Single and multistate integral friction models

In this note, a single state integral friction model is first proposed, defining separately the Dahl, Stribeck, and microviscous effects. The model is based on an integral closed form solution of the Dahl model. A multistate integral friction model, accounting for the hysteresis behavior with non local memory, has been then derived, combining the Dahl and the Maxwell slip model. The accuracy of the model has been experimentally assessed in friction compensation, both in open and in closed velocity loop.     

Hybrid FEM/HTA analysis of friction contact in elastic and elastoplastic plane stress problems

The Hopfield–Tank neural network evolutionary equations (called for short HTA – HT Analog) were modified for the analysis of friction contact interface. The modified HTA was used for modeling a function in the hybrid program FEM/HTA and applied to the analysis of two numerical examples. The first example, taken from [Mistakidis ES, Panagiotopoulos PG. Numerical treatment of the nonmonotone (zig-zag) friction and adhesive contact problems with debounding: approximation by monotone subproblems. Comput Struct 1993;(47):33–46], is related to the analysis of nonmonotone (zig-zag) contact of elastic strips. The second example concerns the tension of elastoplastic strip with a rigid bolt inserted into a circular hole of the strip. The example generalizes the analysis performed in [Pabisek E. Neural analysis of elastoplastic plane stress problem with unilateral constraints. Comput Assis Mech Eng Sci, in press], considering Coulomb’s friction contact in the bolt–hole inference.     

Contact problems with friction: models and simulations

This article deals with the modeling and the numerical simulation of contact and friction problems. Some friction formulations are proposed and numerical methods of resolution by finite elements are presented. Two methods are more particularly developed. The first one is based on a formulation using displacements and the second one is based on a mixed formulation using displacements and contact forces. Then, some improvements of these two methods as well as a comparison of their respective performances are presented.     

Identification of discrete-time state affine state space models using cumulants

This paper is concerned with the identification of discrete-time, time invariant, state affine state space models driven by an independent identically distributed (IID) random input, and in the presence of process and measurement noise. The identification problem is treated using a cumulant based approach. It is shown that the input-output and input-state crosscumulant equations in the time domain have the form of a linear autonomous system. An algorithmic procedure is then developed, for the computation of the unknown system matrices, based on a standard deterministic linear subspace identification algorithm, provided the input signal has some persistent excitation properties. The special case of Gaussian IID input is also examined. The proposed method is computationally very efficient and its accuracy is illustrated by simulations.     

Test selection based on finite state models

A method for the selection of appropriate test case, an important issue for conformance testing of protocol implementations as well as software engineering, is presented. Called the partial W-method, it is shown to have general applicability, full fault-detection power, and yields shorter test suites than the W-method. Various other issues that have an impact on the selection of a suitable test suite including the consideration of interaction parameters, various test architectures for protocol testing and the fact that many specifications do not satisfy the assumptions made by most test selection methods (such as complete definition, a correctly implemented reset function, a limited number of states in the implementation, and determinism), are discussed     

Unique state variable models for linear systems

A now unique canonical form for minimal state variable realizations of transfer function matrices ia presented.     

Robust stability in linear state space models

A new sufficient condition is presented for the stability of interval matrices based on Kharitonov's theorem (1978).     

Solving the elastoplastic unloading problems of an axisymmetric body with actual material hardening models by BEM

This paper presents a boundary element formulation and numerical implementation of the elastoplastic unloading problem of an axisymmetric body with an actual material hardening model in which the reverse yield stress does not lie between the kinematic hardening yield stress and the isotropic hardening yield stress of present plastic theory. Three elastoplastic unloading stress-strain models of high strength low alloy steel with both strain hardening and Bauschinger effect factor are discussed. The modified Aitken method is used for the unloading iteration process. Comparison of the results of this paper with the data of FEM and experiment is given finally.     

Single users' affective responses models for product form design

This paper presents a neural network based approach to modeling consumers' affective responses for product form design. A theoretical framework for a single user's perception is developed. On the basis of this theoretical framework, a mathematical model which enables single users' responses to different products to be predicted was developed. The results obtained show that the mathematical models developed achieved highly accurate predictions.For the purpose of obtaining a global model various individual mathematical models were created, which were based on the opinions of users representing different groups of opinion. The results suggest that, under some conditions, the combined use of various models of individual users can perform as well as a single model generated on the basis of mean market responses.     

Delta sum state estimation for discrete-time dynamical models

Optimal recursive estimates are derived for a class of non-Gaussian nonlinear systems using a discrete approximation of the probability density function of the measurement noise.     

Efficient Bayesian estimation of multivariate state space models

A Bayesian Markov chain Monte Carlo methodology is developed for the estimation of multivariate linear Gaussian state space models. In particular, an efficient simulation smoothing algorithm is proposed that makes use of the univariate representation of the state space model. Substantial gains over existing algorithms in computational efficiency are achieved using the new simulation smoother for the analysis of high dimensional multivariate time series.The methodology is used to analyse a multivariate time series dataset of the Normalised Difference Vegetation Index (NDVI), which is a proxy for the level of live vegetation, for a particular grazing property located in Queensland, Australia.     

Nonlinear state estimation using fuzzy local linear models

A local modelling-based approach to nonlinear state estimation using a Sugeno fuzzy inference framework is presented. Four new fuzzy Kalman filters are proposed on this basis, and simulation results presented suggest potential improvements when compared with conventional extended Kalman filtering.     

Test generation from state based use case models

We present a strategy for the automatic generation of test cases from parametrised use case templates that capture control flow, state, input and output. Our approach allows test scenario selection based on particular traces or states of the model. The templates are internally represented as CSP processes with explicit input and output alphabets, and test generation is expressed as counter-examples of refinement checking, mechanised using the FDR tool. Soundness is addressed through an input–output conformance relation formally defined in the CSP traces model. This purely process algebraic characterisation of testing has some potential advantages, mainly an easy automation of conformance verification and test case generation via model checking, without the need to develop any explicit algorithm.     

Classical state space realizability of input-output bilinear models

This paper studies the realizability property of bilinear input-output (i/o) models in the classical state space form. Constraints on the parameters of the bilinear i/o model are suggested that lead to realizable models. The complete list of 2nd and 3rd order realizable input-output bilinear models together with the corresponding state equations is given. In the general case some subclasses of realizable bilinear models together with their state-space realizations are presented, including the diagonal bilinear model and the special subclass of the superdiagonal bilinear model.     

Hidden Markov models with arbitrary state dwell-time distributions

A hidden Markov model (HMM) with a special structure that captures the ‘semi’-property of hidden semi-Markov models (HSMMs) is considered. The proposed model allows arbitrary dwell-time distributions in the states of the Markov chain. For dwell-time distributions with finite support the HMM formulation is exact while for those that have infinite support, e.g. the Poisson, the distribution can be approximated with arbitrary accuracy. A benefit of using the HMM formulation is that it is easy to incorporate covariates, trend and seasonal variation particularly in the hidden component of the model. In addition, the formulae and methods for forecasting, state prediction, decoding and model checking that exist for ordinary HMMs are applicable to the proposed class of models. An HMM with explicitly modeled dwell-time distributions involving seasonality is used to model daily rainfall occurrence for sites in Bulgaria.     

On Petri net models of infinite state supervisors

A class of supervisory control problems that require infinite state supervisors is considered, and Petri nets with inhibitor arcs (PNs) are introduced to model the supervisors. This PN-based approach to supervisory control is compared to automata-based approaches. The primary advantage of a PN-based supervisory controller is that it provides a finite representation of an infinite state supervisor. For verification, implementation, and testing reasons, a finite PN-based representation of an infinite state supervisor is preferred over an automata-based supervisor. It is shown that this modeling advantage is accompanied by a decision disadvantage, in that in general the controllability of a language that can be generated by the closed-loop system is undecidable     

FE-shape sensitivity of elastoplastic response

A new approach for performing FE-shape design sensitivity analyses (DSA) of structural models with both linear elastic and elastoplastic material behaviour is presented. In the formulation of the method the derivation of the FE equilibrium equations is performed analytically leading to various terms. The differentiation of some parts of these terms is determined numerically, therefore the method is semianalytical. The formulation is particularized for isoparametric finite elements for which exact numerical differentiation can be obtained (exact up to round-off error). Examples of some plane stress problems testify that the results of the new method are not dependent either on the size of perturbation of the design variables or on the FE mesh refinement among other factors.     

Minimax FIR smoothers for deterministic continuous-time state space models

In order to design a smoother for a deterministic continuous-time state space model, a new performance criterion is proposed, which is given by a ratio of the current estimation error to the weighted energy of the deterministic disturbance applied during the recent finite horizon. Among smoothers with the deadbeat property and finite impulse response (FIR) structure, a minimax FIR smoother (MFS) is obtained to optimize the proposed performance criterion. To begin with, the functional optimization problem is formulated with respect to kernel functions of the MFS and then its solution is explicitly presented. The MFS depends only on inputs and outputs on the finite recent horizon, and is independent of any a priori state information. The MFS is first represented in an integral form for simple representation and then a differential form is introduced for efficient numerical computation. As in H_∞ IIR smoothing and H₂ IIR filtering, it is shown that the proposed MFS for a deterministic system can be interpreted as the minimum variance unbiased FIR smoother for a stochastic system.