Journal of Vibration and Control 

About: Journal of Vibration and Control is an academic journal published by SAGE Publishing. The journal publishes majorly in the area(s): Vibration & Nonlinear system. It has an ISSN identifier of 1077-5463. Over the lifetime, 4451 publications have been published receiving 72468 citations. The journal is also known as: JVC.

Dynamics and Control of Cranes: A Review:

Abstract: We review crane models available in the literature, classify them, and discuss their applications and limitations. A generalized formulation of the most widely used crane model is analyzed using th...

Entropy-Based Optimal Sensor Location for Structural Model Updating

Abstract: A statistical methodology is presented for optimally locating the sensors in a structure for the purpose of extracting from the measured data the most information about the parameters of the model used to represent structural behavior. The methodology can be used in model updating and in damage detection and localization applications. It properly handles the unavoidable uncertainties in the measured data as well as the model uncertainties. The optimality criterion for the sensor locations is based on information entropy, which is a unique measure of the uncertainty in the model parameters. The uncertainty in these parameters is computed by a Bayesian statistical methodology, and then the entropy measure is minimized over the set of possible sensor configurations using a genetic algorithm. The information entropy measure is also extended to handle large uncertainties expected in the pretest nominal model of a structure. In experimental design, the proposed entropy-based measure of uncertainty is also well-suited for making quantitative evaluations and comparisons of the quality of the parameter estimates that can be achieved using sensor configurations with different numbers of sensors in each configuration. Simplified models for a shear building and a truss structure are used to illustrate the methodology.

A Hamiltonian Formulation and a Direct Numerical Scheme for Fractional Optimal Control Problems

Abstract: This paper deals with a direct numerical technique for Fractional Optimal Control Problems (FOCPs). In this paper, we formulate the FOCPs in terms of Riemann—Liouville Fractional Derivatives (RLFDs). It is demonstrated that right RLFDs automatically arise in the formulation even when the dynamics of the system is described using left RLFDs only. For numerical computation, the FDs are approximated using the Grunwald—Letnikov definition. This leads to a set of algebraic equations that can be solved using numerical techniques. Two examples, one time-invariant and the other time-variant, are considered to demonstrate the effectiveness of the formulation. Results show that as the order of the derivative approaches an integer value, these formulations lead to solutions for integer order system. The approach requires dividing of the entire time domain into several sub-domains. Further, as the sizes of the sub-domains are reduced, the solutions converge to unique solutions. However, the convergence is slow. A sch...

Bayesian Updating and Model Class Selection for Hysteretic Structural Models Using Stochastic Simulation

Abstract: System identification of structures using their measured earthquake response can play a key role in structural health monitoring, structural control and improving performance-based design. Implementation using data from strong seismic shaking is complicated by the nonlinear hysteretic response of structures. Furthermore, this inverse problem is ill-conditioned for example, even if some components in the structure show substantial yielding, others will exhibit nearly elastic response, producing no information about their yielding behavior. Classical least-squares or maximum likelihood estimation will not work with a realistic class of hysteretic models because it will be unidentifiable based on the data. It is shown here that Bayesian updating and model class selection provide a powerful and rigorous approach to tackle this problem when implemented using a recently developed stochastic simulation algorithm called Transitional Markov Chain Monte Carlo. The updating and model class selection is performed on a previously-developed class of Masing hysteretic structural models that are relatively simple yet can give realistic responses to seismic loading. The theory for the Masing hysteretic models, and the theory used to perform the updating and model class selection, are presented and discussed. An illustrative example is given that uses simulated dynamic response data and shows the ability of the algorithm to identify hysteretic systems even when the class of models is unidentifiable based on the data.

Analog Fractional Order Controller in Temperature and Motor Control Applications

Abstract: An analog fractional order PIλ controller, using a circuit element with fractional order impedance, a Fractor (patent pending), is demonstrated in both a simple temperature control application and a more complex motor controller. The performance improvement over a standard PI controller was notable in both reduction of overshoot and decreased time to stable temperature, while retaining long term stability. In the motor controller, set point accuracy was considerably improved over conventional control. The modification of the standard controller to a fractional order controller was as simple as replacing the integrator capacitor with a Fractor. Mixing (i.e., hybridization) of digital and analog control was demonstrated.