Active disturbance rejected predictive functional control for space vehicles with RCS

doi:10.21629/JSEE.2018.05.13

Abstract

Abstract:

Reaction control system (RCS) is a powerful and efficient actuator for space vehicles attitude control, which is typically characterized as a pulsed unilateral effector only with two states (off/on). Along with inevitable internal uncertainties and external disturbances in practice, this inherent nonlinear character always hinders space vehicles autopilot from pursuing precise tracking performance. Compared to most of pre-existing methodologies that passively suppress the uncertainties and disturbances, a design based on predictive functional control (PFC) and generalized extended state observer (GESO) is firstly proposed for three-axis RCS control system to actively reject that with no requirement for additional fuel consumption. To obtain a high fidelity predictive model on which the performance of PFC greatly depends, the nonlinear coupling multiple-input multiple-output (MIMO) flight dynamics model is parameterized as a state-dependent coefficient form. And based on that, a MIMO PFC algorithm in state space domain for a plant of arbitrary orders is deduced in this paper. The internal uncertainties and external disturbances are lumped as a total disturbance, which is estimated and cancelled timely to further enhance the robustness. The continuous control command synthesised by above controller-rejector tandem is finally modulated by pulse width pulse frequency modulator (PWPF) to on-off signals to meet RCS requirement. The robustness and feasibility of the proposed design are validated by a series of performance comparison simulations with some prominent methods in the presence of significant perturbations and disturbances, as well as measurement noise.

Key words: reaction control system (RCS), predictive functional control (PFC), generalized extended state observer (GESO), pulse width pulse frequency (PWPF), multiple-input multiple-output (MIMO)

Jiayi TIAN, Shifeng ZHANG. Active disturbance rejected predictive functional control for space vehicles with RCS[J]. Journal of Systems Engineering and Electronics, 2018, 29(5): 1022-1035.

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References 33

1	MARTIN F H, BATTIN R H. Computer-controlled steering of the Apollo spacecraft. Journal of Spacecraft and Rockets, 1967, 5 (4): 400- 407.
2	SUN B C, PARK Y K, ROH W R, et al. Attitude controller design and test of Korea Space Launch Vehicle-I upper stage. International Journal Aeronautical and Space Sciences, 2010, 11 (4): 303- 312. doi: 10.5139/IJASS.2010.11.4.303
3	GILBERTO A J, LUIZ S M, SANTANA A C. Optimal onoff attitude control for the Brazilian multimission platform satellite. Mathematical Problems in Engineering, 2009. doi: 10.1155/2009/750945
4	TING T, FIANAGAN N. Space shuttle transition controller OMS-TVC and RCS jet thruster stability analysis. Proc. of the 27th AIAA/SAE/ASME/ASEE Joint Propulsion Conference, 1991: 1-12.
5	CALHOUN P C, QUEEN E M. Entry vehicle control system design for the Mars science laboratory. Journal of Spacecraft and Rockets, 2006, 43 (2): 324- 329. doi: 10.2514/1.19650
6	BROWN T. In-flight tuning of the Cassini RCS attitude controller. Proc. of the AIAA Guidance, Navigation, and Control Conference, 2011: AIAA 2011-6550.
7	DOMAN D B, GAMBLE B J, NGO A D. Quantized control allocation of reaction control jets and aerodynamic control surfaces. Journal of Guidance, Control, and Dynamics, 2009, 32 (1): 13- 24. doi: 10.2514/1.37312
8	THURMAN S W, FLASHNER H. Robust digital autopilot design for spacecraft equipped with pulse-operated thrusters. Journal of Guidance, Control, and Dynamics, 1996, 19 (5): 1047- 1055. doi: 10.2514/3.21744
9	JOHANSEN C T, DANEHY P M, ASHCRAFT S W, et al. PLIF study of Mars Science Laboratory capsule reaction control system jets. Proc. of the 41st AIAA Fluid Dynamics Conference, 2011: 27-30.
10	WENG L G, LI B, CAI W C, et al. Human memory/learning inspired approach for attitude control of crew exploration vehicles (CEVs). Proc. of the American Control Conference, 2007: 3843-3848.
11	CHIANG R, BRECKENRIDGE W, WONG E. Self-tuning thruster control for Cassini spacecraft. Proc. of the AIAA Guidance, Navigation, and Control Conference, 1996: AIAA 1996-3822.
12	SINGH T. Fuel/time optimal control of the benchmark problem. Journal of Guidance, Control, and Dynamics, 1995, 18 (6): 1225- 1231. doi: 10.2514/3.21534
13	HABLANI H B. Multiaxis tracking and attitude control of flexible spacecraft with reaction jets. Journal of Guidance, Control, and Dynamics, 1994, 17 (4): 831- 839. doi: 10.2514/3.21273
14	PARADISO J A. Adaptable method of managing jets and aerosurfaces for aerospace vehicle control. Journal of Guidance, Control, and Dynamics, 1991, 14 (1): 44- 50. doi: 10.2514/3.20603
15	KRØVEL T. Optimal tunning of PWPF modulator for attitude control. Trondheim, Norway: Norwegian University of Science and Technology, 2005.
16	CUI L, YANG Y. Disturbance rejection and robust leastsquares control allocation in flight control system. Journal of Guidance, Control, and Dynamics, 2011, 34 (6): 1632- 1643. doi: 10.2514/1.52234
17	GENG J, SHENG Y Z, LIU X D. Finite-time sliding mode attitude control for a reentry vehicle with blended aerodynamic surfaces and a reaction control system. Chinese Journal of Aeronautics, 2014, 27 (4): 964- 976. doi: 10.1016/j.cja.2014.03.013
18	RICHALET J. Industrial applications of model based predictive control. Automatica, 1993, 29 (5): 1251- 1274. doi: 10.1016/0005-1098(93)90049-Y
19	HE F H, MA K M, YANG B Q, et al. Model predictive control for a missile with blended lateral jets and aerodynamic fins. Proc. of the 27th Chinese Control Conference, 2008: 430-434.
20	BURCHETT B. Predictive optimal pulse-jet control for symmetric projectiles. Proc. of the AIAA Atmospheric Flight Mechics Conference, 2014: AIAA 2014-0883.
21	LEOMANNI M, ROGERS E, GABRIEL S B. Explicit model predictive control approach for low-thrust spacecraft proximity operations. Journal of Guidance, Control, and Dynamics, 2014, 37 (6): 1780- 1790. doi: 10.2514/1.G000477
22	YANG W, BOYD S P. Fast model predictive control using online optimization. IEEE Trans. on Control Systems Technology, 2010, 18 (2): 267- 278.
23	MAALOUF A I. Improving the robustness of a parallel robot using predictive functional control (PFC) tools. Proc. of the 45th IEEE Conference on Decision and Control, 2006: 6468-6473.
24	ROSSITER J A, RICHALET J. Handling constraints with predictive functional control of unstable processes. Proc. of the American Control Conference, 2002: 4746-4751.
25	SATOH T, KANEKO K, SAITO N. Performance improvement of predictive functional control: a disturbance observer approach. Proc. of the IECON 37th Annual Conference on IEEE Industrial Electronics Society, 2011: 669-674.
26	LIU H X, LI S H. Speed control for PMSM servo system using predictive functional control and extended state observer. IEEE Trans. on Industrial Electronics, 2012, 59 (2): 1171- 1183. doi: 10.1109/TIE.2011.2162217
27	ZHANG Y H, YANG H B, JIANG Z Y, et al. Robust predictive functional control for flight vehicles based on nonlinear disturbance observer. International Journal of Aerospace Engineering, 2015, 878971.
28	LI S H, YANG J, CHEN W H, et al. Generalized extended state observer based control for systems with mismatched uncertainties. IEEE Trans. on Industrial Electronics, 2012, 59 (12): 4792- 4802. doi: 10.1109/TIE.2011.2182011
29	SHE J H, FANG M X, OHYAMA Y, et al. Improving disturbance-rejection performance base on an equivalentinput-disturbance approach. IEEE Trans. on Industrial Electronics, 2008, 55 (1): 380- 389. doi: 10.1109/TIE.2007.905976
30	HUNT L R, MEYER G, SU R. Noncausal inverses for linear systems. IEEE Trans. on Automatic Control, 1996, 41 (4): 608- 611. doi: 10.1109/9.489285
31	LI T, ZHANG S F, YANG H B, et al. Robust missile longitudinal autopilot design based on equivalent-input-disturbance and generalized extended state observer approach. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 2015, 229 (6): 1025- 1042. doi: 10.1177/0954410014543715
32	ANTHONY T C, WIE B, CARROLL S. Pulse-modulated control synthesis for a flexible spacecraft. Journal of Guidance, Control, and Dynamics, 1990, 13 (6): 1014- 1022. doi: 10.2514/3.20574
33	XU X Y, CAI Y L. Pulse-width pulse-frequency based optimal controller design for kinetic kill vehicle attitude tracking control. Applied Mathematics, 2011, 2 (5): 565- 574. doi: 10.4236/am.2011.25075

Parameter	Pitch channel	Yaw channel	Roll channel
Arm of force/m	0.437	0.437	0.240
Moment/N·m	8.74	8.74	4.8
Moment of inertia/kg·m²	2	2	1
Thruster force/N	20	20	20

Parameter	U_on	U_off	U_m	K	T
Setting	0.8	0.3	1	5	0.05

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