Contribution to the decoupled control of large-scale mechanical systems

This paper presents the concept of decomposition as applied to the control synthesis of multivariable mechanical systems, in which the coupling among the subsystems can be very strong. The system class considered contains subsystems with powered degrees of freedom, as well as subsystems without their own actuators. Global control is introduced to diminish the influence of coupling on the stability of the whole system. The procedure is illustrated by simulation of two examples, one in robotics, the other in biomechanics.     

Surface and crystalline analysis of aluminum oxide single crystal treated by quasistationary compression plasma flow

Material such as aluminum oxide (Al₂O₃) is important in electronics industry. On the other hand, plasma is one of the most efficient and sophisticated tools for materials processing. In this work a treatment of Al₂O₃ (0001) surface by quasistationary compression plasma flow (CPF) is analyzed in detail. Offline metrology was performed using dielectric measurements, X-ray diffraction (XRD), scanning electron microscopy (SEM) equipped with energy-dispersive X-ray spectroscopy (EDX) and atomic force microscopy (AFM). Oriented low-dimensional periodic structures are occurred for the plasma treated Al₂O₃ single crystal. In the paper is reported that these oriented ripple-shaped structures contain a higher percentage of oxygen than the surrounding crystal surface. This could be the framework for usage of CPF as a tool in manufacturing of surfaces containing the highly organized oxygen-rich structures.     

Particle Swarm Optimization and Pure Adaptive Search in Finish Goods’ Inventory Management

Abstract

Inventory management deals with a tradeoff between the benefits of keeping stocks of goods that allows fulfillment of the customer’s demand, and the cost of carrying inventory. Inventory control techniques are very important components and the most organizations can substantially reduce their costs associated with the flow of materials. This paper presents new inventory management model based on particle swarm optimization and pure adaptive search global optimization algorithm in production-inventory system. The proposed model is focusing on planned level of demand for finished goods, production and raw materials cost, production capacity as the norm, change of the production cost and inventory capital cost, all of which are typical factors in automobile manufacture industry. The model determines different factors such as the minimizing inventory quantity, minimizing inventory value, and minimizing production cost based on demand for production items. The model is tested with original real-world dataset obtained from the automotive company Lear from US and its factory in Novi Sad, Serbia.     

Optimization of the Temperature-Time Curve for the Curing Process of Thermoset Matrix Composites

An intelligent optimization model aiming at off-line or pre-series optimization of the thermal curing cycle of polymer matrix composites is proposed and discussed. The computational procedure is based on the coupling of a finite element thermochemical process model, dynamic artificial neural networks and genetic algorithms. Objective of the optimization routine is the maximization of the composite degree of cure by the definition of the autoclave temperature. Obtained outcomes evidenced the capability of the method as well as its efficiency with respect to hard computing or experimental procedures.     

DESIGN OF SUPERVISORY CONTROL FUNCTIONS BASED ON FEEDFORWARD NEURAL NETWORKS

This paper presents the results of the research concerning possibilities of applying artificial neural networks for fault diagnosis, state estimation, and prediction in the gas pipeline transmission network. The influence of several factors on accuracy of the feedforward neural networks so applied was considered. The emphasis was put on neural network's function as state estimator. The choice of the most informative features, the amount and sampling period of training data sets, as well as different configurations of neural networks were analyzed.     

Prediction of traditionally utilised wheat dough technological quality parameters from Mixolab values: development and evaluation of regression models

The research was conducted with the aim to investigate the possibility of Farinograph, Extensograph and Amylograph values prediction with linear and/or multiple Mixolab regression models. In total, 163 flour samples were divided based on Machalanobis distances into three sets: prediction, validation and external sample set. Determination coefficients ranged from 0.145 to 0.640 for linear regression models and from 0.279 to 0.739 for multiple regression models. Internal and external validation of developed regression models was conducted. Testing of developed models resulted in a high share of samples for which predicted values were out of the ranges of method official reproducibility and a high share of samples for which predicted values were out of the quality level range obtained by analytical measurement. It was concluded that it is impossible to develop applicable regression models for prediction of Farinograph, Extensograph and Amylograph parameters on the basis of Mixolab values from standard protocol.