A robust control of robot manipulators for physical interaction: stability analysis for the interaction with unknown environments

Intelligent Service Robotics - A robust control designed for multiple degrees-of-freedom (DOF) robot manipulators performing complex tasks requiring frequent physical interaction with unknown...     

High‐performance thermosets with tailored properties derived from methacrylated eugenol and epoxy‐based vinyl ester

A renewable chemical, eugenol, is methacrylated to produce methacrylated eugenol (ME) employing the Steglich esterification reaction without any solvent. The resulting ME is used as a low‐viscosity co‐monomer to replace styrene in a commercial epoxy‐based vinyl ester resin (VE). The volatility and viscosity of ME and styrene are compared. The effect of ME loading and temperature on the viscosity of the VE–ME resin is investigated. Moreover, the thermomechanical properties, curing extent and thermal stability of the fully cured VE–ME thermosets are systematically examined. The results indicate that ME is a monomer with low volatility and low viscosity, and therefore the incorporation of ME monomer in VE resins allows significant reduction of viscosity. Moreover, the viscosity of the VE–ME resin can be tailored by adjusting the ME loadings and processing temperature to meet commercial liquid molding technology requirements. The glass transition temperatures of VE–ME thermosets range from 139 to 199 °C. In addition, more than 95% of the monomer is incorporated and fixed in the crosslinked network structure of VE–ME thermosets. Overall, the developed ME monomer exhibits promising potential for replacing styrene as an effective low‐viscosity co‐monomer. The VE–ME resins show great advantages for use in polymer matrices for high‐performance fiber‐reinforced composites. This work is of great significance to the vinyl ester industry by providing detailed experimental support. © 2018 Society of Chemical Industry     

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.     

Microwave dielectric properties of SrLa[Ga1−x(Mg0.5Ti0.5)x]O4 and SrLa[Ga1−x(Zn0.5Ti0.5)x]O4 (x = 0.2-0.8) ceramics

SrLa[Ga_1−x(R_0.5Ti_0.5)_x]O₄ (R = Mg, Zn) ceramics were prepared by a standard solid state sintering method. The single-phase ceramics with K₂NiF₄-type layered perovskite structure and I4/mmm space group were obtained, indicating that SrLa(R_0.5Ti_0.5) and SrLaGaO₄ can form the unlimited solid solutions. With increasing x for R = Mg and Zn, ε_r increases monotonously, the Qf value first increases and then decreases, while τ_f increases from a negative to a positive value. The optimized microwave dielectric properties were obtained as following: ε_r = 23.3, Qf = 89 400 GHz, τ_f = −0.8 ppm/°C for SrLa[Ga_0.6(Mg_0.5Ti_0.5)_0.4]O₄ and ε_r = 23.3, Qf = 76 200 GHz, τ_f = 0.2 ppm/°C for SrLa[Ga_0.7(Zn_0.5Ti_0.5)_0.3]O₄, indicating that the present solid solution ceramics are the promising candidates as microwave resonator materials for the telecommunication applications.     

Image and Feature Space Based Domain Adaptation for Vehicle Detection

The application of deep learning in the field of object detection has experienced much progress. However, due to the domain shift problem, applying an off-the-shelf detector to another domain leads to a significant performance drop. A large number of ground truth labels are required when using another domain to train models, demanding a large amount of human and financial resources. In order to avoid excessive resource requirements and performance drop caused by domain shift, this paper proposes a new domain adaptive approach to cross-domain vehicle detection. Our approach improves the cross-domain vehicle detection model from image space and feature space. We employ objectives of the generative adversarial network and cycle consistency loss for image style transfer in image space. For feature space, we align feature distributions between the source domain and the target domain to improve the detection accuracy. Experiments are carried out using the method with two different datasets, proving that this technique effectively improves the accuracy of vehicle detection in the target domain.