Control of Single-Segment Continuum Robots: Reinforcement Learning vs. Neural Network based PID
Control of Single-Segment Continuum Robots: Reinforcement Learning vs. Neural Network based PID
复制标题
单段连续体机器人的控制：强化学习与基于神经网络的 PID
DOI：
10.1109/iccpcct.2018.8574225
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
S. Bhaumik
中科院分区：
文献类型：
--
作者：
S. Chattopadhyay;Saptak Bhattacherjee;Soutrik Bandyopadhyay;A. Sengupta;S. Bhaumik
连续体机器人由于其在航天、国防、医疗、水下、工业等领域的广泛应用而在最近几天非常受欢迎。由于其高度非线性的动态特性，需要进行无模型智能控制，因此对这些类型的机器人进行建模是困难的。本文将两种智能无模型自适应控制方法，强化学习（RL）和基于神经网络的比例积分微分ANN-PID控制，应用于硬件连续体机器人。这里的RL技术涉及一个连续状态离散动作Q学习方法和ANN-PID是由一个单一的神经元实现。这两种方法的性能进行了比较，通过实现它们的硬件机器人。
Continuum robots have been very popular in the recent days due to their wide spread applications in space, defence, medical, underwater, industries etc. Modelling of these types of robots is difficult due to their highly nonlinear dynamic characteristic which necessitates the need for model-less intelligent control. In this paper two intelligent model-less adaptive methods,Reinforcement Learning (RL) and Artificial Neural Network based proportional integral derivative ANN-PID controlhave been applied on a hardware continuum robot. Here the RL technique involves a continuous state discrete action Q learning method and the ANN-PID is implemented by a single neuron. Performance of both the methods are compared by implementing them on a hardware robot.
影响因子：
7.8
作者：
Burgner-Kahrs, Jessica;Rucker, D. Caleb;Choset, Howie
通讯作者：
Choset, Howie
基于自适应卡尔曼滤波器的连续机器人无模型控制
影响因子：
6.4
作者：
Li, Minhan;Kang, Rongjie;Dai, Jian S.
通讯作者：
Dai, Jian S.