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).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ricco, M., et al.: FPGA-based implementation of dual Kalman filter for PV MPPT applications. IEEE Trans. Industr. Inf. 13(1), 176–185 (2017)
Akgün, G., et al.: Dynamic tunable and reconfigurable hardware controller with EKF-based state reconstruction through FPGA-in the loop. In: 2018 International Conference on ReConFigurable Computing and FPGAs (ReConFig), pp. 1–8 (2018)
Akgün, G., et al.: System identification using LMS, RLS, EKF and neural network. In: 2019 IEEE International Conference of Vehicular Electronics and Safety (ICVES), pp. 1–6 (2019)
Yang, J.N., et al.: An adaptive extended Kalman filter for structural damage identifications II: unknown inputs. Struct. Control Health Monit. 14(3), 497–521 (2007)
Salcic, Z., et al.: A floating-point FPGA-based self-tuning regulator. IEEE Trans. Ind. Electron. 53(2), 693–704 (2006)
Ronquillo-Lomeli, G., et al.: Nonlinear identification of inverted pendulum system using Volterra polynomials. Mech. Based Des. Struct. Mach. 44(1–2), 5–15 (2016)
Kapgate, S.N., et al.: Adaptive Volterra modeling for nonlinear systems based on LMS variants. In: 2018 5th International Conference on Signal Processing and Integrated Networks (SPIN), pp. 258–263 (2018)
Subudhi, U., et al.: Harmonics and decaying DC estimation using Volterra LMS/F algorithm. IEEE Trans. Ind. Appl. 54(2), 1108–1118 (2018)
Morales-Velazquez, L., et al.: Special purpose processor for parameter identification of CNC second order servo systems on a low-cost FPGA platform. Mechatronics 20(2), 265–272 (2010)
Ananthan, T., et al.: An FPGA-based parallel architecture for on-line parameter estimation using the RLS identification algorithm. Microprocess. Microsyst. 38(5), 496–508 (2014)
Navarro, D., et al.: High-level synthesis for accelerating the FPGA implementation of computationally demanding control algorithms for power converters. IEEE Trans. Industr. Inf. 9(3), 1371–1379 (2013)
Morello, R., et al.: Hardware-in-the-loop simulation of FPGA-based state estimators for electric vehicle batteries. In 2016 IEEE 25th International Symposium on Industrial Electronics (ISIE), pp. 280–285 (2016)
Haykin, S.S.: Adaptive Filter Theory, vol. 4. Prentice Hall, Upper Saddle River (2002)
Mie, S., et al.: Real-time UAV attitude heading reference system using extended Kalman filter for programmable SoC. In: 2017 IEEE 11th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC), pp. 136–142, September 2017
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-44534-8_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-44533-1
Online ISBN: 978-3-030-44534-8
eBook Packages: Computer ScienceComputer Science (R0)