Minimal Diagnosis and Diagnosability of Discrete-Event Systems Modeled by Automata

In the last several decades, the model-based diagnosis of discrete-event systems (DESs) has increasingly become an active research topic in both control engineering and artificial intelligence. However, in contrast with the widely applied minimal diagnosis of static systems, in most approaches to the diagnosis of DESs, all possible candidate diagnoses are computed, including nonminimal candidates, which may cause intractable complexity when the number of nonminimal diagnoses is very large. According to the principle of parsimony and the principle of joint-probability distribution, generally, the minimal diagnosis of DESs is preferable to a nonminimal diagnosis. To generate more likely diagnoses, the notion of the minimal diagnosis of DESs is presented, which is supported by a minimal diagnoser for the generation of minimal diagnoses. Moreover, to either strongly or weakly decide whether a minimal set of faulty events has definitely occurred or not, two notions of minimal diagnosability are proposed. Necessary and sufficient conditions for determining the minimal diagnosability of DESs are proven. The relationships between the two types of minimal diagnosability and the classical diagnosability are analysed in depth

Decentralized Adaptive Fuzzy Control for a Class of Large-Scale Mimo Nonlinear Systems with Strong Interconnection and Its Application to Automated Highway Systems

In the previous work of Huang et al., a decentralized adaptive fuzzy controller of large-scale multiple-input multiple-output nonlinear systems is obtained predicated upon the assumption that the interconnections between subsystems can be bounded by first-order polynomials. In this note, we focus in the absence of the conservative assumption upon developing a novel decentralized adaptive fuzzy control scheme. In virtue of fuzzy systems and a regularized inverse matrix, the developed control scheme not only addresses controller singularity under an overall design framework but also copes with interconnections with arbitrary nonlinear bounds. The resulting closed-loop large-scale system is proved to be asymptotically stable. The controller design is applicable to an automated highway system, and simulation results confirm its practical usefulness

Fuzzy Sets Applications in Civil Engineering Basic Areas

Civil engineering is a professional engineering discipline that deals with the design, construction, and maintenance of the physical and naturally built environment, including works like roads, bridges, canals, dams, and buildings. This paper presents some Fuzzy Logic (FL) applications in civil engeering discipline and shows the potential of facilities of FL in this area. The potential role of fuzzy sets in analysing system and human uncertainty is investigated in the paper. The main finding of this inquiry is FL applications used in different areas of civil engeering discipline with success. Once developed, the fuzzy logic models can be used for further monitoring activities, as a management tool