High dynamic output feedback robust control of cascaded extended state observer

High dynamic tracking performance is a key technical index of hydraulic flight motion simulator(HFMS). However, the strong nonlinearities, various model uncertainties and measurement noise in hydraulic actuation systems limit the high dynamic performance improvement. In this paper, the outer axis frame of a HFMS is taken as a case study and its nonlinear dynamic model with consideration of strong nonlinearities, matched and mismatched uncertainties is established.A novel cascaded extended state ...     

Preparation and Mechanical Properties of Glass Coats with High Temperature Radar Absorber

BaO-La2O3-B2O3 （BLB） glass, suitable to be used as a sealing between metals, was chosen to be the binder in preparing glass coats on the Ti-alloy substrate. The SiCN nano-powder was introduced as the filler for the absorbing coat because it is considered to be a good high temperature absorber. The effect of the coating temperature and coating time on the tensile strength of the glass coat was investigated and the proper coating parameters to get good mechanical properties were determined. In addition, the effects of the SiCN content on the tensile strength of the absorbing coat were also discussed. Results show that it is possible to prepare the glass coat using the BLB glass as a binder. That the coat formed at 730℃ for 30 min has the best tensile strength witnesses 730 ℃, 30 min to be the proper parameter to prepare the glass coat. The BLB glass coat without SiCN powder possesses good tensile strength and the introduction of the SiCN absorber into the glass coat will lower the tensile strength. As the SiCN content increases, the tensile strength of the absorbing coat decreases, which could be attributed to the aggregation of SiCN in the coats.     

Preparation and Mechanical Properties of Glass Coats with High Temperature Radar Absorber

BaO-La2O3-B2O3 (BLB) glass, suitable to be used as a sealing between metals, was chosen to be the binder in preparing glass coats on the Ti-alloy substrate. The SiCN nano-powder was introduced as the filler for the absorbing coat because it is considered to be a good high temperature absorber. The effect of the coating temperature and coating time on the tensile strength of the glass coat was investigated and the proper coating parameters to get good mechanical properties were determined. In addition, the effects of the SiCN content on the tensile strength of the absorbing coat were also discussed. Results show that it is possible to prepare the glass coat using the BLB glass as a binder. That the coat formed at 730 ℃ for 30 min has the best tensile strength witnesses 730 ℃, 30 min to be the proper parameter to prepare the glass coat. The BLB glass coat without SiCN powder possesses good tensile strength and the introduc-tion of the SiCN absorber into the glass coat will lower the tensile strength. As the SiCN content increases, the tensile strength of the absorbing coat decreases, which could be attributed to the aggregation of SiCN in the coats.     

Compression Creep Behavior of High Volume Fraction of SiC Particles Reinforced AI Composite Fabricated by Pressureless Infiltration

The compression creep deformation of the high volume fraction of SiC particles reinforced AI-Mg-Si composite fabricated by pressure-less infiltration was investigated. The experimental results show that the creep stress exponents are very high at temperatures of 673 K, 723 K and 773 K, and if taking the threshold stress into account, the true stress exponent of minimum creep strain rate is still approximately 5, although the volume fraction of reinforcements is very high. The creep strain rate in the high volume fraction reinforced aluminum alloy matrix composites is controlled by matrix lattice diffusion. It is found that the creep-strengthening effect of high volume fraction of silicon carbide particles is significant, although the particles do not form effective obstacles to dislocation motion.     

High Temperature Flow Stress Prediction of Nano-Al2O3/Cu Composite Using an Artificial Neural Network

Alumina dispersion strengthened copper composite （nano-Al2O3/Cu composite） was recently emerged as a kind of potentially viable and attractive engineering material for applications requiring high strength, high thermal and electrical conductivities and resistance to softening at elevated temperatures. The nano-Al2O3/Cu composite was produced by internal oxidation. The microstructures of the composite were analyzed by the TEM and its hot deformation behavior was investigated by means of continuous compression tests performed on a Gleeble 1500 thermo-simulator. Making use of the modified algorithm-Levenberg-Marquardt （L-M） algorithm BP neural network, a model for predicting the flow stresses during hot deformation was set up on the base of the experimental data. Results show that the microstructures of the composite are characterized by uniform distribution of nano-Al2O3 particles in Cu-matrix. The sliding of dislocations is the main deformation mechanism. The dynamic recovery is the main softening mode with the flow stress decreasing gently from 500℃ to 850 ~C. The recrystallization of Cu-matrix can be retarded late into as high as 850 ℃, when it happens only partially. The well-trained BP neural network model can accurately describe the influence of the temperature, strain rate, and true strain on the flow stresses, therefore, it can precisely predict the flow stresses of the composite under given deforming conditions and provide a new way to optimize hot deforming process parameters.     

High Temperature Flow Stress Prediction of Nano-Al_2O_3/Cu Composite Using an Artificial Neural Network

Alumina dispersion strengthened copper composite (nano-Al2O3/Cu composite) was recently emerged as a kind of potentially vi-able and attractive engineering material for applications requiring high strength, high thermal and electrical conductivities and resistance to softening at elevated temperatures. The nano-Al2O3/Cu composite was produced by internal oxidation. The microstructures of the composite were analyzed by the TEM and its hot deformation behavior was investigated by means of continuous compression tests per-formed on a Gleeble 1500 thermo-simulator. Making use of the modified algorithm–Levenberg-Marquardt (L-M) algorithm BP neural network, a model for predicting the flow stresses during hot deformation was set up on the base of the experimental data. Results show that the microstructures of the composite are characterized by uniform distribution of nano-Al2O3 particles in Cu-matrix. The sliding of dislocations is the main deformation mechanism. The dynamic recovery is the main softening mode with the flow stress decreasing gen-tly from 500 ℃ to 850 ℃. The recrystallization of Cu-matrix can be retarded late into as high as 850 ℃, when it happens only partially. The well-trained BP neural network model can accurately describe the influence of the temperature, strain rate, and true strain on the flow stresses, therefore, it can precisely predict the flow stresses of the composite under given deforming conditions and provide a new way to optimize hot deforming process parameters.