No abstract

The estirrm tion o/ reservoir properties is inherently an underdetemrined p-ob[em (one having a nonunique solution)because of the large number of unknown parameters relative to the available data.

As the interest in the seismic design of structures has increased considerably over the past few years, accurate predictions of the dynamic responses of soil and structural systems has become necessary. Such predictions require a knowledge of the dynamic properties of the systems under consideration. This paper is concerned with the uniqueness of the results in the identification of such properties. More specifically, the damping and stiffness distributions, which are of importance in the linear range of response, have been investigated. An N-storied structure or an N-layered soil medium is modeled as a coupled, N-degree-of-freedom, lumped system consisting of masses, springs, and dampers. Then, assuming the mass distribution to be known, the problem of identification consists of determining the stiffness and damping distributions from the knowledge of the base excitation and the resulting response at any one mass level. It is shown that if the response of the mass immediately above the base is known, the stiffness and damping distributions can be uniquely determined. Following this, some nonuniqueness problems have been discussed in relation to the commonly used ideas of system reduction in the study of layered soil media. A numerical example is provided to verify some of these concepts and the nature of nonuniqueness of identification is indicated by showing how two very different (yet physically reasonable) systems could yield identical excitation-response pairs. Errors in the calculation of the dynamic forces, due to erroneous identification have also been illustrated thus making the results of the present study useful from the practical standpoint of the safe design of structures to ground shaking.

The problem of optimally positioning sensors in lumped and distributed parameter dynamic systems for the purpose of system identification from time-domain input-output data is formulated and a methodology for its solution is presented. A linear relation between small perturbations in a finite-dimensional representation of the system parameters and a finite sample of observations of the system time response is used to determine approximately the covariance of the parameter estimates. The locations of a given number of sensors are then determined such that a suitable norm of the covariance matrix is minimized. The methodology is applied to the problem of optimally locating a single sensor in a building structure modeled by a shear beam, such that the estimates of the stiffness distributions, obtained from the records of strong ground shaking and the building response at the sensor location, are least uncertain.

The power electronics industry is undergoing a revolution driven by an industry 4.0 perspective, with smart and green/hybrid energy management systems being the requirement of the future. There is a need to highlight the potential of fractional order control in power electronics for the highly efficient systems of tomorrow. This paper reviews the developments in fractional order control in power electronics ranging from stand-alone power converters, industrial drives and electric vehicles to renewable energy systems and management in smart grids and microgrids. Various controllers used in power electronics such as the fractional order PI/PID (FOPI/FOPID) and fractional-order sliding mode controllers have been discussed in detail. This review indicates that the plug-and-play type of intelligent fractional order systems needs to be developed for our sustainable future. The review also points out that there is tremendous scope for the design of modular fractional-order intelligent controllers. Such controllers can be embedded into power converters, resulting in smart power electronic systems that contribute to the faster and greener implementation of industry 4.0 standards. INDEX TERMS Fractional calculus, power electronic converters, fractional order control, industrial drives, electric vehicles, renewable energy applications, smart grids and microgrids, industry 4.0.