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The stability of the mould level in the continuous casting machine is one of the main parameters affecting the surface quality of the final flat products (sheets & plates) in a steel factory. Variations in the mould level should be reduced to only some mm for improved steel quality, a specification which is often hard to fulfill with current standard PI(D) control. This paper introduces results of applying model based predictive control to the mould level process.

The purpose of this paper is to describe the role of non-integer models in healthcare pathways and how these tools provide a breakthrough in our understanding of drug delivery protocols. Two examples will be given as being typical of such applications: modeling glucose levels in diabetic type I patients and modeling pain transmission in patients suffering from (post operative) chronic pain. A wide range of tools to model these pathways and their role for use in healthcare delivery for optimal treatment and patient well-being are discussed. Individualized patient healthcare is nowadays top priority in European research framework for health and these modeling tools are primary aids in meeting these challenges.

In this paper, two methods to tune a fractional-order PI λ D μ controller for a mechatronic system are presented. The first method is based on a genetic algorithm to obtain the parameter values for the fractionalorder PI λ D μ controller by global optimization. The second method used to design the fractional-order PI λ D μ controller relies on an auto-tuning approach by meeting some specifications in the frequency domain. The real-time experiments are conducted using a Steward platform which consists of a table tilted by six servo-motors with a ball on the top of the table. The considered system is a 6 degrees of freedom (d.o.f.) motion platform. The feedback on the position of the ball is obtained from images acquired by a visual sensor mounted above the platform. The fractional-order controllers were implemented and the performances of the steward platform are analyzed.

Part 11: Engineering Applications of AI and Artificial Neural Networks; International audience; In a wet clutch system, a piston is used to compress the friction disks to close the clutch. The position and the velocity of the piston are the key effectors for achieving a good engagement performance. In a real setup, it is impossible to measure these variables. In this paper, we use transmission torque and slip to approximate the piston velocity and position information. By using this information, a process neural network is trained. This neural predictor shows good forecasting results on the piston position and velocity. It is helpful in designing a pressure profile which can result in a smooth and fast engagement in the future. This neural predictor can also be used in other model-based control techniques.

Modeling of hydraulic clutch transmissions is far from straightforward due to their nonlinear hybrid dynamics, i.e. switching between three dynamic phases. In this paper we identify a local linear model only for the constrained first phase, based on which a predictive controller is used to track a suitable engagement signal. The robustness of this controller in the latter two phases is guaranteed by making the constraints inactive and pre-tuning the control parameters based on its closed loop formulation and applying robust stability theorem. This controller is then implemented in real-time on a wet-clutch test setup and is shown to achieve optimal engagement.

Generally, one of the key requirements of modern buildings, besides an appropriate heating and ventilation, is to offer and maintain a suitable lighting. In this contribution, a description of a system capturing and re-directing sunlight into open spaces inside the building without affecting the inside temperature (isotherm) is given. The system mainly consists of three principal components: a mirror capturing the light of the sun and reflecting it inside the building via a prism system, a printed circuit board with two photovoltaic sensors for positioning the mirror, and finally, a motor rotating the printed circuit board and mirror. A closed-loop control system to track the movement of the sun is proposed. The results show good tracking behavior.

Part 2: Optimization-Genetic Algorithms; International audience; In many practical engineering applications, a feed-forward control is often used to control the system with some parameterized signals, for example, a wet clutch system. Usually these signals are designed empirically. In this paper, firstly, genetic algorithm (GA) will be used to optimize parameters. Then by knowing the system response of the test bench in the frequency domain, GA will be used again to fine tuning this parameterized signal. The result is then compared to those performances of using signal without fine tuning step. It is shown that after applying the fine tuning method, the resulted signal can achieve a better performance.

The Forced Oscillation Technique (FOT) denotes a non-invasive lima function test which serves as a medical diagnostic tool to measure human respiratory impedance. The FOT principle is based on superimposing air pressure oscillations onto the normal breathing waves of the subject, measuring both air flow and an pressure at the mouth and analyzing the data with signal processing techniques which apply to linear systems. The motivation to eliminate the need for flow measurement arises from i) economic reasons, because measurement of an flow in this case requires the presence of an expensive component, the pneumotachograph, in the FOT device and innovative aspects, The present work assessed the possibility that the requirement to measure flow could be eliminated if the transformation from excitation signal to measured pressure in the FOT device (given by the internal impedances of the device) is known. This conceptual solution was theoretically proven by analyzing the electrical circuit which models these transformations. Measurements were conceived and performed in order to estimate these quantities.

Carpet customers want a product of which the appearance lasts for years. Therefore, carpet manufacturers certify their products with labels that represent the expected change in appearance after the first year of installation. No automated system exists yet for objectively assigning these ranks. In this approach, we present an automated method for assessing carpet wear based on image analysis. For this, depth and intensity information are captured from eight types of carpet samples. The results show that the method correctly assigns wear labels from 1 to 5 in steps of 1 for six of the eight carpet types.

A novel 3D scanner based on structured light can extract depth information of floor coverings.

Looking back at the history of control engineering, one finds that technology and ideas combine themselves until they reach a successful result, over the timeline of several decades (Bernstein, 2002). It is such that before the computational advances during the so-called Information Age, a manifold of mathematical tools remained abstract and limited to theory. A recent trend has been observed in combining feedback control theory and applications with well-known, but scarcely used in practice, mathematical tools. The reason for the failure of these mathematical tools in practice was solely due to the high computational cost. Nowadays, this problem is obsolete and researchers have grasped the opportunity to exploit new horizons. During the development of modern control theory, it became clear that a fixed controller cannot provide acceptable closed-loop performance in all situations. Especially if the plant to be controlled has unknown or varying dynamics, the design of a fixed controller that always satisfies the desired specifications is not straightforward. In the late 1950s, this observation led to the development of the gain-scheduling technique, which can be applied if the process depends in a known or measurable way on some external, measurable condition (Ilchmann & Ryan, 2003). The drawback of this simple solution is that only static (steady state) variations can be tackled, so the need for dynamic methods of controller (re)tuning was justified. One can speak of three distinct features of the standard PID controller tuning: auto-tuning, gain scheduling and adaptation. Although they use the same basic ingredients, controller auto-tuning and gain scheduling should not be confused with adaptive control, which continuously adjusts controller parameters to accommodate unpredicted changes in process dynamics. There are a manifold of auto-tuning methods available in the literature, based on input-output observations of the system to be controlled (Bueno et al., 1991; Astrom & Hagglund, 1995; Gorez, 1997). The tuning methods can be classified twofold: • direct methods, which do not use an explicit model of the process to be controlled; these can then be either based on tuning rules (Astrom & Hagglund, 1995), either on iterative search methods (Astrom & Wittemark, 1995; Gorez, 1997). • indirect methods, which compute the controller parameters from a model of the process to be controlled, requiring the knowledge of the process model; these can be based on

Increased requirements of flexible production have led to the development of single-wafer processing equipment for integrated circuit fabrication. For commercially feasible throughput, it is substantial to minimize the process cycle time by heating only the wafer surface, in an extremely short time period. This is only possible using radiation heating, leading to RTP systems – Rapid Thermal Processing. Under such circumstances the system is no longer isothermal and temperature uniformity control becomes an issue of considerable concern and technical difficulty. Commercial RTCVD reactors (Rapid Thermal Chemical Vapor Deposition) have been in use for more than a decade, but the technology still suffers from some limitations [6]. One of these is the inability to achieve with commercial control equipment an adequate temperature uniformity across the wafer surface during the rapid heating phases (e.g. from room temperature up to 1100°C in the order of 1 minute). Deposition of silicon should be performed in a manner which minimizes crystalline growth defects, such as lattice slip. Such defects are induced by thermal gradients in the wafer during high temperature processing. For example, while gradients of about 100°C across a wafer may be tolerable at a process temperature of 800°C, respective gradients of only 2—3°C are allowable at process temperatures of 1100°C. Due to the radiant type of heating, these semiconductor reactors represent a highly nonlinear interactive multi-input multi-output system.

Optimal texture analysis algorithms for describing degra- dation of carpets are identified. Experimental design is applied to select from a set of texture analysis algorithms those optimal for iden- tifying texture changes due to degradation of carpets. The degree of wear of a degraded carpet is quantified by comparing its texture to the original texture. The set of texture algorithms is applied on intensity images obtained from the American and the European standards. The performance of the texture algorithms is evaluated using mea- sures that quantify characteristics in the relationship between the met- rics and the changes in texture. The statistical analysis of the experimental results shows that the local binary patterns algorithm is optimal in >50% of the cases, for describing degradation of the car- pets. Other texture algorithms that optimally characterize the degra- dation of carpets include the use of the power spectrum, Wigner

Textiles are mainly used for decoration and protection. In both cases, their original appearance and its retention are important factors for customers. Therefore, evaluation of appearance parameters are critical for quality assurance purposes, during and after manufacturing, to determine the lifetime and/or beauty of textile products. In particular, appearance retention of textile products is commonly certified with grades, which are currently assigned by human experts. However, manufacturers would prefer a more objective system. We present an objective system for grading appearance retention, particularly, for textile floor coverings. Changes in appearance are quantified by using linear regression models on texture features extracted from intensity and range images. Range images are obtained by our own laser scanner, reconstructing the carpet surface using two methods that have been previously presented. We extract texture features using a variant of the local binary pattern technique based on detecting those patterns whose frequencies are related to the appearance retention grades. We test models for eight types of carpets. Results show that the proposed approach describes the degree of wear with a precision within the range allowed to human inspectors by international standards. The methodology followed in this experiment has been designed to be general for evaluating global deviation of texture in other types of textiles, as well as other surface

A process control strategy based on adaptive control by means of a Finite Impulse Response (FIR) controller will be described. The method is called DIRAC (DIRect Adaptive Control). A simulation study indicates that this control algorithm leads to comparable performance characteristics as a well-tuned PID-type regulator. Moreover it has the advantage that it needs no tuning. The underlying adaptation technique is simple. As a special case a self-tuning PID-controller is obtained.

This paper discusses the derivation of a theoretical model of the movement of the shank, i.e. the lower part of the leg, around the knee joint. The objective is to control the angle of the shank with respect to the position of the upper leg. Subsequently, two types of design techniques are used to design a PID controller for the derived system: a model-based design method and an auto-tuner technique. The resulting PID controllers are compared by simulating a squat movement and a normal gait. We conclude that both PID controllers perform similar but the control effort for the auto-tuner technique presents a smoother course. However, the main advantage of the PID auto-tuner technique is that no mathematical transfer function model is necessary in order to tune the controller.

Production of biofuel has a positive environmental and economic impact; therefore, the interest for accurate modeling and more advanced control techniques has grown considerably over the last years. The reason is that it allows optimizing the productivity while the energy consumption is minimized. This paper presents the modeling, simulation and nonlinear control of a double-effect evaporation process to obtain bioethanol from sugarcane juice. This is achieved by controlling the juice concentration at the output of the last evaporator stage. Nonlinear Model Predictive Control (NMPC) has been successfully implemented, following the Extended Prediction Self-Adaptive Control (EPSAC) approach. This algorithm has been chosen among the different methods as it requires less computational load. The effectiveness of the proposed EPSAC controller is presented and compared against PI and Generalized Predictive Control (GPC), through simulation.