Optimal Solar Panel Area Computation and Temperature Tracking for a CubeSat System using Model Predictive Control
Keywords:
Aerospace Systems, CubeSat, Nonlinear MPC, Actuator Power, Nonlinear Dynamical Model, Feedback LinearizationAbstract
Recently, there has been a rising interest in small satellites such as CubeSats in the aerospace community due to their small size and cost-effective operation. It is challenging to ensure precision performance for satellites with minimum cost and energy consumption. To support maneuverability, the CubeSat is equipped with a propellant tank, in which the fuel must be maintained in the appropriate temperature range. Simultaneously, the energy production should be maximized, such that the other components of the satellite are not overheated. To meet the technological requirements, we propose a multicriteria optimal control design using a nonlinear dynamical thermal model of the CubeSat system. First, a PID control scheme with an anti-windup compensation is employed to evaluate the minimum heat flux necessary to keep the propellant tank at a given reference temperature. Secondly, a linearization-based controller is designed for temperature control. Thirdly, the optimization of the solar cell area and constrained temperature control is solved as an integrated nonlinear model predictive control problem using the quasilinear parameter varying form of the state equations. Several simulation scenarios for different power limits and solar cell coverage cases are shown to illustrate the trade-offs in control design and to show the applicability of the approach.
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2. Cheney L.J. Development of safety standards for CubeSat propulsion systems. Faculty of California Polytechnic State University. 2014. 215 p.
3. Lemmer K. Propulsion for CubeSats. Acta Astronautica. 2017. vol. 134. pp. 231–243.
4. Al-Hemeary N., Jaworski M., Kindracki J., Szederkényi G. Thermal model development for a CubeSat. Hungarian Journal of Industry and Chemistry. 2019. pp. 25–32.
5. Al-Hemeary N., Szederkényi G. Fuel tank temperature control of a time-varying CubeSat model. IEEE 17th World Symposium on Applied Machine Intelligence and Informatics. 2019. pp. 339–344.
6. Isidori A. Nonlinear control systems. Springer-Verla. 1995. 549 p.
7. Cox P.B. Towards efficient identification of linear parameter-varying state-space models. PhD dissertation, Eindhoven University of Technology. 2018. 354 p.
8. Mohammadpour J., Scherer C.W. Control of linear parameter varying systems with applications. Springer Science & Business Media. 2012. 550 p.
9. Rotondo D., Nejjari F., Puig V. Quasi-LPV modeling, identification and control of a twin rotor mimo system. Control Engineering Practice. 2013. vol. 21. no. 6. pp. 829–846.
10. Atam E., Mathelin L., Cordier L. A hybrid approach for control of a class of input-affine nonlinear systems. Int. J. Innov. Comput., Inf. Control. 2014. vol. 10. no. 3. pp. 1207–1228.
11. Tu K.H., Shamma J.S. Nonlinear gain-scheduled control design using set-valued methods. Proceedings of the 1998 American Control Conference. ACC (IEEE Cat. No. 98CH36207). 1998. vol. 2. pp. 1195–1199.
12. Hoffmann C. Linear parameter-varying control of systems of high complexity. Technische Universität Hamburg-Harburg. 2016. 402 p.
13. Shin J.Y. Quasi-linear parameter varying representation of general aircraft dynamics over non-trim region. National Institute of Aerospace. 2007. 32 p.
14. Cisneros P.S.G., Voss S., Werner H. Efficient nonlinear model predictive control via quasi-lpv representation. 2016 IEEE 55th Conference on Decision and Control (CDC). 2016. pp. 3216–3221.
15. Marcos A., Balas G.J. Development of linear-parameter-varying models for aircraft. Journal of Guidance, Control, and Dynamics. 2004. vol. 27. no. 2. pp. 218–228.
16. Kouvaritakis B., Cannon M. Nonlinear predictive control: theory and practice. 2001
17. Allgöwer F., Zheng A. Nonlinear Model Predictive Control. Birkhäuser. 2000. 472 p.
18. Cisneros P.S.G., Werner H. Stabilizing model predictive control for nonlinear systems in input-output quasi-LPV form. 2019 American Control Conference (ACC). 2019. pp. 1002–1007.
19. Abbas H.S., Hanema J., Tóth R., Mohammadpour J., Meskin N. An improved robust model predictive control for linear parameter-varying input-output models. International Journal of Robust and Nonlinear Control. 2018. vol. 28(3). pp. 859–880.
20. Cisneros P.S.G., Voss S., Werner H. Efficient nonlinear model predictive control via quasi-LPV representation. 2016 IEEE 55th Conference on Decision and Control (CDC). 2016. pp. 3216–3221.
21. Abbas H.S. et al. A robust MPC for input-output LPV models. IEEE Transactions on Automatic Control. 2016. vol. 61. no. 12. pp. 4183–4188.
22. Hanema J., Tóth R., Lazar M. Stabilizing non-linear MPC using linear parametervarying representations. 2017 IEEE 56th Annual Conference on Decision and Control (CDC). 2017. pp. 3582–3587.
23. Camacho E.F., Alba C.B. Model predictive control. Springer Science & Business Media. 2013. 405 p.
24. Maciejowski J.M. Predictive control: with constraints. Pearson education. 2002. 353 p.
25. Rossiter J.A. Model-based predictive control: a practical approach. CRC press. 2017. 344 p.
26. Rawlings J.B., Mayne D.Q. Model Predictive Control: Theory and Design. Nob Hill Publishing. 2010. 544 p.
27. Bemporad A., Ricker N.L., Morari M. Model Predictive Control Toolbox User’s Guide. The MathWorks. 2019. 37 p.
28. Al-Hemeary N., Szederkényi G. Power regulation and linearization-based control design of a small satellite. 2019 12th International Conference on Developments in eSystems Engineering 2019. pp. 646–650.
29. Fortescue P., Swinerd G., Stark J. Spacecraft systems engineering. John Wiley & Sons. 2011. 724 p.
30. Różowicz S., Zawadzki A. Input-output transformation using the feedback of nonlinear electrical circuits: Algorithms and linearization examples. Mathematical Problems in Engineering. 2018. vol. 2018. 13 p.
31. Cheng D., Isidori A., Respondek W., Tarn T.J. Exact linearization of nonlinear systems with outputs. Mathematical systems theory. 1988. vol. 21(1). pp. 63–83.
32. Zheng A. Some practical issues and possible solutions for nonlinear model predictive control. Nonlinear Model Predictive Control. 2000. pp. 129–143.
33. Polcz P. Optimal solar panel area computation for a CubeSat system using an offline MPC design. GitHub repository. 2020. Available at: https://github.com/ppolcz/CubeSat_optimal_sparea (accessed: 02.03.2020).
Published
2020-06-01
How to Cite
Al-Hemeary, N., Polcz, P., & Szederkényi, G. (2020). Optimal Solar Panel Area Computation and Temperature Tracking for a CubeSat System using Model Predictive Control. SPIIRAS Proceedings, 19(3), 564-593. https://doi.org/10.15622/sp.2020.19.3.4
Section
Robotics, Automation and Control Systems
Copyright (c) Навар Аль-Хемеари, Петер Полц, Габор Седеркеньи
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