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Abstract: A flexible structure with bonded piezoelectric sensors and actuators is considered as a smart structure. In this paper we introduce a new control criterion based on the absorption of [...]

The paper considers design of a predictive Linear Time Varying model-based controller with nonlinear feedforward for regulation of transient processes caused by setpoint step changes in a nonlinear plant. An optimal feedforward control sequence is computed based on an empirical Finite Impulse Response model of the process. Though the control techniques developed in this paper are meant to have more general industrial applicability a specific automotive engine control application--control of Variable Cam Timing automotive engine--is pursued. An advantage of the proposed controller design in this problem is that no first principle models are required. Instead, nonlinear parametric approximations of a neural network type are being used to describe and identify static nonlinear mappings encountered in the problem. A number of simplifying assumptions and approximations are made to make practical implementation of the proposed scheme possible. Validity of the designed controller is verified by simulation. The proposed 'model-free' design can potentially increase flexibility and save labor in development and deployment of such controllers for industrial systems. [DOI: 10.1115/1.1589029]

In this study, we elaborate on an important issue of membership function determination. The main point is that any membership estimation procedure should reconcile the semantics of a fuzzy set (regarded as an information granule arising at some level of information abstraction) with the experimental evidence conveyed by numeric data. This, in the sequel, calls for the development of the hybrid two-phase approach that starts from a rough specification of the support of the fuzzy set that is followed by detailed computations involving a specific type of membership function and an estimation of its parameters. The role of robust statistics in this setting is also raised. A number of experimental results are discussed. Index Terms--Energy and entropy measure of fuzziness, focal region, fuzzy sets, median, membership functions, robust estimator, triangular and parabolic membership functions.

In this paper, we introduce a model of generalization and specialization of information granules. The information granules themselves are modeled as fuzzy sets or fuzzy relations. The generalization is realized by or-ing fuzzy sets while the specialization is completed through logic and operation. These two logic operators are realized using triangular norms (that is t- and s-norms). We elaborate on two (top--down and bottom--up) strategies of constructing information granules that arise as results of generalization and specialization. Various triangular norms are experimented with and some conclusions based on numeric studies are derived. Index Terms--Abstraction, aggregation operators, fuzzy clustering, information granulation, information granules, OR and AND graphs, specialization, triangular norms.

The study introduces a concept of relevance of fuzzy mappings regarded as fundamental constructs of granular computing and rule-based systems, in particular. The notion of relevance of the fuzzy mappings is instrumental in the quantification of the quality of such mappings prior to their detailed construction. For the purposes of such quantification, we introduce shadowed sets and discuss as an algorithmic framework to be instrumental in expressing and quantifying the property of relevance of the fuzzy mappings. It is revealed that shadowed sets provide an interesting three-valued quantification of this property (such as acceptable mapping, marginal mapping, and a lack of mapping). The paper includes a number of detailed calculations concerning two commonly exploited classes of triangular and Gaussian fuzzy sets. Numerical studies are discussed as well. Index Terms--Fuzzy controller, fuzzy mapping, information granularity, linguistic information, relevance of mapping, shadow, shadowed sets.