Identification of nonlinear additive FIR systems

In this paper, we consider identification of a nonlinear additive system. An input signal is designed in such a way that the problem of identification of nonlinear additive systems is reduced to a problem of identification of static nonlinear functions. Then, three approaches are established to estimate the order of the system. The methods exploit the structure of the nonlinear additive model so that their implementations are easy.     

Safe diagnosability for fault-tolerant supervision of discrete-event systems

The problem of achieving fault-tolerant supervision of discrete-event systems is considered from the viewpoint of safe and timely diagnosis of unobservable faults. To this end, the new property of safe diagnosability is introduced and studied. Standard definitions of diagnosability of discrete-event systems deal with the problem of detecting the occurrence of unobservable fault events using model-based inferencing from observed sequences of events. In safe diagnosability, it is required in addition that fault detection occur prior to the execution of a given set of forbidden strings in the failed mode of operation of the system. For instance, this constraint could be required to prevent local faults from developing into failures that could cause safety hazards. If the system is safe diagnosable, reconfiguration actions could be forced upon the detection of faults prior to the execution of unsafe behaviour, thus achieving the objective of fault-tolerant supervision. Necessary and sufficient conditions for safe diagnosability are derived. In addition, the problem of explicitly considering safe diagnosability in controller design, termed “active safe diagnosis problem”, is formulated and solved. A brief discussion of safe diagnosability for timed models of discrete-event systems is also provided.     

Identification of Hammerstein nonlinear ARMAX systems

Two identification algorithms, an iterative least-squares and a recursive least-squares, are developed for Hammerstein nonlinear systems with memoryless nonlinear blocks and linear dynamical blocks described by ARMAX/CARMA models. The basic idea is to replace unmeasurable noise terms in the information vectors by their estimates, and to compute the noise estimates based on the obtained parameter estimates. Convergence properties of the recursive algorithm in the stochastic framework show that the parameter estimation error consistently converges to zero under the generalized persistent excitation condition. The simulation results validate the algorithms proposed.     

On discrete dynamic output feedback min-max controllers

The paper considers output feedback min-max controllers for non-square discrete time uncertain linear systems. Based on previous work, it is demonstrated that static output feedback min-max controllers are only realizable for a specific class of systems. To broaden this class, a compensator based framework is proposed to introduce additional degrees of freedom. The conditions for the existence of such dynamic output feedback min-max controllers are given and are shown to be relatively mild. Furthermore, a simple parameterization of the available design freedom is proposed. An explicit procedure is described which shows how a Lyapunov matrix, which satisfies both a discrete Riccati inequality and a structural constraint, can be obtained using Linear matrix inequality optimization. This Lyapunov matrix is used to calculate the robustness bounds associated with the closed-loop system. A simple aircraft example is provided to demonstrate the efficacy of the design approach.     

Optimal regulation of homogeneous systems

We present an offline numerical algorithm to generate a discontinuous feedback law to robustly regulate the origin of a continuous-time homogeneous system through sample and hold. The proposed feedback comes out of the solution of an infinite horizon optimization problem in discrete time and is in the form of a look-up table. We show that when applied to either chained systems or systems in power form the algorithm results in a closed loop whose origin is globally exponentially stable.     

Design automation for customized apparel products

This paper presents solution techniques for a three-dimensional Automatic Made-to-Measure scheme for apparel products. Freeform surface is adopted to represent the complex geometry models of apparel products. When designing the complex surface of an apparel product, abstractions are stored in conjunction with the models using a non-manifold data structure. Apparel products are essentially designed with reference to human body features, and thus share a common set of features as the human model. Therefore, the parametric feature-based modeling enables the automatic generation of fitted garments on differing body shapes. In our approach, different apparel products are each represented by a specific feature template preserving its individual characteristics and styling. When the specific feature template is encoded as the equivalent human body feature template, it automates the generation of made-to-measure apparel products. The encoding process is performed in 3D, which fundamentally solves the fitting problems of the 2D tailoring and pattern-making process. This paper gives an integrated solution scheme all above problems. In detail, a non-manifold data structure, a constructive design method, four freeform modification tools, and a detail template encoding/decoding method are developed for the design automation of customized apparel products.     

3D virtual apparel design for industrial applications

The integration of physics-based models within CAD systems for garment design leads to highly accurate cloth shape results for virtual prototyping and quality evaluation tasks. To this aim, we present a physics-based system for virtual cloth design and simulation expressly conceived for design purposes. This environment should allow the designer to validate her/his style and design option through the analysis of garment virtual prototypes and simulation results in order to reduce the number and role of physical prototypes. Garment shapes are accurately predicted by including material properties and external interactions through a particle-based cloth model embedded in constrained Newtonian dynamics with collision management, extended to complex-shaped assembled and finished garments. Our model is incorporated within a 3D graphical environment, and includes operators monitoring the whole design process of apparel, e.g. panel sewing, button/dart insertion, multi-layered fabric composition, garment finishings, etc. Applications and case studies are considered, with analysis of CAD modelling phases and simulation results concerning several male and female garments.     

Deployment of an AEC industry sector product model

CIMsteel Integration Standard, Version 2 (CIS/2) is an industry-developed product model based on ISO-STEP technology that has been widely adopted within the steel construction industry. CIS/2 is an early success story of broad use of a product model for both data exchange and improving the productivity of those companies taking advantage of its capabilities. Here, we review the history of CIS/2, the methods and issues arising from its deployment, the benefits it has thus far realized and the research issues these activities have identified.