Robotic Knee Tracking Control to Mimic the Intact Human Knee Profile Based on Actor-Critic Reinforcement Learning

We address a state-of-the-art reinforcement learning(RL)control approach to automatically configure robotic pros-thesis impedance parameters to enable end-to-end,continuous locomotion intended for transfemoral amputee subjects.Specifically,our actor-critic based RL provides tracking control of a robotic knee prosthesis to mimic the intact knee profile.This is a significant advance from our previous RL based automatic tuning of prosthesis control parameters which have centered on regulation control with a designer prescribed robotic knee profile as the target.In addition to presenting the tracking control algorithm based on direct heuristic dynamic programming(dHDP),we provide a control performance guarantee including the case of constrained inputs.We show that our proposed tracking control possesses several important properties,such as weight convergence of the learning networks,Bellman(sub)optimality of the cost-to-go value function and control input,and practical stability of the human-robot system.We further provide a systematic simulation of the proposed tracking control using a realistic human-robot system simulator,the OpenSim,to emulate how the dHDP enables level ground walking,walking on different terrains and at different paces.These results show that our proposed dHDP based tracking control is not only theoretically suitable,but also practically useful.

Density Functional Theory and Tight Binding-Based Dynamical Studies of Carbon Metal Systems of Relevance to Carbon Nanotube Growth

Density functional theory(DFT)and tight binding(TB)models have been used to study systems containing single-walled carbon nanotubes(SWNTs)and metal clusters that are of relevance to SWNT growth and regrowth.In particular,TB-based Monte Carlo(TBMC)simulations at 1000 or 1500 K show that Ni atoms that are initially on the surface of the SWNT or that are clustered near the SWNT end diffuse to the nanotube end so that virtually none of the Ni atoms are located inside the nanotube.This occurs,in part,due to the lowering of the Ni atom energies when they retract from the SWNT to the interior of the cluster.Aggregation of the atoms at the SWNT end does not change the chirality within the simulation time,which supports the application of SWNT regrowth(seeded growth)as a potential route for chirality-controlled SWNT production.DFT-based geometry optimisation and direct dynamics at 2000 K show that Cr and Mo atoms in Cr5Co50 and Mo5Co50 clusters prefer to be distributed in the interior of the clusters.Extension of these calculations should deepen our understanding of the role of the various alloy components in SWNT growth.

Experimental and numerical analysis of the heat flux characteristic of the plume of a 120-N thruster

The convective heat transfer of the plume of a 120-N thruster is investigated experimentally and numerically. Numerical results agree well with experimental results in that there is a nonlinear decrease in convective heat transfer with an increasing cone angle. It is also found that convective heat transfer decreases with increasing distance from the thruster outlet. Furthermore, the convective heat transfer of the plume mainly concentrates within a 35° cone angle and the heat flux decreases to the same order as solar radiation at the Earth’s surface when the cone angle exceeds 60°. The results of the study will help improve spacecraft design.

Partial Dynamic Dimension Reduction for Conditional Mean in Regression

In many regression analysis,the authors are interested in regression mean of response variate given predictors,not its the conditional distribution.This paper is concerned with dimension reduction of predictors in sense of mean function of response conditioning on predictors.The authors introduce the notion of partial dynamic central mean dimension reduction subspace,different from central mean dimension reduction subspace,it has varying subspace in the domain of predictors,and its structural dimensionality may not be the same point by point.The authors study the property of partial dynamic central mean dimension reduction subspace,and develop estimated methods called dynamic ordinary least squares and dynamic principal Hessian directions,which are extension of ordinary least squares and principal Hessian directions based on central mean dimension reduction subspace.The kernel estimate methods for dynamic ordinary least squares and dynamic Principal Hessian Directions are employed,and large sample properties of estimators are given under the regular conditions.Simulations and real data analysis demonstrate that they are effective.