prove the robust and efficient performance of the adaptive neural network controller.

引用

页数：6

共 14 条

[1] Robust Trajectory Tracking of a Delta Robot Through Adaptive Active Disturbance Rejection Control [J].