A robust fuzzy control for spacecraft orbit transfer is investigated in this paper, which includes output tracking, disturbance attenuation, input constraint, and pole assignment. Spacecraft dynamics is modeled using the Keplerian two-body problem in polar coordinates, enabling long-range maneuvers in a circular orbit, while the relative motion between spacecraft is particularly useful for proximity operations. Utilizing the fuzzy theory approach, a Takagi-Sugeno (T-S) fuzzy model is constructed through the gain-scheduling technique, which involves selecting multiple operating points to develop a linearized model of the equation of motion. An output tracking reference is incorporated to enable the spacecraft to follow and reach its final position. Using the parallel distributed compensation (PDC) approach, sufficient conditions for the design of a robust fuzzy controller are derived to guarantee the asymptotic stability of the closed-loop system. This controller minimizes fuel consumption, attenuates external disturbances, and restricts system poles within a specified region. Using Lyapunov theory, the orbit transfer problem is addressed as a convex optimization problem, with the controller’s objectives expressed as linear matrix inequalities (LMIs). Numerical simulations demonstrate the effectiveness of the proposed controller, efficiently transferring the spacecraft to its final orbit while using limited thrust and minimizing the disturbance index.
MontenbruckOGillE.Satellite orbits: models, methods and applications. Berlin: Springer-Verlag, 2000.
2.
FelisiakP.Control of spacecraft for rendezvous maneuver in an elliptical orbit. PhD dissertation, Wroclaw University of Technology, Poland, 2015.
3.
DongKLuoJLimonD.A novel stable and safe model predictive control framework for autonomous rendezvous and docking with a tumbling target. Acta Astronaut2022; 200: 176–187.
4.
SinglaPSubbaraoKJunkinsJL.Adaptive output feedback control for spacecraft rendezvous and docking under measurement uncertainty. J Guid Control Dyn2006; 29(4): 892–902.
5.
HeSWangWLinD, et al. Robust output feedback control design for autonomous spacecraft rendezvous with actuator faults. Proc IMechE, Part I: J Systems and Control Engineering2016; 230(6): 578–587.
6.
YangXCaoXGaoH.Sampled-data control for relative position holding of spacecraft rendezvous with thrust nonlinearity. IEEE Trans Ind Electron2012; 59(2): 1146–1153.
7.
ChilaliMGahinetP.H infinity design with pole placement constraints: an LMI approach. IEEE Trans Automat Contr1996; 41(3): 358–436. 7
8.
HongSKNamY.Stable fuzzy control system design with pole-placement constraint: an LMI approach. Comput Ind2003; 51(1): 1–11.
9.
HuijunGXueboYPengS.Multi-objective robust control of spacecraft rendezvous. IEEE Trans Control Syst Technol2009; 17(4): 794–802.
10.
ZhangKDuanGR.Robust dynamic output feedback control for spacecraft rendezvous with poles and input constraint. Int J Syst Sci2016; 48: 1022–1034.
11.
ZhouYYanYHuangX, et al. Multi-objective and reliable output feedback control for spacecraft hovering. Proc IMechE, Part G: J Aerospace Engineering2014; 229(10): 1798–1812.
12.
GaoXTeoKLDuanGR.Non-fragile robust control for uncertain spacecraft rendezvous system with pole and input constraints. Int J Control2012; 85(7): 933–941.
13.
TakagiTSugenoM.Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern1985; SMC-15(1): 116–132.
14.
TanakaKWangHO.Fuzzy control systems design and analysis: a linear matrix inequality approach. New York: John Wiley & Sons, Inc, 2001.
15.
BenzaouiaAHajjajiA.Advanced Takagi-Sugeno fuzzy systems. Cham: Springer International Publishing, 2016.
16.
ZhangZDongJ.Fault-tolerant containment control for IT2 fuzzy networked multiagent systems against denial-of-service attacks and actuator faults. IEEE Trans Syst Man Cybern Syst2021; 52(4): 2213–2224.
17.
SuXPhilip ChenCLLiuZ.Adaptive fuzzy control for uncertain nonlinear systems subject to full state constraints and actuator faults. Inf Sci2021; 581: 553–566.
18.
ZhangZDongJ.Robust output-feedback online optimization control for T–S fuzzy systems via differential evolution algorithm. IEEE Trans Fuzzy Syst2023; 31(11): 4109–4120.
19.
FarboodMEchreshaviZShasadeghiM, et al. Asynchronous robust fuzzy event-triggered control of nonlinear systems. J Franklin Inst2023; 360(13): 9904–9923.
20.
WangQXueA.Robust control for spacecraft rendezvous system with actuator unsymmetrical saturation: a gain scheduling approach. Int J Control2018; 91(6): 1241–1250.
21.
MaYKJiHB.Robust control for spacecraft rendezvous with disturbances and input saturation. Int J Control Autom Syst2015; 13(2): 353–358. http://www.springer.com/12555
22.
WuSNZhouWYTanSJ, et al. Robust control for spacecraft rendezvous with a noncooperative target. Sci World J2013; 2013(1): 579703.
23.
WangQDuanGR.Output feedback stabilization of spacecraft autonomous rendezvous subject to actuator saturation. Int J Autom Comput2016; 13: 428–437.
24.
ZhaoLJiaY.Multi-objective output feedback control for autonomous spacecraft rendezvous. J Franklin Inst2014; 351(2014): 2804–2821.
25.
ZavoliAColasurdoG.Indirect optimization of finite-thrust cooperative rendezvous. J Guid Control Dyn2014; 38(1): 304–314.
26.
ArthurEBHoJC.Applied optimal control: optimization, estimation, and control. New York: Taylor & Francis Group, LLC, 1975.
27.
FujimoriATsunetomoHWuZY.Gain-scheduled control using fuzzy logic and its application to flight control. J Guid Control Dyn1999; 22(1): 175–178.
28.
Guang-RenDHai-HuaY.LMIs in control systems analysis, design and applications. London: CRC Press Taylor & Francis Group, 2013.
29.
TuanHDApkarianPNarikiyoT, et al. Parameterized linear matrix inequality techniques in fuzzy control system design. IEEE Trans Fuzzy Syst2001; 09(2): 324–332.
30.
HugangH. T-S fuzzy controller in consideration of input constraint. In: Second international conference on innovative computing, information and control (ICICIC 2007), 2007, pp.15. DOI: 10.1109/ICICIC.2007.555
31.
AssawinchaichoteWNguangSKShiP, et al. Fuzzy state-feedback control design for nonlinear systems with D-stability constraints: an LMI approach. Math Comput Simul2008; 78(4): 514–531.
