Abstract
Bu çalışmada, ürünlerin dayanım ve performanslarının
belirlenmesi için kullanılan hidrolik test sistemlerinin kontrolü için Model
Öngörülü Kontrol (Model Predictive Control- MPC) tasarımı yapılmıştır Sistem
kısıtlarının optimal kontrol kuralına dahil edilmesiyle test sistemi
uygulamalarında karşılaşılan doyum problemleri için performans artımı
sağlanması hedeflenmiştir. Bu amaçla, ilk olarak örnek sistem için sistemin
doğrusal olmayan dinamik denklemleri oluşturulmuştur. Model çalışma noktası
etrafında doğrusallaştırılarak, ivme durum değişkeni olacak şekilde durum uzayı
modeli oluşturulmuştur. Elde edilen model, örnek sisteme ait model
parametreleri kullanılarak, MPC içerisinde kullanılmak üzere
ayrıklaştırılmıştır. MPC kuralı, yığın metodu (batch method) yardımı ile
oluşturularak, kısıtlamalı optimal kontrol problemi arama algoritması yardımı
ile çözülmüştür. Kontrol performansının tespiti amacı ile LQR ile
karşılaştırmalı sayısal benzetim sonuçları sunulmuştur. Ayrıca sayısal benzetim
testleri model belirsizliği ve ölçüm gürültüsü koşulları altında tekrarlanmış
ve sonuçlar sunulmuştur. Elde edilen sonuçlar yorumlanmıştır ve gelecek
çalışmalar için önerilerde bulunulmuştur.
References
- Plummer AR. “Control techniques for structural testing: A review”. Proceedings of the Institution of Mechanical Engineers, PartI: Journal of Systems and Control Engineering, 221(2), 139-169, 2007.
- Tuncelli AC, Guner H, Longchamp R. "Hydraulic axis control using pressure feedback". Intelligent Motion Control, Proceedings of the IEEE International Workshop on. Vol. 2. IEEE, 1990.
- Daley S. "Application of a Fast Self-tuning Control Algorithm to a Hydraulic Test Rig". Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 201.4, 285-295, 1987.
- Hinton CE. "The Maximum-gain Minimum-integral Principle Applied to Materials Testing". Getting the Best Our of PID in Machine Control (Digest No.: 1996/287), IEE Colloquium on. IET, 1996.
- Stoten DP, Gomez EG. “Adaptive control of shaking tables using the minimal control synthesis algorithm”. Philosophical Transaction of the Royal Society, A: Mathematical, Physical and Engineering Sciences, 359, (1786), 1697-1723, 2001.
- Langdon JD. Design and Adaptive Control of a Lab-based, Tire-coupled, Quarter-car Suspension Test Rig for the Accurate Re-creation of Vehicle Response. PhD Thesis, Virginia Tech, Blacksburg, USA, 2007.
- Clarke DW, Hinton CJ. “Adaptive Control of Material-testing Machines”. Automatica, 33(6), 1119-1131, 1997.
- Daley S, Hatönen J, Owens DH. “Hydraulic Servo System Command Shaping Using Iterative Learning Control”. Proceedings of UKACC Control 2004 Mini Symposia, Bath, UK, 2004.
- Cuyper JD, Dominiek C. “service load simulation on multi-axis test rigs”. Sound and Vibration, 33, 30-35, 1999.
- Dodds CJ. "A Computer System for Multi-channel Remote Parameter Control of a Test Specimen". MTS publication, 1977.
- Cuyper JD. Linear Feedback Control for Durability Test Rigs in the Automotive Industry. PhD Thesis, Katholieke Universiteit Leuven, 2006.
- Dursun U, Gören L. “Yineleyerek öğrenmeli kontrol yöntemi ile hidrolik simulatörlerin kontrolü”. Proceeding of TOK 2016, Eskişehir, Türkiye, 2016.
- Hampson SP. Nonlinear Model Predictive Control of a Hydraulic Actuator. PhD Thesis, University of Canterbury, Christchurch, New Zealand, 1995.
- Marusak PM, Kuntanapreeda S. "Constrained Model Predictive Force Control of an Electrohydraulic Actuator". Control Engineering Practice, 19.1, 62-73, 2011.
- Yuan HB, Na HC, Kim YB. “System Identification and Robust Position Control for Electro-hydraulic Servo System Using Hybrid Model Predictive Control”. Journal of Vibration and Control, 1077546317721417, 2017.
- Wang D, Zhao D, Gong M, Yang B. “Research on Robust Model Predictive Control for Electro-hydraulic Servo Active Suspension Systems”. IEEE Access, 2017.
- Jelali M, Kroll A. Hydraulic Servo-systems: Modelling, Identification and Control. Springer Science & Business Media, 2012.
- Worthmann K. “Estimates of the Prediction Horizon Length in MPC: A Numerical Case Study”. IFAC Proceedings Volumes, 45(17), 232-237, 2012.
- Borrelli F, Bemporad A, Morari M. Predictive Control for Linear and Hybrid Systems. Cambridge University Press, 2017.
- Pytlak R. Numerical Methods for Optimal Control Problems with State Constraints. Springer, 2006.
- Dursun U. Hidrolik Simülatörlerin Kontrolü. Yüksek Lisans Tezi, İstanbul Teknik Üniversitesi, İstanbul, Türkiye, 2013.
- Zeilinger MN, Jones CN, Morari M. “Real-time suboptimal model predictive control using a combination of explicit MPC and online optimization”. IEEE Transactions on Automatic Control, 56(7), 1524-1534, 2011.
Abstract
In this study, Model Predictive Control (MPC) is
designed for the control of hydraulic test systems that are used for
determining the strength and performance of the product. It is aimed to
increase the performance of the saturation problems faced during the test
system applications while including the system constraints in the optimal
control rule. For this purpose, the nonlinear dynamic equations are first
obtained for the considered test system. The state space model is obtained by
linearizing the model around the equilibrium point in a way that the design variable
is considered to be acceleration. The obtained model is discretized for
employing it in MPC by using the model parameters of the considered system. MPC
rule is solved via constituting batch approach method through constrained
optimal control problem search algorithm. The simulation result of the
comparisons with LQR is presented with the aim of examining the control
performance. In addition, numerical simulations are repeated under parametric
model uncertainty and measurement noise conditions and results are presented. The
obtained results are discussed, and future studies are suggested.
References
- Plummer AR. “Control techniques for structural testing: A review”. Proceedings of the Institution of Mechanical Engineers, PartI: Journal of Systems and Control Engineering, 221(2), 139-169, 2007.
- Tuncelli AC, Guner H, Longchamp R. "Hydraulic axis control using pressure feedback". Intelligent Motion Control, Proceedings of the IEEE International Workshop on. Vol. 2. IEEE, 1990.
- Daley S. "Application of a Fast Self-tuning Control Algorithm to a Hydraulic Test Rig". Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 201.4, 285-295, 1987.
- Hinton CE. "The Maximum-gain Minimum-integral Principle Applied to Materials Testing". Getting the Best Our of PID in Machine Control (Digest No.: 1996/287), IEE Colloquium on. IET, 1996.
- Stoten DP, Gomez EG. “Adaptive control of shaking tables using the minimal control synthesis algorithm”. Philosophical Transaction of the Royal Society, A: Mathematical, Physical and Engineering Sciences, 359, (1786), 1697-1723, 2001.
- Langdon JD. Design and Adaptive Control of a Lab-based, Tire-coupled, Quarter-car Suspension Test Rig for the Accurate Re-creation of Vehicle Response. PhD Thesis, Virginia Tech, Blacksburg, USA, 2007.
- Clarke DW, Hinton CJ. “Adaptive Control of Material-testing Machines”. Automatica, 33(6), 1119-1131, 1997.
- Daley S, Hatönen J, Owens DH. “Hydraulic Servo System Command Shaping Using Iterative Learning Control”. Proceedings of UKACC Control 2004 Mini Symposia, Bath, UK, 2004.
- Cuyper JD, Dominiek C. “service load simulation on multi-axis test rigs”. Sound and Vibration, 33, 30-35, 1999.
- Dodds CJ. "A Computer System for Multi-channel Remote Parameter Control of a Test Specimen". MTS publication, 1977.
- Cuyper JD. Linear Feedback Control for Durability Test Rigs in the Automotive Industry. PhD Thesis, Katholieke Universiteit Leuven, 2006.
- Dursun U, Gören L. “Yineleyerek öğrenmeli kontrol yöntemi ile hidrolik simulatörlerin kontrolü”. Proceeding of TOK 2016, Eskişehir, Türkiye, 2016.
- Hampson SP. Nonlinear Model Predictive Control of a Hydraulic Actuator. PhD Thesis, University of Canterbury, Christchurch, New Zealand, 1995.
- Marusak PM, Kuntanapreeda S. "Constrained Model Predictive Force Control of an Electrohydraulic Actuator". Control Engineering Practice, 19.1, 62-73, 2011.
- Yuan HB, Na HC, Kim YB. “System Identification and Robust Position Control for Electro-hydraulic Servo System Using Hybrid Model Predictive Control”. Journal of Vibration and Control, 1077546317721417, 2017.
- Wang D, Zhao D, Gong M, Yang B. “Research on Robust Model Predictive Control for Electro-hydraulic Servo Active Suspension Systems”. IEEE Access, 2017.
- Jelali M, Kroll A. Hydraulic Servo-systems: Modelling, Identification and Control. Springer Science & Business Media, 2012.
- Worthmann K. “Estimates of the Prediction Horizon Length in MPC: A Numerical Case Study”. IFAC Proceedings Volumes, 45(17), 232-237, 2012.
- Borrelli F, Bemporad A, Morari M. Predictive Control for Linear and Hybrid Systems. Cambridge University Press, 2017.
- Pytlak R. Numerical Methods for Optimal Control Problems with State Constraints. Springer, 2006.
- Dursun U. Hidrolik Simülatörlerin Kontrolü. Yüksek Lisans Tezi, İstanbul Teknik Üniversitesi, İstanbul, Türkiye, 2013.
- Zeilinger MN, Jones CN, Morari M. “Real-time suboptimal model predictive control using a combination of explicit MPC and online optimization”. IEEE Transactions on Automatic Control, 56(7), 1524-1534, 2011.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Engineering |
| Journal Section | Research Article |
| Authors | |
| Publication Date | December 29, 2018 |
| Published in Issue | Year 2018 Volume: 24 Issue: 8 |
