.png){kind=icon}
返回
Radial Basis Function Neural Network-Based Adaptive Trajectory Tracking Control for Continuum Robots
.png){kind=icon}
PRE
AI
DOI：10.1007/978-981-95-2095-4_49.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
连续体机器人作为高度耦合的非线性多变量系统，存在显著模型不准确问题，限制了精确运动控制，尤其是在手术和检测任务中。为解决此问题，我们提出了一种基于神经网络的自适应轨迹跟踪框架。径向基函数（RBF）神经网络用于逼近动态不确定性（重力、弹性、科里奥利力和外部扰动），同时建立了惯性矩阵范数的下界。这些被整合到自适应反步控制器中，确保全局一致最终有界稳定性。仿真结果表明该方法具有出色的跟踪精度和针对未建模动态及扰动的强鲁棒性。
Keyword:
Radial Basis Function
Neural Networks
Continuum Robots
Adaptive Control
Trajectory Tracking Control
期刊
I
IF:
0
论文数:
35
被引数:
0
机构
引用论文
Online TCP trajectory planning for redundant continuum manipulators using quadratic programming
PREAI
分享收藏Model-free motion control of continuum robots based on a zeroing neurodynamic approach.png)
NEURAL NETWORKS
NEURAL NETWORKSIF6.3
PREAI
分享收藏An Efficient Multi-solution Solver for the Inverse Kinematics of 3-Section Constant-Curvature Robots
OAAI
OAAI分享收藏Development of the Ultralight Hybrid Pneumatic Artificial Muscle: Modelling and optimizationPLOS ONEOAAI
PLOS ONEIF0
OAAI分享收藏Control of Single-Segment Continuum Robots: Reinforcement Learning vs. Neural Network based PID2018 International Conference on Control, Power, Communication and Computing Technologies (ICCPCCT)
2018 International Conference on Control, Power, Communication and Computing Technologies (ICCPCCT)IF0
PREAI
分享收藏
查看更多内容