基于个体协同触发强化学习的多机器人行为决策方法* .txt

摘要：为了提高多机器人行为最优决策控制中强化学习的效率和收敛速度，研究了多机器人的分布式马尔科夫建模与控制策略。根据机器人有限感知能力设计了个体 协同感知触发函数，机器人个体从环境观测结果计算个体 协同触发响应概率，定义一次触发过程后开始计算联合策略，减少机器人间通讯量和计算资源。引入双学习率改进Q学习算法，并将该算法应用于机器人行为决策。仿真实验结果表明，当机器人群组数量在20左右时，本文算法的协同效率较高，单位时步比为1085 0。同时距离调节参数η对机器人协同搜索效率有影响，当η=0008时，所需的移动时步比和平均移动距离都能达到最小值。通过双学习率的引入，该算法较基于环境模型的强化学习算法具有更高的学习效率和适用性，平均性能提升35%，对于提高多机器人自主协同能力具有较高的理论意义及应用价值。 .txt     

Distributed learning consensus control based on neural networks for heterogeneous nonlinear multiagent systems

This paper considers a novel distributed iterative learning consensus control algorithm based on neural networks for the control of heterogeneous nonlinear multiagent systems. The system's unknown nonlinear function is approximated by suitable neural networks; the approximation error is countered by a robust term in the control. Two types of control algorithms, both of which utilize distributed learning laws, are provided to achieve consensus. In the provided control algorithms, the desired reference is considered to be an unknown factor and then estimated using the associated learning laws. The consensus convergence is proven by the composite energy function method. A numerical simulation is ultimately presented to demonstrate the efficacy of the proposed control schemes.     

Q690钢焊接接头微观组织对冷裂纹的影响研究

分析了Q690钢手工电弧焊与气体保护焊焊接冷裂纹产生与微观组织的联系。结果表明: 焊接冷裂纹起源于斜Y坡口根部, 沿熔合区、焊缝扩展至焊缝表面;2种工艺焊接的接头组织硬度均较母材高, 而塑性较差。热影响区为马氏体、贝氏体与铁素体混合组织, 焊缝组织由先共析铁素体、侧板条铁素体、贝氏体和针状铁素体等组成, 针状铁素体抗裂纹扩展能力较其他组织强。在2种焊接方法对应断口中观察到了冷裂纹起裂、扩展与断裂区的不同形貌, 起裂与扩展区存在塑性变形, 而断裂区为解理断裂。     

Electrocatalytic Reduction of CO2 to Ethylene by Molecular Cu‐Complex Immobilized on Graphitized Mesoporous Carbon

The electrochemical reduction of carbon dioxide (CO₂) to hydrocarbons is a challenging task because of the issues in controlling the efficiency and selectivity of the products. Among the various transition metals, copper has attracted attention as it yields more reduced and C2 products even while using mononuclear copper center as catalysts. In addition, it is found that reversible formation of copper nanoparticle acts as the real catalytically active site for the conversion of CO₂ to reduced products. Here, it is demonstrated that the dinuclear molecular copper complex immobilized over graphitized mesoporous carbon can act as catalysts for the conversion of CO₂ to hydrocarbons (methane and ethylene) up to 60%. Interestingly, high selectivity toward C2 product (40% faradaic efficiency) is achieved by a molecular complex based hybrid material from CO₂ in 0.1 m KCl. In addition, the role of local pH, porous structure, and carbon support in limiting the mass transport to achieve the highly reduced products is demonstrated. Although the spectroscopic analysis of the catalysts exhibits molecular nature of the complex after 2 h bulk electrolysis, morphological study reveals that the newly generated copper cluster is the real active site during the catalytic reactions.