Abstract
Model accuracy is the most important step towards efficient control design. Various system identification techniques exist which are used to estimate model parameters. However, these techniques have their merits and demerits which need to be considered before selecting a particular system identification technique. In this paper, various system identification techniques as the Kalman filter (EKF), recursive least square (RLS) and least mean square (LMS) filters are used to estimate the parameters of linear (DC motor) and nonlinear systems (inverted pendulum and adaptive polynomial models). FPGAs are widely used for rapid prototyping, real-time and high computationally demanding applications. Therefore, a real-time FPGA-in the loop architecture has been used for evaluating each identification algorithm of the SysIDLib library. The identification algorithms are evaluated regarding the convergence rate, accuracy and resource utilization performed on a system-onchip (SoC). The results have shown that the RLS algorithm estimated approximately the parameter values of a nonlinear system. However, it requires up to 17% less lookup-tables, 5.5% less flip-flops and 14% less DSPs compared to EKF with accurate results on the programmable logic (PL).
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Acknowledgment
The work described in this paper has been funded by the German Research Foundation (DFG, Deutsche Forschungsgemeinschaft) as part of Germany’s Excellence Strategy – EXC 2050/1 – Project ID 390696704 – Cluster of Excellence “Centre for Tactile Internet with Human-in-the-Loop” (CeTI) of Technische Universität Dresden.
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Akgün, G., Khan, H., Hebaish, M., Elshimy, M., Ghany, M.A.A.E., Göhringer, D. (2020). SysIDLib: A High-Level Synthesis FPGA Library for Online System Identification. In: Rincón, F., Barba, J., So, H., Diniz, P., Caba, J. (eds) Applied Reconfigurable Computing. Architectures, Tools, and Applications. ARC 2020. Lecture Notes in Computer Science(), vol 12083. Springer, Cham. https://doi.org/10.1007/978-3-030-44534-8_8
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DOI: https://doi.org/10.1007/978-3-030-44534-8_8
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