Impulsive Feedback Linearization for Decoupling of a Constant Disturbance with Low Relative Degree to Control Maglev Systems
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
Authors
This paper presents the control of a linearized Maglev system, which is obtained using the Isidori feedback linearization method. In this system, the gravitational force is considered as a disturbance, and strictly speaking its presence prevents the application of this methodology since it cannot be decoupled through pure feedback linearization. In fact, the relative degree of the disturbance with respect to the position is less than the relative degree of the input with respect to the position. For this reason, an additive impulsive action is used for approximative cancellation of this constant. The linearized system is then controlled using a P IDD2, through the classical root-locus method, and the results are evaluated based on simulation studies. For the utilization of state variables and system parameters in the control laws, an extended Kalman filter is employed within the loop, rendering this an advanced model-based feedback control strategy.
Original language | English |
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Title of host publication | 2024 28th International Conference on System Theory, Control and Computing (ICSTCC) : October 10 - 12, 2024 Sinaia, Romania; Proceedings |
Editors | Lucian-Florentin Barbulescu |
Number of pages | 7 |
Place of Publication | Piscataway |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Publication date | 11.11.2024 |
Pages | 470-476 |
ISBN (print) | 979-8-3503-6430-9 |
ISBN (electronic) | 979-8-3503-6429-3, 979-8-3503-6428-6 |
DOIs | |
Publication status | Published - 11.11.2024 |
Event | 28th International Conference on System Theory, Control and Computing - ICSTCC 2024 - Sinaia, Romania Duration: 10.10.2024 → 12.10.2024 Conference number: 28 https://icstcc2024.ace.ucv.ro/ |
Bibliographical note
Publisher Copyright:
© 2024 IEEE.
- Feedback linearization, Kalman filtering, PIDD
- Engineering