Alexander Pollok
Institute of System Dynamics and Control, DLR German Aerospace Center, Germany
Francesco Casella
Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Italy
Ladda ner artikel
http://dx.doi.org/10.3384/ecp18148223Ingår i: Proceedings of the 2nd Japanese Modelica Conference, Tokyo, Japan, May 17-18, 2018
Linköping Electronic Conference Proceedings 148:31, s. 223-229
Publicerad: 2019-02-21
ISBN: 978-91-7685-266-8
ISSN: 1650-3686 (tryckt), 1650-3740 (online)
For optimization studies of dynamical systems, it is common practice to model and tune local controllers for miscellaneous subsystems. For instance, a model of a chemical plant may contain a valve motor model, and a model of a PID controller may be included to control the motor. The associated controller tuning effort is ultimately wasted. The actual controller will be retuned anyway after finalization of the system design, or will be structurally different.
For this reason, control algorithms are needed that just provide the functionality of the actual control algorithm that will be designed in a later phase of the system design. These temporary algorithms need to have low tuning requirements, and it must be possible for non-controlspecialist to generate them. On the other hand, they only need to function inside a simulation environment.
Several mainstream control approaches are reviewed, and boundary layer sliding mode control is proposed as a suitable approach for this kind of task. This class of controllers can be used without any tuning effort, and is able to compete with tuned PID-controllers in terms of tracking performance. An end-user friendly implementation of a universal controller in the equation-based and object-oriented modelling language Modelica is presented. Several examples are shown to demonstrate the performance of the proposed approach.
Modelling, Modelica, Sliding Mode, Modelling aids, Optimization, Local Controller
Paul Acquatella, Wouter Falkena, Erik-Jan van Kampen, and Q Ping Chu. Robust nonlinear spacecraft attitude control using incremental nonlinear dynamic inversion. In AIAA Guidance, Navigation, and Control Conference, page 4623, 2012.
Karl J Åström and BjörnWittenmark. Adaptive control. Courier Corporation, 2013.
G Bartolini, A Ferrara, and E Usai. Chattering avoidance by second-order sliding mode control. IEEE Transactions on Automatic control, 43(2):241–246, 1998.
John Doyle. Guaranteed margins for lqg regulators. IEEE Transactions on Automatic Control, 23(4):756–757, 1978.
Christopher Edwards and Sarah Spurgeon. Sliding mode control: theory and applications. Crc Press, 1998.
Rudolf Emil Kalman et al. Contributions to the theory of optimal control. Bol. Soc. Mat. Mexicana, 5(2):102–119, 1960.
Linda R Petzold et al. A description of dassl: A differential/algebraic system solver. Scientific computing, 1:65–68, 1982.
Andreas Pfeiffer. Optimization library for interactive multicriteria optimization tasks. In Proceedings of the 9th International MODELICA Conference; September 3-5; 2012; Munich; Germany, number 76, pages 669–680. Linköping University Electronic Press; Linköpings universitet, 2012.
Alexander Pollok, Dirk Zimmer, and Francesco Casella. Fractional-order modelling in Modelica. In Proceedings of the 11th International Modelica Conference, 2015.
Sigurd Skogestad and Ian Postlethwaite. Multivariable feedback control: analysis and design, volume 2. Wiley New York, 2007.
Michael Thümmel, Gertjan Looye, Matthias Kurze, Martin Otter, and Johann Bals. Nonlinear inverse models for control. In Proceedings of the 4th International Modelica Conference, pages 267–279, 2005.
Vadim Utkin, Jürgen Guldner, and Jingxin Shi. Sliding mode control in electro-mechanical systems, volume 34. CRC press, 2009.
Blas M Vinagre, Concepción A Monje, Antonio J Calderón, and José I Suárez. Fractional pid controllers for industry application. a brief introduction. Journal of Vibration and Control, 13(9-10):1419–1429, 2007.
K David Young, Vadim I Utkin, and Umit Ozguner. A control engineer’s guide to sliding mode control. In Variable Structure Systems, 1996. VSS’96. Proceedings., 1996 IEEE International Workshop on, pages 1–14. IEEE, 1996.
Ali Zilouchian and Mohammad Jamshidi. Intelligent control systems using soft computing methodologies. CRC Press, Inc., 2000.
