TY  - CONF
AU  - Haber, R.
AU  - Strzelczak, Stanislaw
AU  - Miljković, Zoran
AU  - Castano, F.
AU  - Fumagalli, L.
AU  - Petrović, Milica
PY  - 2020
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/3447
AB  - Nowadays, digital twins are fostering the development of plug, simulate and optimize behavior in industrial cyber-physical systems. This paper presents a digital twin-based optimization of a motion system on the basis of a grey wolf optimization (GWO) method. The digital twin of the whole ultraprecision motion system with friction and backlash including a P-PI cascade controller is used as a basement to minimize the maximum position error. The simulation study and the real-time experiments in trajectory control are performed to compare the performance of the proposed GWO algorithm and the industrial method called Fine tune (FT) method. The simulation study shows that the digital twin-based optimization using GWO outperformed FT method with improvement of 66.4% in the reduction of the maximum position error. The real-time experimental results obtained show also the advantage of GWO method with 18% of improvement in the maximum peak error and 16% in accuracy.
PB  - Institute of Electrical and Electronics Engineers Inc.
C3  - Proceedings - 2020 IEEE Conference on Industrial Cyberphysical Systems, ICPS 2020
T1  - Digital twin-based Optimization on the basis of Grey Wolf Method. A Case Study on Motion Control Systems
EP  - 474
SP  - 469
DO  - 10.1109/ICPS48405.2020.9274728
ER  - 

@conference{
author = "Haber, R. and Strzelczak, Stanislaw and Miljković, Zoran and Castano, F. and Fumagalli, L. and Petrović, Milica",
year = "2020",
abstract = "Nowadays, digital twins are fostering the development of plug, simulate and optimize behavior in industrial cyber-physical systems. This paper presents a digital twin-based optimization of a motion system on the basis of a grey wolf optimization (GWO) method. The digital twin of the whole ultraprecision motion system with friction and backlash including a P-PI cascade controller is used as a basement to minimize the maximum position error. The simulation study and the real-time experiments in trajectory control are performed to compare the performance of the proposed GWO algorithm and the industrial method called Fine tune (FT) method. The simulation study shows that the digital twin-based optimization using GWO outperformed FT method with improvement of 66.4% in the reduction of the maximum position error. The real-time experimental results obtained show also the advantage of GWO method with 18% of improvement in the maximum peak error and 16% in accuracy.",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
journal = "Proceedings - 2020 IEEE Conference on Industrial Cyberphysical Systems, ICPS 2020",
title = "Digital twin-based Optimization on the basis of Grey Wolf Method. A Case Study on Motion Control Systems",
pages = "474-469",
doi = "10.1109/ICPS48405.2020.9274728"
}

Haber, R., Strzelczak, S., Miljković, Z., Castano, F., Fumagalli, L.,& Petrović, M.. (2020). Digital twin-based Optimization on the basis of Grey Wolf Method. A Case Study on Motion Control Systems. in Proceedings - 2020 IEEE Conference on Industrial Cyberphysical Systems, ICPS 2020
Institute of Electrical and Electronics Engineers Inc.., 469-474.
https://doi.org/10.1109/ICPS48405.2020.9274728

Haber R, Strzelczak S, Miljković Z, Castano F, Fumagalli L, Petrović M. Digital twin-based Optimization on the basis of Grey Wolf Method. A Case Study on Motion Control Systems. in Proceedings - 2020 IEEE Conference on Industrial Cyberphysical Systems, ICPS 2020. 2020;:469-474.
doi:10.1109/ICPS48405.2020.9274728 .

Haber, R., Strzelczak, Stanislaw, Miljković, Zoran, Castano, F., Fumagalli, L., Petrović, Milica, "Digital twin-based Optimization on the basis of Grey Wolf Method. A Case Study on Motion Control Systems" in Proceedings - 2020 IEEE Conference on Industrial Cyberphysical Systems, ICPS 2020 (2020):469-474,
https://doi.org/10.1109/ICPS48405.2020.9274728 . .

TY  - CONF
AU  - Petrović, Milica
AU  - Villalonga, Alberto
AU  - Miljković, Zoran
AU  - Castano, Fernando
AU  - Strzelczak, Stanislaw
AU  - Haber, Rodolfo
PY  - 2019
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/3177
AB  - Cascade control configurations are one of the widely used control solutions for improving dynamic response of the feed drive systems in the manufacturing industry. However, optimal tuning of cascade controllers in presence of hard nonlinearities such as backlash and friction is still a complex and time-consuming task. This paper presents a computational procedure for tuning P-PI cascade controller by using particle swarm optimization (PSO) for a feed drive system of machine tools in the presence of friction and backlash. The minimizing of the maximum position error during the reversal of the axes is used as an objective function for optimization. The performance of the PSO method is compared in simulations and real-time experiments with the fine tune (FT) method, which is one of the standard methods applied in industry. Both, simulation and real-time experimental studies carried out on a test platform with 8070 Fagor controller show a remarkable improvement in the performance of the cascade control system using the proposed PSO-based strategy.
PB  - Institute of Electrical and Electronics Engineers Inc.
C3  - IEEE International Conference on Industrial Informatics (INDIN)
T1  - Optimal Tuning of Cascade Controllers for Feed Drive Systems using Particle Swarm Optimization
EP  - 330
SP  - 325
VL  - 2019-July
DO  - 10.1109/INDIN41052.2019.8972132
ER  - 

@conference{
author = "Petrović, Milica and Villalonga, Alberto and Miljković, Zoran and Castano, Fernando and Strzelczak, Stanislaw and Haber, Rodolfo",
year = "2019",
abstract = "Cascade control configurations are one of the widely used control solutions for improving dynamic response of the feed drive systems in the manufacturing industry. However, optimal tuning of cascade controllers in presence of hard nonlinearities such as backlash and friction is still a complex and time-consuming task. This paper presents a computational procedure for tuning P-PI cascade controller by using particle swarm optimization (PSO) for a feed drive system of machine tools in the presence of friction and backlash. The minimizing of the maximum position error during the reversal of the axes is used as an objective function for optimization. The performance of the PSO method is compared in simulations and real-time experiments with the fine tune (FT) method, which is one of the standard methods applied in industry. Both, simulation and real-time experimental studies carried out on a test platform with 8070 Fagor controller show a remarkable improvement in the performance of the cascade control system using the proposed PSO-based strategy.",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
journal = "IEEE International Conference on Industrial Informatics (INDIN)",
title = "Optimal Tuning of Cascade Controllers for Feed Drive Systems using Particle Swarm Optimization",
pages = "330-325",
volume = "2019-July",
doi = "10.1109/INDIN41052.2019.8972132"
}

Petrović, M., Villalonga, A., Miljković, Z., Castano, F., Strzelczak, S.,& Haber, R.. (2019). Optimal Tuning of Cascade Controllers for Feed Drive Systems using Particle Swarm Optimization. in IEEE International Conference on Industrial Informatics (INDIN)
Institute of Electrical and Electronics Engineers Inc.., 2019-July, 325-330.
https://doi.org/10.1109/INDIN41052.2019.8972132

Petrović M, Villalonga A, Miljković Z, Castano F, Strzelczak S, Haber R. Optimal Tuning of Cascade Controllers for Feed Drive Systems using Particle Swarm Optimization. in IEEE International Conference on Industrial Informatics (INDIN). 2019;2019-July:325-330.
doi:10.1109/INDIN41052.2019.8972132 .

Petrović, Milica, Villalonga, Alberto, Miljković, Zoran, Castano, Fernando, Strzelczak, Stanislaw, Haber, Rodolfo, "Optimal Tuning of Cascade Controllers for Feed Drive Systems using Particle Swarm Optimization" in IEEE International Conference on Industrial Informatics (INDIN), 2019-July (2019):325-330,
https://doi.org/10.1109/INDIN41052.2019.8972132 . .