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Prof. Dr. Eng. Habil. MIHAI–AURELIU LUNGU University of Craiova, Faculty of Electrical Engineering, Electric, Energetic and Aerospatiale Engineering Department Aerospace Engineering Doctoral School of NUST Politehnica Bucharest |
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Mihai Lungu
Program: Human Resources
Grant type: Young Teams
Coordinator institute: University of Craiova
Financial sources: Executive Agency for Higher Education, Research, Development and Innovation Funding (UEFISCDI)
Grant duration: 24 months
Grant value: 521.695 lei (117.235 Euro)
PROJECT NO. 89/1.10.2015
MODERN ARCHITECTURES FOR THE CONTROL
OF AIRCRAFT LANDING
Abstract of the project:
Aircraft landing is the most challenging flight phase. Till now, no automatic landing system (ALS) has been tested to all the sensor errors that can appear in the same time with severe wind shears and atmospheric turbulences; this will lead to very robust ALSs. Beside the deterministic errors of the sensors, we will take into account the variations of these errors with respect to the environment conditions. Two ALSs will be designed: the former will be an optimal one, while the latter will be an adaptive ALS; the optimal automatic landing system will be the first architecture which will use, in the same time, the H2/H∞ technique, the dynamic inversion and, as subsystems: multiple observers, reference models, blocks modeling the aircraft geometry of landing, dynamic compensators etc. The new adaptive architecture will be the first ALS which will bring together the neural networks, the dynamic inversion, multiple observers, and Pseudo Control Hedging (PCH) blocks modeled by means of the fuzzy logic; moreover, to this configuration, we will add: reference models, blocks modeling aircraft geometry of landing, dynamic compensators. We will design optimal and adaptive ALSs for the longitudinal and lateral-directional planes, these being then concatenated. To study the performances of the new obtained automatic landing systems, these will be software implemented, tested, and validated by using real flight data for Boeing 747 airplanes.
Objectives of the project:
Main objective:
The increase of young researchers’ number and of their professional performances, as well as the knowledgedevelopmentby means of fundamental research with experimentally validated results
General objectives:
1) Developing of young researchers’ capacity to lead research teams and participate in research grants.
2) Increasing of the young researchers’ capacity to implement their own research program.
3) Forming, training of the young specialists, as well as the increasing of their international visibility by involving them into multidisciplinary valuable research.
4) Solving borderline scientific domains’ open issues having a strong interdisciplinary character.
Specific objectives:
1) To know aircraft dynamics, the error sensors, and all the atmospheric disturbances during landing.
2) To know the existing optimal and adaptive approaches for aircraft control during landing.
3) To design analytical models of the sensors taking into account both the deterministic and the stochastic errors.
4) To design a new multiple-observer for aircraft longitudinal motion during landing (innovative architecture).
5) To design a new optimal landing system (longitudinal plane) by using the H2/H∞ technique, the dynamic inversion approach, reference models, the geometry of landing, and dynamic compensators, taking into account the sensor errors, the wind shears, and the atmospheric turbulences (innovative architecture).
6) To design a new multiple-observer for aircraft lateral motion during landing (innovative architecture).
7) To design a new optimal landing system (lateral plane) by using the H2/H∞ technique, the dynamic inversion approach, reference models, the geometry of landing, and dynamic compensators, taking into account the sensor errors, the wind shears, and the atmospheric turbulences (innovative architecture).
8) To interconnect the two automatic landing subsystems and to obtain a new and innovative ALS based on the H2/H∞ technique and the dynamic inversion approach (innovative auto-pilot architecture).
9) To software implement, test and validate the new optimal ALS (software package).
10) To optimize and to improve the robustness of the new optimal ALS; to study the sensor errors and atmospheric disturbances’ influence onthe new optimal ALS (optimization studies).
11) To design the components of a new adaptive architecture for landing control (longitudinal plane).
12) To design a Pseudo Control Hedging block (PCH), for aircraft motion in longitudinal plane, byusing classical methods or a new method based on fuzzy logic (innovative architecture).
13) To design a new adaptive landing system (longitudinal plane) by using neural networks, the dynamic inversion approach, reference models, the geometry of landing, dynamic compensators, and a PCH block taking into account the sensor errors, the wind shears, and the atmospheric turbulences (innovative architecture).
14) To design the components of a new adaptive architecture for landing control (lateral plane).
15) To design a Pseudo Control Hedging block (PCH), for aircraft motion in lateral plane, byusing classical methods or a new method based on fuzzy logic (innovative architecture).
16) To design a new adaptive landing system (lateral plane) by using neural networks, the dynamic inversion approach, reference models, the geometry of landing, dynamic compensators, and a PCH block taking into account the sensor errors, the wind shears, and the atmospheric turbulences (innovative architecture).
17) To interconnect the two automatic landing subsystems and to obtain a new and innovative ALS based on the neural networks’ usage and the dynamic inversion approach (innovative auto-pilot architecture).
18) To software implement, test and validate the new adaptive ALS (software package).
19) To optimize and to improve the robustness of the new adaptive ALS; to study the sensor errors and atmospheric disturbances’ influence onthe new optimal ALS (optimization studies).
20) To compare our new two ALSs with other already existing ALSs and to compare the new designed ALSs from the performances’ point of view (comparative studies).
21) To disseminate the results in the scientific, academic, and socio-economic environment.
Team of the project:
1. Lungu Mihai-Aureliu – Researcher, Project Manager (University of Craiova)
2. Grigorie Teodor Lucian – Postdoctoral Researcher (University of Craiova)
3. Tutunea Dragos – Postdoctoral Researcher (University of Craiova)
4. Corcau Jenica Ileana – Postdoctoral Researcher (University of Craiova)
5. Lungu Romulus – Senior Researcher (University of Craiova)
6. Butu Florentin Alin – PhD. Researcher (University “Politehnica” of Bucharest)
7. Ioan Mihai – PhD. Researcher (University “Politehnica” of Bucharest)
Work plan of the project:
I. Documentary studies regarding landing, analitic models and design of the sensors and multiple observers (3 months / 2015)
I.1. Documentary studies regarding aicraft dynamics during landing, the errors of the sensors and the atmospheric disturbances associated to the landing process.
I.2. Documentary studies regarding modern optimal and adaptive control techniques for aircraft control during landing.
I.3. Design of the complex analitical models of the sensors.
I.4. Design of the multiple observers for aircraft motions in longitudinal and lateral-directional planes during landing.
I.5. Design of the project's web-site.
II. Design, validation and optimization of the optimal automatic landing system (ALS) (12 months / 2016)
II.1. Design of the optimal control laws using the H2/H∞ and dynamic inversion techniques (longitudinal and lateral-directional planes).
II.2. Design of the blocks associated to the reference models, landing geometry and dynamic compensator (longitudinal and lateral-directional planes).
II.3. Design of the PCH (Pseudo Control Hedging) blocks using classical/fuzzy logic techniques (longitudinal and lateral-directional planes).
II.4. Design of the optimal subsystems for landing control (longitudinal and lateral-directional planes).
II.5. Interconnection of the two optimal subsystems and the obtaining of the new optimal automatic landing system.
II.6. Software implementation of the optimal automatic landing system.
II.7. Organize a special session within ICATE’16 IEEE Conference.
II.8. Dissemination of results.
III. Proiectarea, validarea si optimizarea ALS-ului adaptiv. Studii comparative intre cele doua ALS-uri proiectate (9 months / 2017)
III.1. Optimal ALS’s optimization and robust-ness’improvement. Study of the sensor errors and atmospheric disturbances’ influence.
III.2. Adaptive components’ design; neural networks’ design and train (longitudinal and lateral-directional planes).
III.3. Design of the adaptive control laws (longitudinal and lateral-directional planes).
III.4. Design the adaptive automatic landing subsystems (longitudinal and lateral-directional planes).
III.5. Interconnection of the two adaptive subsystems and the obtaining of the new adaptive automatic landing system.
III.6. Software implementation of the adaptive automatic landing system.
III.7. Adaptive ALS’s optimization and robustness’improvement. Study of the sensor errors and atmospheric disturbances’ influence.
III.8. Comparative studies using the two automatic landing systems.
III.9. Dissemination of results.
Expected results:
Stage I (2015 - 3 months):
- Research report Download PDF
Stage II (2016 - 12 months):
- New optimal automatic landing system software package (software instruments)
- Publishing of 2 ISI Journal papers and 4 database papers BDI
- Organize a special session within ICATE’16 IEEE Conference
- Research report Download PDF
Stage III (2017 - 9 months):
- New adaptive automatic landing system software package (software instruments)
- Publishing of 1 ISI Journal paper and 6 database papers
- Final research report Download PDF
Results (published papers):
1. Lungu, R., Lungu, M. Design of Automatic Landing Systems using the H-inf Control and the Dynamic Inversion. Journal of Dynamic Systems, Measurement and Control (Transactions of ASME), vol. 138, no. 2, 5 pp, 2016, ISSN: 0022-0434 (ISI Journal). Databases: ISI Web of Science.
2. Lungu, R., Lungu, M. Adaptive Flight Control Law Based on Neural Networks and Dynamic Inversion for Micro Aerial Vehicles. Neurocomputing Journal, vol. 199, pp. 40-49, 2016, ISSN: 0925-2312 (ISI Journal). Databases: ISI Web of Science.
3. Lungu, M., Lungu, R., Tutunea, D. Control of Aircraft Landing using the Dynamic Inversion and the H-inf Control. 17th International Carpathian Control Conference (ICCC 2016), Tatranská Lomnica, Slovak Republic, May 29 - June 1, 2016, pp. 461-466. Databases:IEEE Xplore.
4. Lungu, M., Lungu, R., Preotu, O. Estimation of Aircraft State during Landing by means of Multiple Observers. 23th International Conference on Systems, Signals and Image Processing (IWSSIP 2016), 23-25 May 2016, Bratislava, Slovakia. Databases: IEEE Xplore.
5. Lungu, M., Lungu, R. Reduced-Order Multiple Observer for Aircraft State Estimation during Landing. 11th edition of the International Conference on Trends in Aerospace, Robotics, Manufacturing Systems, Mechanical Engineering, Bioengineering, Power and Energy Engineering, Materials Engineering, Jupiter, 29 iunie - 2 iulie 2016; Applied Mechanics and Materials, vol. 841, 2014, pp. 253-259, 2016, DOI:10.4028/www.scientific.net/AMM.841.253 ISSN: 1660-9336 (ISI Proceedings). Databases: ISI Web of Science.
6. Lungu, M., Lungu, R., Grigorie, L., Preotu, O. The Influence of Atmospheric Turbulences on Aircraft Landing Process. International Conference on Applied and Theoretical Electricity – ICATE 2016 (ISI Proceedings). Databases: ISI Web of Science, IEEE Xplore.
7. Tudosie, A., Butu, A. Aircraft Landing With Decelerated Approach (Longitudinal Movement Model). International Conference on Applied and Theoretical Electricity – ICATE 2016 (ISI Proceedings). Databases: ISI Web of Science, IEEE Xplore.
8. Lungu, R., Lungu, M. Aircraft Landing Control Using the H-inf Control and the Dynamic Inversion Technique. Chapter in the book „Automation and Control Trends”, ISBN 978-953-51-2671-3 (editors: Pedro Ponce, Arturo Molina Gutierrez, Luis M. Ibarra). Intech Publisher, 2016, pp. 101-120. Databases: ISI Web of Science.
9. Lungu, M., Lungu, R. The Control of Airplane Landing in Longitudinal and Lateral-directional Planes by using the H-inf Control. 18th International Carpathian Control Conference (ICCC 2017), Sinaia, România, 28-31 Mai 2017 (ISI Proceedings). Databases: ISI Web of Science, IEEE Xplore.
10. Lungu, M., Lungu, R. Complete Landing Autopilot having Control Laws Based on Neural Networks and Dynamic Inversion. 18th International Carpathian Control Conference (ICCC 2017), Sinaia, România, 28-31 Mai 2017 (ISI Proceedings). Databases: ISI Web of Science, IEEE Xplore.
11. Lungu, M. Automatic control of aircraft landing by using the H2/H∞ control technique. The 36th IASTED International Conference on Modelling, Identification and Control (MIC 2017), February 20-21, 2017, Innsbruck, Austria, DOI: 10.2316/P.2017.848-004, pag. 222-229. Databases: Scopus.
12. Lungu, M., Lungu, R. Landing Autopilot for the Control of Airplane by using the H-inf Control. 12th International Conference on Aerospace, Robotics, Mechatronics, Mechanical Engineering, Manufacturing systems, Neurorehabilitation and Bioengineering (OPTIROB) 29 Iunie-3 Iulie 2017, Jupiter. International Journal of Modeling and Optimization (IJMO), vol. 7, nr. 3, 2017, ISSN: 2010-3697. Databases: Google Scholar, EI (INSPEC, IET), Engineering & Technology Digital Library, Crossref, DOAJ.
13. Voicu, S., Butu, F.L. H-Infinity Design for Automatic Landing System. 12th International Conference on Aerospace, Robotics, Mechatronics, Mechanical Engineering, Manufacturing systems, Neuroreha-bilitation and Bioengineering (OPTIROB), June 29- July 3, 2017, Jupiter. International Journal of Modeling and Optimization (IJMO), vol. 7, nr. 3, 2017, ISSN: 2010-3697. Databases: Google Scholar, EI (INSPEC, IET), Engineering & Technology Digital Library, Crossref, DOAJ.
14. Lungu, R., Lungu, M. Automatic control of the micro aerial vehicles’ attitude and position. International Journal of Micro Aerial Vehicles, vol. 9, nr. 1, pag. 61-73, 2017, ISSN: 1756-8293 (ISI Journal). Databases: ISI Web of Science.
15. Lungu, R., Lungu, M. Automatic Landing System using Neural Networks and Radio-technical Subsystems. Chinese Journal of Aeronautics, vol. 30, nr. 1, pag. 399-411, 2017, ISSN: 1000-9361 (ISI Journal). Factorde impact relativ revista: 1.070. Databases: ISI Web of Science.
16. Lungu, M., Lungu, R. Landing Auto-pilots for Aircraft Motion in Longitudinal Plane using Adaptive Control Laws Based on Neural Networks and Dynamic Inversion. Asian Journal of Control, vol. 19, nr. 1, pag. 302-315, 2017, ISSN:1561-8625 (ISI Journal). Databases: ISI Web of Science.
Contact:
Project Manager: Lecturer PhD. Eng. Mihai Lungu
e-mail: Lma1312@yahoo.com, mlungu@elth.ucv.ro
University of Craiova
Facultaty of Electrical Engineering
Decebal B-dul, no. 107, Zip code: 200440, Craiova, Dolj, Romania