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.     

Design and Fabrication of Concentration‐Gradient Generators with Two and Three Inlets in Microfluidic Chips

A simple and low‐cost method for designing and fabricating concentration‐gradient generators with two and three inlets is proposed which can generate different concentration gradients at varying flow velocities. The microchannel structure was designed in S‐shape and left‐right symmetry. The concentration‐gradient generator was simulated based on the finite element method. The microchannels were processed on a computer numerical control (CNC) engraving and milling machine on poly(methylmethacrylate) substrate, and then two concentration‐gradient generators were fabricated by hot bonding technology. The results of experiment and simulation were compared to prove the feasibility of the method. Flow velocity was an important factor for generating different concentration gradients. The concentration‐gradient profiles of the generators with two and three inlets present approximately linear and quadratic curves.     

Al2O3/Yttria‐Stabilized Zirconia Hollow‐Fiber Membrane Incorporated with Iron Oxide for Pb(II) Removal

Removal by absorptive ceramic membranes can simultaneously absorb and separate metal ions from water. Alumina/yttria‐stabilized zirconia (Al₂O₃/YSZ) hollow‐fiber membranes, fabricated using phase inversion and sintering process, were deposited with iron oxide by an in‐situ hydrothermal process. The results showed that α‐Fe₂O₃ was produced and incorporated across the membranes. A reduction in flux was recorded with the deposition of α‐Fe₂O_3. However, it improved the adsorption capacity for heavy metal adsorption. The adsorption‐separation test demonstrated that the optimized membrane is able to completely remove Pb(II) ions after two hours.     

Systematic convergence of nonlinear stochastic estimators on the Special Orthogonal Group SO(3)

This paper introduces two novel nonlinear stochastic attitude estimators developed on the Special Orthogonal Group with the tracking error of the normalized Euclidean distance meeting predefined transient and steady‐state characteristics. The tracking error is confined to initially start within a predetermined large set such that the transient performance is guaranteed to obey dynamically reducing boundaries and decrease smoothly and asymptotically to the origin in probability from almost any initial condition. The proposed estimators produce accurate attitude estimates with remarkable convergence properties using measurements obtained from low‐cost inertial measurement units. The estimators proposed in continuous form are complemented by their discrete versions for the implementation purposes. The simulation results illustrate the effectiveness and robustness of the proposed estimators against uncertain measurements and large initialization error, whether in continuous or discrete form.     

β-环糊精修饰Ni/SFCC强化C9石油树脂催化加氢性能

以废弃的流化催化裂化催化剂（简称SFCC）为载体、β-环糊精为金属络合剂、硝酸镍为镍源，采用湿法浸渍法制备β-环糊精修饰的Ni/SFCC催化剂（简称Ni/SFCC-CD催化剂），考察其对C9石油树脂的催化加氢性能。通过BET比表面积测试、H2程序升温还原、X射线光电子能谱等手段对催化剂的物相结构进行表征，研究β-环糊精的作用机理及其对催化剂加氢性能的影响。研究结果表明：在反应温度为260 ℃、反应压力为7 MPa、反应时间为2.0 h的最优条件下，采用Ni/SFCC-CD催化C9石油树脂加氢，可制得溴值为1.45 gBr/（100 g）、色号（加纳德）小于1的水白色氢化C9石油树脂，催化剂循环使用4次后仍保持良好活性；β-环糊精的作用机理是：β-环糊精与硝酸镍产生络合作用，抑制硝酸镍的分解、控制NiO的结晶过程和增强活性组分Ni与载体之间的相互作用力，从而提高了Ni/SFCC-CD的催化活性和稳定性。     

Automated classification of simulated wind field patterns from multiphysics ensemble forecasts

In this study, we have proposed an automated classification approach to identify meaningful patterns in wind field data. Utilizing an extensive simulated wind database, we have demonstrated that the proposed approach can identify low‐level jets, near‐uniform profiles, and other patterns in a reliable manner. We have studied the dependence of these wind profile patterns on locations (eg, offshore vs onshore), seasons, and diurnal cycles. Furthermore, we have found that the probability distributions of some of the patterns depend on the underlying planetary boundary layer schemes in a significant way. The future potential of the proposed approach in wind resource assessment and, more generally, in mesoscale model parameterization improvement is touched upon in this paper.     

Random poly(ε‐caprolactone‐L‐alanine) by direct melt copolymerization

A series of random polyesteramides (PEAs) with a range of molar composition from 90/10 to 50/50 were synthesized by direct melt polycondensation of ε‐caprolactone and l ‐alanine. Their structure was fully characterized by Fourier transform IR and NMR spectroscopy. The resulting copolymers are completely amorphous with the exception of PEA‐90/10 which possesses a semicrystalline structure. These PEAs present increasing glass transition temperatures at increasing l ‐alanine contents and exhibit fairly good thermal stability with 10% mass loss temperatures reaching 315 °C. © 2020 Society of Industrial Chemistry