Quantized Control Allocation of Reaction Control Jets and Aerodynamic Control Surfaces

Doman, David B.; Gamble, Brian J.; Ngo, Anhtuan D.

doi:10.2514/1.37312

Cited by 73 publications

(34 citation statements)

References 13 publications

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“…(Doman, Gamble & Ngo 2009) propose a control allocation approach to optimally combine the use of (discrete) pulsed reaction control jets with (continuous) control surfaces in spacecraft transitions from exo-atmospheric to endo-atmospheric flight, using mixed-integer linear programming.…”

Section: Spacecraftmentioning

confidence: 99%

Control allocation—A survey

2013

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The control algorithm hierarchy of motion control for over-actuated mechanical systems with a redundant set of effectors and actuators commonly includes three levels. First, a high-level motion control algorithm commands a vector of virtual control efforts (i.e. forces and moments) in order to meet the overall motion control objectives. Second, a control allocation algorithm coordinates the different effectors such that they together produce the desired virtual control efforts, if possible. Third, low-level control algorithms may be used to control each individual effector via its actuators. Control allocation offers the advantage of a modular design where the high-level motion control algorithm can be designed without detailed knowledge about the effectors and actuators. Important issues such as input saturation and rate constraints, actuator and effector fault tolerance, and meeting secondary objectives such as power efficiency and tear-and-wear minimization are handled within the control allocation algorithm. The objective of the present paper is to survey control allocation algorithms, motivated by the rapidly growing range of applications that have expanded from the aerospace and maritime industries, where control allocation has its roots, to automotive, mechatronics, and other industries. The survey classifies the different algorithms according to two main classes based on the use of linear or nonlinear models, respectively. The presence of physical constraints (e.g input saturation and rate constraints), operational constraints and secondary objectives makes optimization-based design a powerful approach. The simplest formulations allow explicit solutions to be computed using numerical linear algebra in combination with some logic and engineering solutions, while the more challenging formulations with nonlinear models or complex constraints and objectives call for iterative numerical optimization procedures. Experiences using the different methods in aerospace, maritime, automotive and other application areas are discussed. The paper ends with some perspectives on new applications and theoretical challenges.

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Section: Spacecraftmentioning

confidence: 99%

Control allocation—A survey

2013

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“…The coordination of multi-actuator and redundant control makes the compound controller design for RLV more become critical [3]. Meanwhile, the attitude controller comes up with control allocation issues [4]- [6]. According to the flight mission and control efficiency, the compound control strategy of planning the control modes of RCS and aerosurfaces should be designed carefully to meet aerodynamic and control constraints of different flight phases.…”

Section: Introductionmentioning

confidence: 99%

A RLV nonlinear optimal compound controller design

Guangxue

2014

Proceeding of the 11th World Congress on Intelligent Control and Automation

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The reusable launch vehicle (RLV) has strongly nonlinear, fast time-varying characters and multiple control modes, which challenge attitude control in task-adaptability greatly. A nonlinear RCS/aerosurfaces compound controller control scheme was presented. First a RLV model equipped with reaction control system (RCS) and aerosurfaces is developed. Then fast and slow loops control system is designed based on time-scale separate theory. The optimal control law was implemented to generate optimal control moments. The control allocation maps the control moment commands into thrusters and aerodynamic effectors' commands. The daisy-chain combination method is applied to the control allocation to deal with the harmonization problem of the RCS and aerosufaces. The Results show the compound control scheme meets the attitude tracking performance requirements. Simulation demonstrates that the nonlinear RCS/aerosurfaces compound controller saves RCS fuel effectively and achieves coordinated control between RCS and aerosurfaces. The control scheme can also be used in other reentry vehicles or aerospace planes.

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“…This additional degree of freedom requires special consideration in the guidance and control system. In previous researches, the focus is primarily on designing missile autopilot 2,3 , which is used to respond the acceleration commands created by conventional proportional navigation (PN) guidance law or its derivation. At the end of the terminal homing phase, the RCS is initiated to decrease the miss distance and perform a hit-to-kill.…”

mentioning

confidence: 99%

Effect of Reaction-jet Thrust on Homing Performance of Endoatmospheric Interceptors

Cheng

et al. 2010

AIAA Guidance, Navigation, and Control Conference

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For a low altitude endoatmospheric interceptor steered by aerodynamic fins and reaction jets, the propellant of the reaction-jet control system (RCS) is finite. An increase in the RCS thrust leads to a decrease in the maximum operation time. In this paper, the interception of a maneuvering tactical ballistic missile (TBM) is formulated as a zero-sum pursuit evasion game. Then, game space structure is employed to investigate the effect of the RCS thrust on homing performance. It's shown and demonstrated via simulation that, under propellant limits, a bigger RCS thrust can not guarantee a higher homing performance, especially when the target has high maneuverability. NomenclatureV = velocity ( m s ) X = state vector max , b a a = maximum acceleration and acceleration of the interceptor body ( 2 m s ) , u v = acceleration command of missile and target ( 2 m s ) T d = direct lift coefficient of the tail τ = guidance time constant ( s ) t = interception time (s) , RS RE t t = RCS start and end time ( s ) t Δ = RCS maximum operation time ( s ) Z = zero-effort miss distance ( m ) , θ λ = flight-path angle, angle of line-of-sight ( rad ), μ ε = maneuverability and dynamics ratio Subscripts 0, f = initial and final P, E = interceptor (pursuer) and target (evader) T, J = aero-tail and reaction-jet , T J P P = tail and RCS of the interceptor

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Quantized Control Allocation of Reaction Control Jets and Aerodynamic Control Surfaces

Cited by 73 publications

References 13 publications

Control allocation—A survey

Control allocation—A survey

A RLV nonlinear optimal compound controller design

Effect of Reaction-jet Thrust on Homing Performance of Endoatmospheric Interceptors

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