Volume 19, Issue 7 (July 2019)
Modares Mechanical Engineering 2019, 19(7): 1675-1684 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Falahati Nodeh T, Mirzaei M, Babazadeh Mehrababni E, Khosrowjerdi M. Investigating the Effect of Different Sensors on the Observer Performance in Vehicle Suspension System Based on the Observable Degree Analysis. Modares Mechanical Engineering 2019; 19 (7) :1675-1684
URL: http://mme.modares.ac.ir/article-15-24652-en.html
URL: http://mme.modares.ac.ir/article-15-24652-en.html
1- Mechanical Engineering Department, Mechanical Engineering Faculty, Sahand University of Technology, Tabriz, Iran
2- Mechanical Engineering Department, Mechanical Engineering Faculty, Sahand University of Technology, Tabriz, Iran ,mirzaei@sut.ac.ir
3- Electrical Engineering Department, Electrical Engineering Faculty, Sahand University of Technology, Tabriz, Iran
2- Mechanical Engineering Department, Mechanical Engineering Faculty, Sahand University of Technology, Tabriz, Iran ,
3- Electrical Engineering Department, Electrical Engineering Faculty, Sahand University of Technology, Tabriz, Iran
Abstract: (4110 Views)
In this paper, the effect of different sensors on the observer performance of vehicle suspension system is investigated. For this purpose, the concept of observable degree analysis is used to quantitatively measure the observability for different sensor choices. A new method, for determining the observable degree of linear time invariant (LTI) systems has been developed on the basis of distance of system from set of similar unobservable systems. A long distance is equivalent to a strong observability and a short distance is equivalent to a weak observability. The zero distance means that the system is unobservable. Since the distance to different unobservable modes can be determined separately, a comprehensive investigation of system observability and the effect of different sensor choices on the observer performance can be provided. In the following, the observable analysis of the suspension system was performed based on the proposed method and the effect of different outputs on the observer performance has been investigated. The results show that when the observable degree is increased for a specific sensor, the observer gain is decreased and consequently the sensitivity of observer relative to the noise and measurement errors is decreased. The increased accuracy of observer demonstrates a good conformity between observable degree analysis and observer performance. Also, a comparative study showed that, contrary to previous criteria that only considered a certain aspect of observability, the proposed method is more comprehensive and realistic, and the results obtained from the previous criteria can easily be achieved through the proposed method.
Article Type: Original Research |
Subject:
Mechatronics
Received: 2018/12/27 | Accepted: 2019/02/4 | Published: 2019/07/1
Received: 2018/12/27 | Accepted: 2019/02/4 | Published: 2019/07/1
References
1. Ge Q, Ma J, Chen Sh, Wang Y, Bai L. Observable degree analysis to match estimation performance for wireless tracking networks. Asian Journal of Control. 2017;19(4):1259-1270. [Link] [DOI:10.1002/asjc.1386]
2. Dhingra NK, Jovanović MR, Luo ZQ. An ADMM algorithm for optimal sensor and actuator selection. 53rd IEEE Conference on Decision and Control, 15-17 Dec 2014, Los Angeles CA, USA. Piscataway: IEEE; 2014. [Link] [DOI:10.1109/CDC.2014.7040017]
3. Argha AR, Su SW, Savkin A, Celler B. A framework for optimal actuator/sensor selection in a control system. International Journal of Control. 2019;92(2):242-260. [Link] [DOI:10.1080/00207179.2017.1350755]
4. Healey M, Mackinnon DJ. A quantitative measure of observability for a linear system. International Journal of Control. 1975;22(3):421-426. [Link] [DOI:10.1080/00207177508922094]
5. Hamdan AMA, Nayfeh AH. Measures of modal controllability and observability for first- and second-order linear systems. Journal of Guidance Control and Dynamics. 1989;12(3):421-428. [Link] [DOI:10.2514/3.20424]
6. Müller PC, Weber HI. Analysis and optimization of certain qualities of controllability and observability for linear dynamical systems. Automatica. 1972;8(3):237-246. [Link] [DOI:10.1016/0005-1098(72)90044-1]
7. Damak T, Babary JP, Nihtilä MT. Observer design and sensor location in distributed parameter bioreactors. Dynamics and Control of Chemical Reactors Distillation Columns and Batch Processes. 1993;87-92. [Link] [DOI:10.1016/B978-0-08-041711-0.50015-5]
8. Dochain D, Tali-Maamar N, Babary JP. On modelling, monitoring and control of fixed bed bioreactors. Computers & Chemical Engineering. 1997;21(11):1255-1266. [Link] [DOI:10.1016/S0098-1354(96)00370-5]
9. Waldraff W, Dochain D, Bourrel S, Magnus A. On the use of observability measures for sensor location in tubular reactor. Journal of Process Control. 1998;8(5-6):497-505. [Link] [DOI:10.1016/S0959-1524(98)00017-1]
10. Van Den Berg FWJ, Hoefsloot HCJ, Boelens HFM, Smilde AK. Selection of optimal sensor position in a tubular reactor using robust degree of observability criteria. Chemical Engineering Science. 2000;55(4):827-837. [Link] [DOI:10.1016/S0009-2509(99)00360-7]
11. Ham FM, Grover Brown R. Observability, eigenvalues, and Kalman filtering. IEEE Transactions on Aerospace and Electronic Systems. 1983;AES-19(2):269-273. [Link] [DOI:10.1109/TAES.1983.309446]
12. Cheng XH, Wan DJ, Zhong X. Study on observability and its degree of strapdown inertial navigation system. Journal of Southeast University. 1997;(6):6-11. [Link]
13. Dong JL, Mo B. The method of system observability analysis using pseudo-inverse of system observability matrix. Proceedings of the 32nd Chinese Control Conference, 26-28 July 2013, Xi'an, China. Piscataway: IEEE; 2013. [Link]
14. Chen Y, Zhao Y, Li QS. Observable degree analysis method and its application in transfer alignment. Journal of Chinese Inertial Technology. 2013;(4):467-471. [Link]
15. Zhuo P, Ge Q, Shao T, Wang Y, Bai L. Observable degree analysis using unscented information filter for nonlinear estimation systems. 10th International Conference on Information, Communications and Signal Processing (ICICS), 2-4 Dec 2015, Singapore, Singapore. Piscataway: IEEE; 2015. [Link] [DOI:10.1109/ICICS.2015.7459932]
16. Lystianingrum V, Hredzak B, Agelidis VG, Djanali VS. Observability degree criteria evaluation for temperature observability in a battery string towards optimal thermal sensors placement. IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), 21-24 April 2014, Singapore, Singapore. Piscataway: IEEE; 2014. [Link] [DOI:10.1109/ISSNIP.2014.6827641]
17. Ma J, Ge Q, Wang Y, Bai L. Comparison on system observable degree analysis methods for target tracking. IEEE International Conference on Information and Automation, 8-10 Aug 2015, Lijiang, China. Piscataway: IEEE; 2015. [Link] [DOI:10.1109/ICInfA.2015.7279439]
18. Eising R. The distance between a system and the set of uncontrollable systems. In: Fuhrmann PA, editor. Mathematical theory of networks and systems, lecture notes in control and information sciences. 58th Volume. Berlin/Heidelberg: Springer; 1984. pp. 303-314. [Link] [DOI:10.1007/BFb0031061]
19. Van De Wal M, De Jager B. Selection of sensors and actuators for an active suspension control problem. Proceeding of the 1996 IEEE International Conference on Control Applications IEEE International Conference on Control Applications held together with IEEE International Symposium on Intelligent Control, 15 Sept-18 Nov 1996, Dearborn, MI, USA. Piscataway: IEEE; 1996. [Link]
20. Sarshari E, Khaki Sedigh A. Selection of sensors for hydro-active suspension system of passenger car with input-output pairing considerations. Journal of Dynamic Systems Measurement and Control. 2013;135(1):011004. [Link] [DOI:10.1115/1.4006625]
21. Malekshahi A, Mirzaei M, Aghasizade S. Non-linear predictive control of multi-input multi-output vehicle suspension system. Journal of Low Frequency Noise Vibration and Active Control. 2015;34(1):87-105. [Link] [DOI:10.1260/0263-0923.34.1.87]
22. Malekshahi A, Mirzaei M. Designing a non-linear tracking controller for vehicle active suspension systems using an optimization process. International Journal of Automotive Technology. 2012;13(2):263-271. [Link] [DOI:10.1007/s12239-012-0023-6]
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |