Novel Fractional Order Autotuners for Poorly Damped Systems to Ensure Improved Safety and Comfort

Research grant awarded by UEFISCDI Romania www.uefiscdi.gov.ro, project code:PN-III-P1-1.1-TE-2019-0745, TE 143/2020

SUMMARY

Poorly damped systems (PDS) are usually the mathematical representation of a large category of processes encountered in important areas that affect everyday life, such as dynamics of structures (tall buildings, bridges, etc.), automotive industry (suspension car models, etc), aerospace industry (airplane wing model, vertical take off and landing systems, etc.). Quite frequently PDS are modeled using simple second order (plus dead time) transfer functions, which is a clear limitation, since these simple models cannot capture the complex dynamics of PDS. Complicated large models are also available, but these complicate in turn the design of the controller. A major concern is to seek improved control solutions that are independent from accurate modeling of PDS. This leads to the idea of capturing the essential dynamic characteristics of such systems and use these characteristics for designing the controllers based on autotuning methods. The main overall objective of the research grant

is to develop robust and efficient control algorithms, through a novel idea of combining several “hot” concepts of the control engineering community: new autotuning control solutions, automatic identification of process characteristics and fractional calculus, as an emerging tool in control applications. The project aims at experimenting, testing and the validation of original methods to be applied to PDS. The new control structures developed aim to offer a novel, more efficient and robust solution for closed loop control in specific environments where PDS are employed and especially in vital applications for human life. Thus, the results of the project have the main target of increasing safety and comfort in these domains. The project is expected to have
a major impact on opening new themes/research directions regarding fractional order autotuners, automatic system identification. Additionally, the results of the project contribute to new findings and research directions in PDS.

OBJECTIVES

Objective 1: Development of a complete and up-to- date study of the state of the art regarding modern software approaches to exploit step response and frequency response data of PDS, as well as integer order (IO) autotuning methods for controlling these systems and general FO autotuning methods; Objective 2: Design and implementation of existing IO/FO autotuners on PDS, as a basis for comparison; Objective 3: Development of new FO autotuning methods for PDS based on automatic identification of process characteristics using solely step response/ frequency response data; Objective 4: Highlighting the advantages of proposed FO autotuners for PDS, in terms of closed loop robustness, as well as the advantages over existing methods (both IO and FO).

PROJECT REPORTS

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OVERALL PROJECT RESULTS

1. A detailed analysis of modern software approaches to exploit step response and frequency response data of PDS, as well as integer order (IO) autotuning methods for controlling these systems and general FO autotuning methods; 2. A comprehensive study regarding the design and implementation of existing IO/FO autotuners on PDS and an accurate assessment of their limitations; 3. Novel FO autotuning methods for PDS based on automatic identification of process characteristics using solely step response/ frequency response data; 4. New and improved control structures to be applied to PDS systems and available for direct use by the general engineer (fully automatic both in terms of acquiring process information and FO controller tuning) 5. A novel approach in the tuning of FOPID controllers.

OVERALL SCIENTIFIC RESULTS

  1. C.I. Muresan, I. Birs, R. De Keyser (2021) An alternative design approach for Fractional Order Internal Model Controllers for time delay systems,
    Journal of Advanced Research, Volume 31,Pages 177-189,DOI:10.1016/j.jare.2021.01.004 (ISI impact factor 10.479)
  2. Copot, Cosmin, Cristina I. Muresan, Manuel Beschi, and Clara M. Ionescu (2021), “A 6DOF Virtual Environment Space Docking Operation with Human Supervision” Applied Sciences 11, no. 8: 3658. https://doi.org/10.3390/app11083658 (ISI impact factor 2.679)
  3. Muresan, Cristina I., Isabela R. Birs, Eva H. Dulf, Dana Copot, and Liviu Miclea (2021), “A Review of Recent Advances in Fractional-Order Sensing and Filtering Techniques” Sensors 21, no. 17: 5920. https://doi.org/10.3390/s21175920 (ISI impact factor 3.576)
  4. Muresan, Cristina I., De Keyser, R. (2021), Revisiting Ziegler-Nichols. A fractional order approach, ISA Transactions, under review (ISI Impact factor 5.468)

 

  1. Birs, I., Muresan, C., Nascu, I, De Keyser, R. (2021) Experimental comparison between discrete time and event-based PID controllers on a nonlinear process, Proceedings of the  2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), pp. 1-5, DOI: 10.1109/ICECCME52200.2021.9590879, 7-8 October 2021, Mauritius (IEEExplore)
  2. Birs, I., Ionescu, C., Nascu, I., Muresan, C. (2021) A comparison between FOIMC and FOPI controllers for a submerged robot, Proceedings of the 25th International Conference on System Theory, Control and Computing, 20-23 October 2021, Iasi, Romania
  3. Bunescu, I., Birs, I., De Keyser, R., Muresan, C. (2021) A Novel Toolbox for Automatic Design of Fractional Order PI Controllers based on Automatic System Identification from Step Response Data, 16th INTERNATIONAL CONFERENCE Dynamical Systems Theory and Applications, 6-9 December 2021, online
  4. Mihai, M.D., Birs, I., Muresan, C., Dulf, E., De Keyser, R. (2021), Comparisons and Experimental Validation of Several Autotuning Methods for Fractional Order Controllers,  16th INTERNATIONAL CONFERENCE Dynamical Systems Theory and Applications, 6-9 December 2021, online

RESEARCH TEAM

Dr. Eng. Cristina I. Muresan – principal investigator

Dr. Eng. Ovidiu Prodan

Dr. Eng. Cosmin Darab

Eng. Isabela Birs (PhD student)