Browsing Engineering and Informatics by Subject "; Algorithm"
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Design of full electric power steering with enhanced performance over that of hydraulic power-assisted steeringThis paper presents a method of designing a full electrical power steering system to replace a hydraulic power-assisted steering system with improved performance and benefits including energy saving, improved steering 'feel', simpler construction and environmental gain. The designed performance of the electrical power steering system represented an ideal hydraulic power-assisted steering power boost curve which was mathematically modelled to provide the required control characteristic for the electrical power steering system, including variation in the perceived power assistance with the vehicle's forward speed. A full electrical power steering system provides all the torque necessary to steer the wheels, and the steering feel is artificially generated by an electric 'feedback' motor which provides resistance to the driver's input. The performance of the electrical power steering system described in this paper was enhanced by manipulating the reactive torque to the driver's input at the steering wheel so that it depended upon the driving conditions. Full-vehicle software models were generated using ADAMS/car software based on an actual car fitted with hydraulic power-assisted steering and full electrical power steering. The simulation results from both models were compared, and it is concluded that the steering performances of both systems were similar but the steering feel of the full electrical power steering system could be tuned to provide improved feedback to the driver in use. The performance of the full electrical power steering system could be further improved with the introduction of a controller to manipulate the steering feel during undesired conditions.
Evolutionary membrane computing: A comprehensive survey and new resultsEvolutionary membrane computing is an important research direction of membrane computing that aims to explore the complex interactions between membrane computing and evolutionary computation. These disciplines are receiving increasing attention. In this paper, an overview of the evolutionary membrane computing state-of-the-art and new results on two established topics in well defined scopes (membrane-inspired evolutionary algorithms and automated design of membrane computing models) are presented. We survey their theoretical developments and applications, sketch the differences between them, and compare the advantages and limitations. (C) 2014 Elsevier Inc. All rights reserved.
Low-cost process monitoring for polymer extrusionPolymer extrusion is regarded as an energy-intensive production process, and the real-time monitoring of both energy consumption and melt quality has become necessary to meet new carbon regulations and survive in the highly competitive plastics market. The use of a power meter is a simple and easy way to monitor energy, but the cost can sometimes be high. On the other hand, viscosity is regarded as one of the key indicators of melt quality in the polymer extrusion process. Unfortunately, viscosity cannot be measured directly using current sensory technology. The employment of on-line, in-line or off-line rheometers is sometimes useful, but these instruments either involve signal delay or cause flow restrictions to the extrusion process, which is obviously not suitable for real-time monitoring and control in practice. In this paper, simple and accurate real-time energy monitoring methods are developed. This is achieved by looking inside the controller, and using control variables to calculate the power consumption. For viscosity monitoring, a 'soft-sensor' approach based on an RBF neural network model is developed. The model is obtained through a two-stage selection and differential evolution, enabling compact and accurate solutions for viscosity monitoring. The proposed monitoring methods were tested and validated on a Killion KTS-100 extruder, and the experimental results show high accuracy compared with traditional monitoring approaches.