"The proposed controller requires very little prior knowledge of the dynamic model, is robust to unknown dynamics and exogenous disturbances, and can achieve asymptotic output tracking." "Different from a classical RISE control law, a tanh function is utilized instead of a sign function to acquire ...
(Gradient Networks,Gradient Field Learning,GradNets)Reinforcement learning based adaptive control for uncertain mechanical systems with asymptotic tracking 这篇文章的详细信息如下: 文章信息 作者: Shreyas Chaudhari, Srinivasa Pranav, José M.F. Moura 作者单位: Carnegie Mellon University's Department of Elec...
Reinforcement-Learning-based Adaptive Optimal Control for Arbitrary Reference Tracking Model-free control based on the idea of Reinforcement Learning is a promising control approach that has recently gained extensive attention. However, most Reinforcement-Learning-based control methods solely focus on the re...
网络自适应控制与学习控制 网络释义 1. 自适应控制与学习控制 资源查看 ... Fault Diagnosis 故障诊断Adaptive control and Learning Control自适应控制与学习控制... lib.hbut.edu.cn|基于9个网页
Finally, we test the performance of this approach based on a practical complex system, namely, the learning and control of the tension and height of the looper system in a hot strip mill. Experimental results demonstrate that the proposed approach can achieve effective and robust performance. ...
网络自适应重复学习控制 网络释义 1. 自适应重复学习控制 自适应重复学... ... )adaptive repetitive learning control自适应重复学习控制) Adaptive-learning control 自适应学习控制 ... www.dictall.com|基于3个网页 例句
Learning-based Adaptive Optimal Impedance Control to Enhance Physical Human-robot Interaction Performance This paper presents a framework of adaptive optimal impedance control to enhance physical human-robot interaction (pHRI) performance. The overall structure... Y Guo,Y Tian,H Wang - 《International ...
aBy requiring exponentially distributed firing times, we obtain stochastic Petri nets (SPN). Stochastic Reward nets are SPNs augmented with the ability to specify output measures as reward-based functions, for the evaluation of reliability for complex systems [3]. 通过需要指数地分布的射击的时期,我们...
审稿看到一篇神经网络NN(RBFNN、Actor-critic learning)和滑膜控制SMC(sliding mode control)结合换个应用对象纯灌水的投稿。Actor-critic learning based adaptive super-twisting sliding mode control for uncertain robot manipulators with full state constraints. 这种滑模控制(SMC) sliding mode control套各种神经网络(...
Georgia Tech 的 Adaptive Control and Reinforcement Learning 课程 Adaptive Control and Reinforcement Learning- Georgia Tech --- 上述链接最后 acknowledge 了 CMU 的同名课程: Adaptive Control and Reinf…