Viscoelasticity of polyethylene/montmorillonite nanocomposite melts

Observations are reported on low-density polyethylene melts reinforced with montmorillonite nanoclay at concentrations of filler ranging from 0 to 10 wt.% in small-amplitude shear oscillatory tests, start-up tests with a constant strain rate, and relaxation tests. Constitutive equations are derived for the time-dependent response of a nanocomposite melt at three-dimensional deformations with finite strains. The model accounts for (i) inhomogeneity in the distribution of nanoparticles, (ii) non-affinity of an equivalent polymer network with sliding junctions, and (iii) evolution of energies of inter-chain interaction driven by orientation of clay platelets. It is demonstrated that the stress–strain relations correctly describe the experimental data and adjustable parameters change consistently with nanoclay content.     

A multi-scale template method for shape detection with bio-medical applications

In this paper we present a novel methodology based on non-parametric deformable prototype templates for reconstructing the outline of a shape from a degraded image. Our method is versatile and fast and has the potential to provide an automatic procedure for classifying pathologies. We test our approach on synthetic and real data from a variety of medical and biological applications. In these studies it is important to reconstruct accurately the shape of the object under investigation from very noisy data. Here we assume that we have some prior knowledge about the object outline represented by a prototype shape. Our procedure deforms this shape by means of non-affine transformations and the contour is reconstructed by minimizing a newly developed objective function that depends on the transformation parameters. We introduce an iterative template deformation procedure in which the scale of the deformation decreases as the algorithm proceeds. We compare our results with those from a Gaussian Mixture Model segmentation and two state-of-the-art Level Set methods. This comparison shows that the proposed procedure performs consistently well on both real and simulated data. As a by-product we develop a new filter that recovers the connectivity of a shape.

Simplex sliding mode methods for the chattering reduction control of multi-input nonlinear uncertain systems

The simplex sliding mode control method is further developed by considering uncertain control systems non-affine in the control law. In order to reduce chattering effects, a set of integrators is added in the input channels. The augmented system is then controlled by a switching logic based on the simplex control method. As a result, the original control vector turns out to be continuous. A second order sliding mode observer is used when the sliding output is not available. Explicit conditions are identified about systems uncertainties and the simplex geometry in order to guarantee the convergence of the proposed methodology.     

Elastic energy in microscopically phase-separated swollen polymer networks

The paper analyzes a microscopic regime of strain, different from the one conventionally considered, that presumably takes place in swollen polymers showing strong microscopic phase separation, such as ion-exchange resins in water. Such systems show linear dependence of the elastic pressure on swelling in contrast to the Flory-Rehner theory and its modifications. The present work proposes a simple model that predicts this kind of behavior. Swelling is considered as a non-affine ‘inflation’ of the hydrophobic matrix by small aggregates of water molecules (‘droplets’) adsorbed by highly hydrophilic groups, whereas the macroscopic dimensions of the sample change as a result of the compression of the ‘films’ separating the droplets. This compression is then analyzed along the classical lines. In the case of the Dowex resins a partial test of the model based on the reported shear moduli showed reasonable agreement with experiment.     

Nonlinear adaptive switching control for a class of non-affine nonlinear systems

An improved nonlinear adaptive switching control method is presented to relax the assumption on the higher order nonlinear terms of a class of discrete-time non-affine nonlinear systems. The proposed control strategy is composed of a linear adaptive controller, a neural network (NN) based nonlinear adaptive controller and a switching mechanism. An incremental model is derived to represent the considered system and an improved robust adaptive law is chosen to update the parameters of the linear adaptive controller. A new performance criterion of the switching mechanism is designed to select the proper controller. Using this control scheme, all the signals in the system are proved to be bounded. Numerical examples verify the effectiveness of the proposed algorithm.     

RBFN-based decentralized adaptive control of a class of large-scale non-affine nonlinear systems

For a class of large-scale decentralized nonlinear systems with strong interconnections, a radial basis function neural network (RBFN) adaptive control scheme is proposed. The system is composed of a class of non-affine nonlinear subsystems, which are implicit function and smooth with respect to control input. Based on implicit function theorem, inverse function theorem and the design idea of pseudo-control, a novel control algorithm is proposed. Two neural networks are used to approximate unknown nonlinearities in the subsystem and unknown interconnection function, respectively. The stability is proved rigidly. The result of simulation validates the effectiveness of the proposed scheme.     

An ISS-modular approach for adaptive neural control of pure-feedback systems

Controlling non-affine non-linear systems is a challenging problem in control theory. In this paper, we consider adaptive neural control of a completely non-affine pure-feedback system using radial basis function (RBF) neural networks (NN). An ISS-modular approach is presented by combining adaptive neural design with the backstepping method, input-to-state stability (ISS) analysis and the small-gain theorem. The difficulty in controlling the non-affine pure-feedback system is overcome by achieving the so-called “ISS-modularity” of the controller-estimator. Specifically, a neural controller is designed to achieve ISS for the state error subsystem with respect to the neural weight estimation errors, and a neural weight estimator is designed to achieve ISS for the weight estimation subsystem with respect to the system state errors. The stability of the entire closed-loop system is guaranteed by the small-gain theorem. The ISS-modular approach provides an effective way for controlling non-affine non-linear systems. Simulation studies are included to demonstrate the effectiveness of the proposed approach.     

Adaptive thermal control for PEMFC systems with guaranteed performance

Proton exchange membrane fuel cell (PEMFC) s are faced with dynamical load scenario in practical applications, and the resulting temperature variation will decrease the performance and consequently shorten the fuel cell lifetime. To address this problem, a control strategy for regulating the stack temperature is proposed in this paper. Firstly, a thermal management-oriented dynamic model of a water-cooled PEMFC system is built to facilitate the control design. Secondly, considering that the stack temperature should be maintained in a certain range regardless of the dynamical changing current demand, a Barrier Lyapunov function is employed to construct a feedback error of the stack temperature. Thirdly, a set of adaptation laws is designed to estimate the unknown parameters related to the gas flow rates in the flow fields. Particularly, a dynamic inversion tracking methodology is applied to design the non-affine input. A Lyapunov method based analysis demonstrates the stability and convergence of the closed-loop properties. Simulation results are provided to show that the proposed control strategy can satisfy all the control objectives and enhance the control performance compared to the proportional-integral controlled case.     

Improved prescribed performance control for air-breathing hypersonic vehicles with unknown deadzone input nonlinearity

An improved prescribed performance controller is proposed for the longitudinal model of an air-breathing hypersonic vehicle (AHV) subject to uncertain dynamics and input nonlinearity. Different from the traditional non-affine model requiring non-affine functions to be differentiable, this paper utilizes a semi-decomposed non-affine model with non-affine functions being locally semi-bounded and possibly in-differentiable. A new error transformation combined with novel prescribed performance functions is proposed to bypass complex deductions caused by conventional error constraint approaches and circumvent high frequency chattering in control inputs. On the basis of backstepping technique, the improved prescribed performance controller with low structural and computational complexity is designed. The methodology guarantees the altitude and velocity tracking error within transient and steady state performance envelopes and presents excellent robustness against uncertain dynamics and deadzone input nonlinearity. Simulation results demonstrate the efficacy of the proposed method.     

Tracking control of air-breathing hypersonic vehicles with non-affine dynamics via improved neural back-stepping design

This study considers the design of a new back-stepping control approach for air-breathing hypersonic vehicle (AHV) non-affine models via neural approximation. The AHV's non-affine dynamics is decomposed into velocity subsystem and altitude subsystem to be controlled separately, and robust adaptive tracking control laws are developed using improved back-stepping designs. Neural networks are applied to estimate the unknown non-affine dynamics, which guarantees the addressed controllers with satisfactory robustness against uncertainties. In comparison with the existing control methodologies, the special contributions are that the non-affine issue is handled by constructing two low-pass filters based on model transformations, and virtual controllers are treated as intermediate variables such that they aren't needed for back-stepping designs any more. Lyapunov techniques are employed to show the uniformly ultimately boundedness of all closed-loop signals. Finally, simulation results are presented to verify the tracking performance and superiorities of the investigated control strategy.