Sharif Digital Repository

Position control of Shape Memory Alloy (SMA) actuators has been a challenging topic during the last years due to their nonlinearities in the governing physical equations as well as their hysteresis behaviors. Using the inverse of phenomenological hysteresis model in order to compensate the input-output hysteresis behavior of these actuators shows the effectiveness of this approach. In this paper, in order to control the tip deflection of a large deformation flexible beam actuated by an SMA actuator wire, a feedforward-feedback controller is proposed. The feedforward part of the proposed control system, maps the beam deflection into SMA temperature, is based on the inverse of the generalized... 

This paper presents a new robust control strategy for an islanded microgrid in the presence of load unmodeled dynamics. The microgrid consists of parallel connection of several electronically interfaced distributed generation units and a local load. The load is parametrically uncertain and topologically unknown and, thus, is the source of unmodeled dynamics. The objective is to design a robust controller to regulate the load voltage in the presence of unmodeled dynamics. To achieve the objective, the problem is first characterized by a two-degree-of-freedom (2DOF) feedback-feedforward controller. The 2DOF control design problem is then transformed to a nonconvex optimization problem.... 

In this study, artificial neural network (ANN) was used to model the hot deformation behavior of 7075 aluminum alloy during compression test, in the strain rate range of 0.0003-1 s-1 and temperature range of 200-450 C. The inputs of the model were temperature, strain rate, and strain, while the output of the model was the flow stress. The feed-forward back-propagation network with two hidden layers was built and successfully trained at different deformation domains by Levenberg-Marquardt training algorithm. Comparative analysis of the results obtained from the hyperbolic sine, the power law constitutive equations, and the ANN shows that the newly developed ANN model has a better performance... 

Hysteresis and significant nonlinearities in the behavior of Shape Memory Alloy (SMA) actuators encumber effective utilization of these actuator. Due to these effects, the position control of SMA actuators has been a great challenge in recent years. Literature review of the research conducted in this area shows that using the inverse of the phenomenological hysteresis models can compensate the hysteresis of these actuators effectively. But, inverting some of these models, such as Preisach model, is numerically a complex task. However, the generalized Prandtl-Ishlinskii model is analytically invertible, and therefore can be implemented conveniently as a feedforward controller for compensating... 

Magnetorheological (MR) damper is a prominent semi-active control device to vibrate mitigation of structures. Due to the inherent non-linear nature of MR damper, an intelligent non-linear neuro-fuzzy control strategy is designed to control wave-induced vibration of an offshore steel jacket platform equipped with MR dampers. In the proposed control system, a dynamic-feedback neural network is adapted to model non-linear dynamic system, and the fuzzy logic controller is used to determine the control forces of MR dampers. By use of two feedforward neural networks required voltages and actual MR damper forces are obtained, in which the first neural network and the second one acts as the inverse... 

A method for noise reduction in active-$RC$ circuits is introduced. It is shown that the output noise in an active-$RC$ circuit can be considerably reduced, without disturbing the circuit transfer function by inserting appropriate passive or active components in the circuit. The inserted components introduce new signal paths in the circuit for noise reduction while the original circuit transfer function is kept unchanged. The procedure to define the proper paths in the circuit and their transfer functions is given. The effectiveness of the presented method is demonstrated using a second-order active-RC filter fabricated in a 0.18-$ {m}$ CMOS technology  

Crude oil density is an important thermodynamic property in simulation processes and design of equipment. Using laboratory methods to measure crude oil density is costly and time consuming; thus, predicting the density of crude oil using modeling is cost-effective. In this article, we develop a neural network-based model to predict the density of undersaturated crude oil. We compare our results with previous works and show that our method outperforms them  

The past few years have witnessed a growing rate of attraction in adoption of Artificial Intelligence (AI) techniques and combining them to improve forecasting accuracy in different fields. Besides, stock market forecasting has always been a subject of interest for most investors and professional analysts. Stock market forecasting is a tough problem because of the uncertainties involved in the movement of the market. This paper proposes a hybrid artificial intelligence model for stock exchange index forecasting, the model is a combination of genetic algorithms and feedforward neural networks. Actually it evolves neural network weights by using genetic algorithms. We also employ preprocessing... 

This study describes an adaptive sliding mode technique for attitude and position control of a rigid body insect-like flapping wing model in the presence of uncertainties. For this purpose, a six-degrees-of-freedom nonlinear and time-varying dynamic model of a typical hummingbird is considered for simulation studies. Based on the quasi-steady assumptions, three major aerodynamic loads including delayed stall, rotational lift and added mass are presented and analyzed, respectively. Using the averaging theory, a time-varying system is then transformed into the time-invariant system to design the adaptive controller. The controller is designed so that the closed-loop system will follow any... 

In this article, two adaptive model predictive controllers (AMPC) are applied to regulate the blood glucose in type 1 diabetic patients. The first controller is constructed based on a linear model, while the second one is designed by using a nonlinear Hammerstein model. The adaptive version of these control schemes is considered to make them more robust against model mismatches and external disturbances. The least squares method with forgetting factor is used to update the model parameters. For simulation study, two well-known mathematical models namely, Puckett and Hovorka which describe the dynamical behavior of patient's body have been selected. The performances and robustness of the... 

Remaining useful life prediction of rolling element bearings with offline condition monitoring data is the purpose of this paper. A data driven algorithm based on feedforward neural network is proposed for this aim. Since, usually the number of offline measurements are not much enough, the generalized Weibull failure rated function is used for producing the auxiliary points that are employed for training. Considering the physics of the bearing degradation, level of vibration in the highfrequency bandwidth of the spectrum is used as a feature and its performance in bearing prognostic problem is compared with that of using popular recommended features in the diagnostic standard. Bearing... 

The approximation power of general feedforward neural networks with piecewise linear activation functions is investigated. First, lower bounds on the size of a network are established in terms of the approximation error and network depth and width. These bounds improve upon state- of-the-art bounds for certain classes of functions, such as strongly convex functions. Second, an upper bound is established on the difference of two neural networks with identical weights but different activation functions. © The Author(s) 2018  

The process of local scour around bridge piers is fundamentally complex due to the three-dimensional flow patterns interacting with bed materials. For geotechnical and economical reasons, multiple pile bridge piers have become more and more popular in bridge design. Although many studies have been carried out to develop relationships for the maximum scour depth at pile groups under clear-water scour condition, existing methods do not always produce reasonable results for scour predictions. It is partly due to the complexity of the phenomenon involved and partly because of limitations of the traditional analytical tool of statistical regression. This paper addresses the latter part and... 

Artificial Neural Networks (ANNs) have recently been introduced as an efficient artificial intelligence modeling technique for applications involving a large, number of variables, especially with highly nonlinear and complex interactions among input/output variables in a system without any prior knowledge about the nature, of these, interactions. Various types of ANN models are developed and used for different problems. In this paper, an artificial neural network of the feed-forward back-propagation type has been applied for the prediction of density and compressive strength properties of the cement paste portion of concrete mixtures. The mechanical properties of concrete are highly...