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  • 1.
    Holmberg, Ulf
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS).
    Valentinotti, S.
    Institut d 'Automatique, Ecole Polytechnique Federale de Lausanne, Switzerland.
    Bonvin, D.
    Institut d 'Automatique, Ecole Polytechnique Federale de Lausanne, Switzerland.
    An identification-for-control procedure with robust performance2000Ingår i: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939, Vol. 8, nr 10, s. 1107-1117Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    A data-driven controller design procedure is proposed in this paper. The controller is based on both an estimated plant model and its estimated uncertainty described by an ellipsoid in parameter space. Desired performance is specified by the speed and the damping of the modeled response. The unmodeled response is rejected by requiring robust performance with respect to a generalized stability region. Moreover, estimation of a disturbance model enables further rejection of the unmodeled response. The methodology is applied to a nonlinear and unstable magnetic suspension system. High performance is achieved for various specifications over a large operational range.

  • 2.
    Nilsson, Kenneth
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS).
    Svensson, Bertil
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS). Department of Computer Engineering, Chalmers University of Technology, Göteborg, Sweden.
    Wiberg, Per-Arne
    Department of Computer Engineering, Chalmers University of Technology, Göteborg, Sweden.
    A modular, massively parallel computer architecture for trainable real-time control systems1993Ingår i: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939, Vol. 1, nr 4, s. 655-661Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    A new system-architectural concept for trainable real-time control systems is based on resource adequacy both in processing and communication. Cyclically executing programs in distributed nodes communicate via a shared high-speed medium. Static scheduling of programs and communication implies that the maximum possible work-load can always be handled in a time-deterministic manner. The use of Artificial Neural Networks (ANN) algorithms and trainability implies a new system development strategy based on a Continuous Development paradigm. An implementation of the Architectural concept is presented. The communication speed is measured in Gbps and the access method is TDMA. An implementation of the system-development strategy is also presented. © 1993.

  • 3.
    Valentinotti, S.
    et al.
    Laboratoire d'Automatique, EPFL, 1015, Lausanne, Switzerland.
    Srinivasan, B.
    Laboratoire d'Automatique, EPFL, 1015, Lausanne, Switzerland.
    Holmberg, Ulf
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE).
    Bonvin, D.
    Laboratoire d'Automatique, EPFL, 1015, Lausanne, Switzerland.
    Cannizzaro, C.
    Laboratoire de Génie Chimique et Biologie, EPFL, 1015, Lausanne, Switzerland.
    Rhiel, M.
    Laboratoire de Génie Chimique et Biologie, EPFL, 1015, Lausanne, Switzerland.
    von Stockar, U.
    Laboratoire de Génie Chimique et Biologie, EPFL, 1015, Lausanne, Switzerland.
    Optimal operation of fed-batch fermentations via adaptive control of overflow metabolite2003Ingår i: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939, Vol. 11, nr 6, s. 665-674Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The maximization of biomass productivity in the fed-batch fermentation of Saccharomyces cerevisiae is analyzed. Due to metabolic bottleneck, often attributed to limited oxygen capacity, ethanol is formed when the substrate concentration is above a critical value, which results in a decrease in biomass productivity. Thus, to maximize the production of biomass, the substrate concentration should be kept at the critical value. However, this value is unknown a priori and may change from experiment to experiment. A way to overcome this lack of knowledge is to allow the cells to produce a very small amount of ethanol. This way, the problem of maximizing the production of biomass is converted into that of regulating the concentration of ethanol, for which cell growth can be viewed as a perturbation. A novel adaptive control methodology based on the internal model principle is used to maintain the desired ethanol setpoint and reject the perturbation. Only a single parameter needs to be estimated on-line. Experimental results demonstrate the effectiveness of the proposed control methodology.

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