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Data-driven Koopman fractional order PID control of a MEMS gyroscope using bat algorithm

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Abstract

Data-driven control methods are strong tools due to their predictions for controlling the systems with a nonlinear dynamic model. In this paper, the Koopman operator is used to linearize the nonlinear dynamic model. Generating the Koopman operator is the most important part of using the Koopman theory. Dynamic mode decomposition (DMD) is used to obtain eigenfunction for producing the Koopman operator. Then, a fractional order PID (FOPID) controller is applied to control the linearized dynamic model. A swarm intelligence bat optimization algorithm is utilized to tune the FOPID controller’s parameters. Simulation results on micro-electromechanical systems (MEMS) gyroscope under conventional PID controller, FOPID, Koopman-based FOPID controller (Koopman-FOPID), and Koopman-FOPID control optimized by bat algorithm (Koopman-BAFOPID) show that the proposed Koopman-BAFOPID controller has better performance in comparison with three other controllers in terms of high tracking performance, low tracking error, and low control efforts.

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Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

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Funding

Funding was provide by Directorate for Engineering, National Science Foundation.

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Authors and Affiliations

  1. The Polytechnic School, Ira Fulton School of Engineering, Arizona State University, Mesa, AZ, 85212, USA

    Mehran Rahmani & Sangram Redkar

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  1. Mehran Rahmani
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  2. Sangram Redkar
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Correspondence to Sangram Redkar.

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Rahmani, M., Redkar, S. Data-driven Koopman fractional order PID control of a MEMS gyroscope using bat algorithm. Neural Comput & Applic 35, 9831–9840 (2023). https://doi.org/10.1007/s00521-023-08220-w

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