3,279 research outputs found

    Autotuning method for a fractional order controller for a multivariable 13C isotope separation column

    Get PDF
    The preferred controller design technique in industrial applications is based on autotuning procedures that do not involve knowledge about an actual mathematical model of the process. In this paper, a novel autotuning method for designing fractional order controllers is addressed. The proposed technique is simple and efficient. Previous research with respect to autotuning methods for fractional order controllers has considered exclusively the case of a single-input single-output process. However, in this paper, a multivariable case study is preferred. The simulation results demonstrate the validity of the design technique

    The application of a new PID autotuning method for the steam/water loop in large scale ships

    Get PDF
    In large scale ships, the most used controllers for the steam/water loop are still the proportional-integral-derivative (PID) controllers. However, the tuning rules for the PID parameters are based on empirical knowledge and the performance for the loops is not satisfying. In order to improve the control performance of the steam/water loop, the application of a recently developed PID autotuning method is studied. Firstly, a 'forbidden region' on the Nyquist plane can be obtained based on user-defined performance requirements such as robustness or gain margin and phase margin. Secondly, the dynamic of the system can be obtained with a sine test around the operation point. Finally, the PID controller's parameters can be obtained by locating the frequency response of the controlled system at the edge of the 'forbidden region'. To verify the effectiveness of the new PID autotuning method, comparisons are presented with other PID autotuning methods, as well as the model predictive control. The results show the superiority of the new PID autotuning method

    A Survey on Compiler Autotuning using Machine Learning

    Full text link
    Since the mid-1990s, researchers have been trying to use machine-learning based approaches to solve a number of different compiler optimization problems. These techniques primarily enhance the quality of the obtained results and, more importantly, make it feasible to tackle two main compiler optimization problems: optimization selection (choosing which optimizations to apply) and phase-ordering (choosing the order of applying optimizations). The compiler optimization space continues to grow due to the advancement of applications, increasing number of compiler optimizations, and new target architectures. Generic optimization passes in compilers cannot fully leverage newly introduced optimizations and, therefore, cannot keep up with the pace of increasing options. This survey summarizes and classifies the recent advances in using machine learning for the compiler optimization field, particularly on the two major problems of (1) selecting the best optimizations and (2) the phase-ordering of optimizations. The survey highlights the approaches taken so far, the obtained results, the fine-grain classification among different approaches and finally, the influential papers of the field.Comment: version 5.0 (updated on September 2018)- Preprint Version For our Accepted Journal @ ACM CSUR 2018 (42 pages) - This survey will be updated quarterly here (Send me your new published papers to be added in the subsequent version) History: Received November 2016; Revised August 2017; Revised February 2018; Accepted March 2018

    Validation of the KC autotuning principle on a multi-tank pilot process

    Get PDF
    PIDs are the most widely used controllers in industrial applications. This particular interest generates on-going research regarding simplified tuning methods appealing to the industrial user. Such methods refer also to a fast design of PID controllers in the absence of a mathematical model of the process. Autotuners represent one way of achieving such a fast design. In this paper, the experimental validation of a previously presented direct autotuner is presented. The autotuning method requires only one simple sine test on the process to compute the PID controller parameters. The case study consists in the Quanser Six Tanks Process. Comparisons with other popular tuning methods are also presented. The results show that the proposed autotuning method is a valuable option for controlling industrial processes

    Robust fractional order PI control for cardiac output stabilisation

    Get PDF
    Drug regulatory paradigms are dependent on the hemodynamic system as it serves to distribute and clear the drug in/from the body. While focusing on the objective of the drug paradigm at hand, it is important to maintain stable hemodynamic variables. In this work, a biomedical application requiring robust control properties has been used to illustrate the potential of an autotuning method, referred to as the fractional order robust autotuner. The method is an extension of a previously presented autotuning principle and produces controllers which are robust to system gain variations. The feature of automatic tuning of controller parameters can be of great use for data-driven adaptation during intra-patient variability conditions. Fractional order PI/PD controllers are generalizations of the well-known PI/PD controllers that exhibit an extra parameter usually used to enhance the robustness of the closed loop system. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved
    • …
    corecore