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Integrated automation in a manufacturing plant demands control of manufacturing and material and product flow processes. Efficient data archiving and retrieval, quality control and process automation facilities are essential components which should be deployed to form one coherent system. As managers of extruder plants running look for ways and means for meeting market requirements, the demand for such integrated automation systems is rising. Integrated Process automation involves: - Overall production and process control - Data acquisition, archiving evaluation with feed-back to production planning and process control - Decentral control in local control loops for achieving isothermal extrusion - On-line visualisation of process variables for tighter man-machine interaction Providing the facility for prescribing the ranges of relevant process variables viz. billet temperature, die temperature, profile temperature at die exit, rate of cooling of the profile and extrusion speed is part of the integrated automation system. At the heart of the system are processes for mastering the core task of management of the thermal processes in the extruder by employing sensors and control techniques. Goal is to minimise the time to extrude a billet and simultaneously keep the process variables within prescribed limits. Automation in extrusion plants is made possible by progress in sensor technology and enhanced possibilities of data processing and organisation, advanced control methods which require simple models and facilities of integration of hardware and software employing state-of-the-art networking and protocols. Crucial is the non-contact temperature measurement of the billet and the profile at die exit. Since about two decades there has been a continuous progress in temperature measurement technology based on multi-spectral radiation pyrometers. They perform well for a large number of alloys and surface finishes and are reliable for industrial use. Disturbances can be suppressed using adequate signal processing

In this paper, we consider the convergence and robustness of a general iterative learning control scheme for a class of systems which we term “strongly positive”. The analysis is made in the framework of Hilbert-space theory. Thus the results are valid for discrete-time as well as continuous-time systems which may be time-variant or time-invariant. For the special case of continuous linear time-invariant systems which are defined over the Hilbert-space of square integrable functions, we will give a characterization of strongly positive systems in the frequency domain.

Iterativ lernende Regelungen eignen sich zur Beherrschung zyklischer Prozesse mit nichtverschwindenden Zwischenzyklenzeiten. Bei den Semibatch- bzw. Semibatch-Mehrstoff-Prozessen liegt ein Prozess vor, der sich im Ablauf zwar wiederholt, jedoch mit unterschiedlichen Ausgangsstoffen arbeitet. Im Unterschied zum reinen Batch-Prozess werden Edukte auch während der laufenden Reaktion zugegeben. Speziell bei chemischen Semibatchprozessen kommt noch hinzu, dass der verwendete Reaktor nicht notwendigerweise nach jedem Batch gereingt werden kann, sodass sich bestimmte Reaktorparameter von Zyklus zu Zyklus ändern. Der im vorliegenden Aufsatz dargestellte ILR-Ansatz behandelt genau diese Schwierigkeiten durch Einführung eines speziellen Speichers, der abhängig vom gefahrenen Produkt und Verschmutzungsgrad des Reaktors arbeitet. Das dargestellte Regelverfahren wurde als Referenz anhand des Chylla-Haase-Modells erprobt.

Der Beitrag beschäftigt sich mit Strategien und Algorithmen für die optimale Gangwahl bei Kraftfahrzeugen mit automatisiertem Getriebe. Zum Zweck der Simulation wurde ein vorausschauendes Fahrermodell entwickelt. Darüber hinaus entstand eine Funktion zur Prädiktion der Fahrzeug-Geschwindigkeit, welche sowohl in der Simulation als auch im endgültigen prädiktiven Schaltalgorithmus verwendet wird. Der Aufbau wird beschrieben und die Funktion sowohl in der Simulation als auch auf der Straße getestet.

Der vorliegende Aufsatz beschäftigt sich mit Klassifikationsverfahren wie sie z.B. im Rahmen der technischen Diagnose oder für die digitale Bildverarbeitung eingesetzt werden können. Aus der Vielzahl der verfügbaren Methoden werden die charakteristischen Vertreter behandelt. Ein besonderes Augenmerk wurde dabei der praktischen Anwendbarkeit der Verfahren gewidmet – deren Vorzüge und Nachteile werden ausführlich erörtert und ihre Leistungsfähigkeit anhand unterschiedlicher charakteristischer Merkmalsverteilungen getestet. Ferner wird auf die Auswahl von Merkmalen sowie die dazu notwendigen Gütekriterien eingegangen. Abschließend werden die Verfahren an zwei Modellfällen aus der digitalen Bildverarbeitung und der technischen Diagnose erprobt und die erhaltenen Ergebnisse aus praktischer Sicht diskutiert.

Steer by wire (SBW) systems in which the mechanical link between the steering wheel and road wheels of an automobile are replaced by a control system consisting of sensors, actuators and controllers seem to offer great advantages such as enhanced system performance, simplified construction, design flexibility etc. However, for implementation in road vehicles, safety considerations and fault tolerance become major issues and have to be regarded. In the paper, a SBW control system configuration which takes the safety aspects into account is presented. The proposed system comprises a redundant force-feedback steering wheel actuator to provide road feedback to the driver and a redundant front wheel actuator. Salient results are given based on simulations of the system.

In this paper a discrete-time iterative learning controller for single input single output systems is presented. The iterative learning controller works with a reduced sampling rate that ensures the reduction of an appropriate norm of the error trajectory from cycle to cycle and can cope with initial state error. Initial state error occurs when the initial state of the system is different from the initial state that is implicitly given by the reference trajectory. If the initial state changes for every learning iteration, then the controller cannot achieve ideal tracking but the error trajectory is bounded. Using two different sample times together with a potentially time variant learning gain improves the controller performance for dealing with initial state error. Simulation examples are presented to show the results of the proposed iterative learning controller with reduced sampling rate.

Pneumatically actuated control valves find widespread use in process engineering. Usually the valve shaft position is adjusted by a position controller. In order to examine the static and dynamic behaviour of such controllers they are up to now tested in conjunction with an actuator. Since the friction characteristic of the actuator, which varies due to abrasion etc, has a crucial impact on the control loop performance, the tests are not comparative. In the paper, a simulation concept for “hardware–in–the–loop” testing of position controllers is presented. By simulating the friction in a computer and retaining the rest of the hardware, reproducible results are achieved and at the same time real life process conditions are retained.

The extrusion of aluminium profiles is a typical cyclic process, i.e. operations and transitions occur in repetition. In this paper a control scheme especially designed for the control of cyclic or repetitive processes is presented and application results from an aluminium extrusion plant are depicted. The control scheme improves the system performance from cycle to cycle in an iterative manner: The input trajectories of the cycle k + 1 are computed using the input and output trajectories of the cycle k.

Rule based interpolating control ( RIP control) is an approach to deal with linguistic expert knowledge in the form of production rules, As an alternative to fuzzy control. RIP control transforms the production rules into support points in a hyperspace and applies multidimensional interpolation methods to obtain the input/output mapping of the controller. The paper introduces RIP control and its application in knowledge-based systems. The potential of hybrid RIP rule bases are demonstrated using flow control as an example.

The segmentation of surface deformations like scratches or embossed patterns can be decisively supported by using several images taken under different illumination directions. To demonstrate this approach we present two applications-the segmentation of embossed letters and the detection of grains on grinding wheels-together with two illumination devices which aid in the development of such solutions. We address further the problem of (additive) shading and how at may be solved using conventional approaches based on 'real' or estimated reference images and as well as by artificial reference images obtained by a rendering approach using a model of the setup. Finally we give an outlook on the planning of illumination setups.

In this paper we present two algorithms to derive sequences of morphological gray-scale opening/closing operators from gray-scale (anti-) granulometries which transform images containing textures of two previously known classes into images within which segmentation can be done simply by using a threshold.

In many process engineering plants the transition from one operating point to another occurs repetitively. In this paper a control scheme especially suited for the control of cyclic or repetitive processes is presented and application results for the start up of a dual-input dual-output (DIDO) plant are given. The control scheme improves the system performance from cycle to cycle in an iterative manner: the input trajectories of the cycle k+1 are computed using the input and output trajectories of the cycle k.

In repetitive and cyclic processes, product output and quality can be effectively enhanced by employing learning control. Typical applications are in robot control, control of batch processes, extruder control, etc. Linear iterative learning control was proposed in the 1980's for tracking a control input which is exactly repeated from cycle to cycle. In the paper, an optimal iterative learning control which is not as restrictive as the linear iterative learning control is proposed. Applications of iterative learning control to a coupled double-input double-output plant and for the isothermal control of an industrial extruder for aluminium are described.

Deals with the design of iterative learning controllers (ILC) based on extended state space models for nonlinear cyclic process control. In order to design a suitable learning operator, knowledge about the plant's dynamical behaviour is needed which implies that a system model has to be set up. It is expedient to acquire a state space model of the plant using identification methods. Here we deal especially with the case, that a linear model represents system dynamics inadequately. We start with a nonlinear model and linearize the system along the current trajectory, thus obtaining a linear time variant model. Using this as the basis, we develop methods for identification and control of the nonlinear process. Experimental results show that a good system model is also useful to perform a pre-training for the ILC; this is especially interesting in case large deviations from a desired system output trajectory must be avoided. The presented algorithms have been implemented and tested experimentally with a real-life nonlinear processing plant.

In the paper, we deal with the analysis of blood and bone marrow smears. The main aim of this long term project is to obtain a relative frequency histogram of the white blood cells of different lineage and maturity. Especially for clinical application, a proper image normalization and segmentation of the color images of blood and bone marrow smears are necessary. For the image normalization, two approaches were adopted: a) active image processing for pre acquisition standardization and b) a histogram based method for post acquisition standardization. Both methods are based on the HSI (Hue Saturation Intensity) Transform. We have developed a robust method for the declustering of the inevitable clusters of white blood cells based on a thresholded distance transform and an extended region growing algorithm that in contrast to active contours does not need any parameterization. For a successful classification, medical morphologic features are translated into feature extraction operators: the mesh structure of the cells' nucleus is analyzed using watershed transform and Gabor features, the shape of cell and nucleus is analyzed using a set of rotational invariant contour based features. The color and granularity of the cytoplasm yield further features for classification. Current work is focused on classification using the presented features.

In this paper a method for the stability analysis of a closed loop system, composed of a linear system and its controller with simultaneous acting amplitude and rate control commands limitations, is provided. This kind of nonlinearity is unavoidably encountered in many fields of control applications, because of physical constraints of the actuation devices. In particular, when the control input derivative exceeds the rate limit, the actuation system behaves as the series of a nonlinear element and a dynamic delay, i.e. with a behaviour that cannot be described by an input-output static relationship. The presence of a delay element may lead the whole closed loop system to instability; due to the presence of the saturation element such instability (if occurring) evolves into a limit cycle behaviour. It is proposed to analyse the nonlinear effects of amplitude and rate limited actuators in the frequency domain by using an optimal quasi-linearization approach. The main contribution of the paper is a methodology for the detection and the stability analysis of limit cycles; then the asymptotic stability of the equilibrium of the nonlinear closed loop system is investigated. The theory is applied to an aeronautical real world example to show the effectiveness of the proposed technique.

The in vivo determination of the composition of gallstones is of interest to physicians, as it would offer the basis for choosing the appropriate therapy: high calcium content stones would call for laparoscopy, calcium free stones are smashed with a lithotriptor and flushed out using oral solvents. In publications regarding gallstone examinations employing ultrasound, opinion is divided as to whether or not the B-mode image is correlated with the stone composition. The following work aims at resolving this controversy. B-scans were examined in vivo and in vitro. The B-scans were segmented and several texture based features were tried out. Calcification was verified by x-ray imaging. The evaluation of the texture parameters of the segmented areas do not show any calcification dependent clustering. Analysis of images obtained in vitro using a water bath do not offer any better results than those obtained in vivo. However, the features proposed in the literature do show some correlation with the surface geometry. The investigation of tissue characterization using ultrasound imaging seems to be still open.

In this paper we deal with the analysis of category II (nonlinear) pilot-in-the-loop oscillations (PIO). PIO phenomena are originated by a misadaptation between the pilot and the aircraft that causes sustained or uncontrollable oscillations, which especially occur during some tasks where tight closed loop control of the aircraft is required from the pilot. Category II PIO are those oscillations that can strictly be correlated with the activation of rate and position limiter elements in the closed loop pilot-vehicle system. This kind of nonlinearity is unavoidably present in every aircraft, because of physical constraints of elements such as stick/column deflections, actuator position and rate limiters, limiters in the controller software and so on. In this paper we propose an approach, based on the describing function technique, to evaluate the nonlinear effects of the simultaneous presence of position and rate saturations in the control loop. The X-15 landing flare PIO is used as test case to demonstrate the effectiveness of the method.

Various forms of predictive algorithms are available in the literature. We give an unified representation of these, which includes the multivariable case. Practical considerations of boundedness of the input are taken into account. The algorithms presented are the basis for an industrial multivariable predictive controller.