1,870 research outputs found

    High-order volterra model predictive control and its application to a nonlinear polymerisation process

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    Model Predictive Control (MPC) has recently found wide acceptance in the process industry, but the existing design and implementation methods are restricted to linear process models. A chemical process involves, however, severe nonlinearity which cannot be ignored in practice. This paper aims to solve this nonlinear control problem by extending MPC to nonlinear models. It develops an analytical framework for nonlinear model predictive control (NMPC), and also offers a third-order Volterra series based nonparametric nonlinear modelling technique for NMPC design which relieves practising engineers from the need for first deriving a physical-principles based model. An on-line realisation technique for implementing the NMPC is also developed. The NMPC is then applied to a Mitsubishi Chemicals polymerisation reaction process. The results show that this nonlinear MPC technique is feasible and very effective. It considerably outperforms linear and low-order Volterra model based methods. The advantages of the approach developed lie not only in control performance superior to existing NMPC methods, but also in relieving practising engineers from the need for deriving an analytical model and then converting it to a Volterra model through which the model can only be obtained up to the second order

    Improvements in the preparation of heterologous antilymphocyte globulin with special reference to absorption and diethylaminoethyl cellulose batch production

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    Antilymphocyte gamma-G globulin (ALGG) was produced from the serum of immunized horses. Modifications of the preliminary absorption techniques permitted the removal of undesirable, extraneous antibodies. With the use of a batch technique, pure gamma-G globulin could then be removed in bulk quantities. The resulting product was first confirmed to have immunosuppressive qualities in dogs and then given a clinical trial. In patients, its administration occasionally caused low-grade fever and thrombocytopenia. Pain at the injection site was not eliminated. Precipitin antibody responses have apparently been prevented in the patients but not a host response to Forssman antigens. © 1969

    Nonparametric nonlinear model predictive control

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    Model Predictive Control (MPC) has recently found wide acceptance in industrial applications, but its potential has been much impeded by linear models due to the lack of a similarly accepted nonlinear modeling or databased technique. Aimed at solving this problem, the paper addresses three issues: (i) extending second-order Volterra nonlinear MPC (NMPC) to higher-order for improved prediction and control; (ii) formulating NMPC directly with plant data without needing for parametric modeling, which has hindered the progress of NMPC; and (iii) incorporating an error estimator directly in the formulation and hence eliminating the need for a nonlinear state observer. Following analysis of NMPC objectives and existing solutions, nonparametric NMPC is derived in discrete-time using multidimensional convolution between plant data and Volterra kernel measurements. This approach is validated against the benchmark van de Vusse nonlinear process control problem and is applied to an industrial polymerization process by using Volterra kernels of up to the third order. Results show that the nonparametric approach is very efficient and effective and considerably outperforms existing methods, while retaining the original data-based spirit and characteristics of linear MPC

    CAutoCSD-evolutionary search and optimisation enabled computer automated control system design

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    This paper attempts to set a unified scene for various linear time-invariant (LTI) control system design schemes, by transforming the existing concept of 'Computer-Aided Control System Design' (CACSD) to the novel 'Computer-Automated Control System Design' (CAutoCSD). The first step towards this goal is to accommodate, under practical constraints, various design objectives that are desirable in both time and frequency-domains. Such performance-prioritised unification is aimed to relieve practising engineers from having to select a particular control scheme and from sacrificing certain performance goals resulting from pre-committing to the adopted scheme. With the recent progress in evolutionary computing based extra-numeric, multi-criterion search and optimisation techniques, such unification of LTI control schemes becomes feasible, analytically and practically, and the resultant designs can be creative. The techniques developed are applied to, and illustrated by, three design problems. The unified approach automatically provides an integrator for zero-steady state error in velocity control of a DC motor, meets multiple objectives in designing an LTI controller for a non-minimum phase plant and offers a high-performing LTI controller network for a nonlinear chemical process

    Evidence for Carrier-Induced High-Tc Ferromagnetism in Mn-doped GaN film

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    A GaN film doped with 8.2 % Mn was grown by the molecular-beam-epitaxy technique. Magnetization measurements show that this highly Mn-doped GaN film exhibits ferromagnetism above room temperature. It is also revealed that the high-temperature ferromagnetic state is significantly suppressed below 10 K, accompanied by an increase of the electrical resistivity with decreasing temperature. This observation clearly demonstrates a close relation between the ferromagnetism with extremely high-Tc and the carrier transport in the Mn-doped GaN film.Comment: 9 pages, 3 figure
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