Cleveland State University is a public university located in downtown Cleveland, Ohio. It was established in 1964 when the state of Ohio assumed control of Fenn College, and it absorbed the Cleveland-Marshall College of Law in 1969. Today it is part of the University System of Ohio and has approximately 16,000 students and over 100,000 alumni. Wikipedia.
Richter H.,Cleveland State University
Automatica | Year: 2011
This paper proposes a multi-regulator control scheme for single-input systems, where the setpoint of a regulated output must be changed under the constraint that a set of minimum-phase outputs remain within prescribed bounds. The strategy is based on a maxmin selector system frequently used in the aerospace field. The regulators used for the regulated and limited outputs are of the sliding mode type, where the sliding variable is defined as the difference between an output and its allowable limit. The paper establishes overall asymptotic stability, as well as invariance properties leading to limit protection. The design methodology is illustrated with a detailed simulation example on thrust control of a turbofan engine. © 2011 Elsevier Ltd. All rights reserved.
Gao Z.,Cleveland State University
ISA Transactions | Year: 2014
In this paper, it is shown that the problem of automatic control is, in essence, that of disturbance rejection, with the notion of disturbance generalized to symbolize the uncertainties, both internal and external to the plant. A novel, unifying concept of disturbance rejector is proposed to compliment the traditional notion of controller. The new controller-rejector pair is shown to be a powerful organizing principle in the realm of automatic control, leading to a Copernican moment where the model-centric design philosophy is replaced by the one that is control-centric in the following sense: the controller is designed for a canonical model and is fixed; the difference between the plant and the canonical model is deemed as disturbance and rejected. © 2013 ISA.
Simon D.,Cleveland State University
IET Control Theory and Applications | Year: 2010
The Kalman filter is the minimum-variance state estimator for linear dynamic systems with Gaussian noise. Even if the noise is non-Gaussian, the Kalman filter is the best linear estimator. For nonlinear systems it is not possible, in general, to derive the optimal state estimator in closed form, but various modifications of the Kalman filter can be used to estimate the state. These modifications include the extended Kalman filter, the unscented Kalman filter, and the particle filter. Although the Kalman filter and its modifications are powerful tools for state estimation, we might have information about a system that the Kalman filter does not incorporate. For example, we may know that the states satisfy equality or inequality constraints. In this case we can modify the Kalman filter to exploit this additional information and get better filtering performance than the Kalman filter provides. This paper provides an overview of various ways to incorporate state constraints in the Kalman filter and its nonlinear modifications. If both the system and state constraints are linear, then all of these different approaches result in the same state estimate, which is the optimal constrained linear state estimate. If either the system or constraints are nonlinear, then constrained filtering is, in general, not optimal, and different approaches give different results. © 2010 The Institution of Engineering and Technology.
Simon D.,Cleveland State University
Applied Soft Computing Journal | Year: 2011
We derive a dynamic system model for biogeography-based optimization (BBO) that is asymptotically exact as the population size approaches infinity. The states of the dynamic system are equal to the proportion of each individual in the population; therefore, the dimension of the dynamic system is equal to the search space cardinality of the optimization problem. The dynamic system model allows us to derive the proportion of each individual in the population for a given optimization problem using theory rather than simulation. The results of the dynamic system model are more precise than simulation, especially for individuals that are very unlikely to occur in the population. Since BBO is a generalization of a certain type of genetic algorithm with global uniform recombination (GAGUR), an additional contribution of our work is a dynamic system model for GAGUR. We verify our dynamic system models with simulation results. We also use the models to compare BBO, GAGUR, and a GA with single-point crossover (GASP) for some simple problems. We see that with small mutation rates, as are typically used in real-world problems, BBO generally results in better optimization results than GAs for the problems that we investigate. © 2011 Elsevier B.V. All rights reserved.
Talu O.,Cleveland State University
Journal of Physical Chemistry C | Year: 2013
Adsorption thermodynamics is based on Gibbs definition, which transforms the nonuniform interfacial region to a uniform three-phase system including a two-dimensional adsorbed phase on a hyper-surface. Gibbs definition is a pure mathematical construct applicable wherever the hyper-surface is located. On the other hand, physical quantification of adsorption and hence its applications require that the hyper-surface be located. Conceptually, the location of hyper-surface differentiates between so-called absolute, excess, and the recently introduced (Gumma and Talu, Langmuir2010, 26 (22), 17013-17023) net adsorption thermodynamic frameworks. This article details net adsorption thermodynamic framework for mixtures. In addition, a thermodynamic inconsistency is recognized in the calculation of grand potential (or solid chemical potential) with commonly used implementation of excess adsorption in literature. The inconsistency is shown to have a substantial impact on further thermodynamic calculations such as mixture adsorption predictions for even a simple typical example as oxygen-nitrogen-zeolite 5A system at 22 C and moderate pressures. Historically, this inconsistency seems to originate from adopting intuitive concepts for planar surfaces to microporous systems without regard to the differences in the physical nature of these two types of interfaces. Net adsorption framework circumvents the inconsistency as well as providing an unequivocal description of adsorption in micropores. © 2013 American Chemical Society.