An adaptive intelligent control strategy based on a brain emotional learning model is investigated in the application of the rate regulation of suborbital reentry payloads. Because of nonlinear time-varying dynamics of these payloads, choosing an appropriate control mechanism and stability strategy can be an engineering challenge. Thus in a new approach, a moving mass control system in conjunction with brain emotional learning-based intelligent control is used to fulfill payload de-tumbling. The contribution of brain emotional learning-based intelligent control in handling the nonlinear time-varying dynamics is shown by comparison with results obtained from a linear proportional–integral controller. The results demonstrate excellent performance of brain emotional learning-based intelligent control in learning dynamic couplings and improvement of behavior without any considerable control system complexity.
MohammadiATayefiMKashaniH. Rate regulation of a suborbital reentry payload by moving mass actuators. Proc IMechE, Part G: J Aerospace Engineering. Epub ahead of print January 6, 2012. DOI: 10/1177/0954410011429231.
2.
YoshinagaTTakahashiSSoedaK. Transient coning moment acting on cone cylinders at high angels of attack. In: AIAA atmospheric flight mechanics conference, Austin, TX, 11–14 August2003. Redston, VA: AIAA.
RogersJCostelloM. Control authority of a projectile equipped with a controllable internal translating mass. J Guid Contr Dyn2008; 31(5): 1323–1333.
5.
PetsopoulosTReganFBarlowJ. Moving mass roll control system for fixed-trim re-entry vehicle. J Spacecraft Rockets1996; 33(1): 54–61.
6.
MenonPKSweridukGDOhlmeyerEJ. Integrated guidance and control of moving-mass actuated kinetic warheads. J Guid Contr Dyn2004; 27(1): 118–127.
7.
MenonPKVaddiSSOhlmeyerEJ. Finite-horizon robust integrated guidance-control of a moving-mass actuated kinetic warhead. In: AIAA guidance, navigation, and control conference and exhibit, Keystone, CO, 21–24 August2006.
8.
WangSYangMWangZ. Moving mass trim control system design for spinning vehicles. In: 6th world congress on intelligent control & automation, Dalian, China, 21–23 June2006.
9.
BolleACirciC. Solar sail attitude control through in-plane moving masses. Proc IMechE, Part G: J Aerospace Engineering2008; 222(1): 81–94.
10.
RogersJCostelloM. A variable stability projectile using an internal moving mass. Proc IMechE, Part G: J Aerospace Engineering2009; 223(7): 927–938.
11.
ZadehLA. Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans Syst Man Cybern1973; 3(1): 28–44.
12.
RumelhartDEMcClellandJLGroupP. Parallel distributed processing. Cambridge, MA: MIT Press, 1986.
13.
KimBSCaliseAJ. Nonlinear flight control using neural networks. J Guid Contr Dyn1997; 20(1): 26–33.
14.
LeuYGWangWYLeeTT. Robust adaptive fuzzy-neural controllers for uncertain nonlinear systems. IEEE Trans Robot Autom1999; 15(5): 805–817.
15.
MorenJBalkeniusC. A computational model of emotional learning in the amygdala. In: From animals to animals 6, Proceedings of the 6th international conference on the simulation of adaptive behavior, Paris, France, 11℃15 September2000. Cambridge, MA: MIT Press, 2000.
16.
LucasCShahmirzadiDSheikholeslamiN. Introducing BELBIC: brain emotional learning based intelligent controller. Int J Intell Autom Soft Comput2004: 10(1): 11–22.
17.
MohammdiMRLucasCNajarAB. A novel controller for a power system based BELBIC (brain emotional learning based intelligent controller). In: World automation congress, 28 June–1 July2004, pp.409–420.
18.
MohammdiMRLucasCAraabiB. Intelligent modeling and control of washing machine using LLNF modeling and modified BELBIC. In: International conference on control and automation, Budapest, Hungary, 2005, pp.812–817.
19.
MohammdiMRLucasCAraabiBN. Implementation of emotional controller for interior permanent magnet synchronous motor drive. In: Industry applications conference, 8–12 October2006, vol. 4, pp.1767–1774.
20.
SadeghiehASazgarHGoodarziK. Identification and real-time position control of a servo-hydraulic rotary actuator by means of a neurobiologically motivated algorithm. ISA Trans2012; 51(1): 208–219.
21.
KhalilianMAbediADeris ZadehA. Position control of hybrid stepper motor using brain emotional controller. In: 2nd international conference on advances in energy engineering (ICAEE), Energy Procedia, 2012. Elsevier.
RouhaniHJaliliMAraabiBN. Brain emotional learning based intelligent controller applied to neurofuzzy model of micro-heat exchanger. Expert Syst Applic2007; 32: 911–918.
24.
ZhenZYWangDBWangZS. Flight simulation servo system based on learning based intelligent controller. In: 4th international conference on impulse and hybrid dynamic systems, Nanning, China, 20–22 July2007, pp.1372–1375.
25.
HuYZhenZYWangZS. Brain emotional learning based adaptive identification method for nonlinear dynamic system. In: 2008 Chinese control and decision conference, Yantai, China, 2–4 July2008, pp.3498–3501.
26.
HuangGHZhenZYWangDB. Brain emotional learning based intelligent controller for nonlinear system. In: 2nd IEEE international symposium on intelligent information technology application (IITA ’08), Shanghai, China, 20–22 December2008, vol. 2, pp.660–663.
27.
JafarzadehSMirheidariRJahed MotlaghMR. Intelligent autopilot control design for a 2-DOF. Int J Comput Commun Contr2006; III(Suppl.: Proc ICCCC 2008): 337–342.
28.
KaneTRLevinsonDA. Dynamics: theory and applications. New York: McGraw-Hill, 1985.
29.
SchuabHJunkinsJL. Analytical mechanics of space systems, AIAA education series. Reston, VA: American Institute of Aeronautics and Astronautics, 2003.
30.
ZipfelPH. Modeling and simulation of aerospace vehicle dynamics, AIAA education series. Reston, VA: American Institute of Aeronautics and Astronautics, 2000.
