Integrated Adaptive Guidance and Control for Re-Entry Vehicles with Flight Test Results

Schierman, John D.; Ward, Dave; Hull, Jason R.; Gandhi, Neha; Oppenheimer, Michael W.; Doman, David B.

doi:10.2514/1.10344

Cited by 135 publications

(68 citation statements)

References 9 publications

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“…For example, an actuator that is locked in a faulty position could be systematically treated by setting the lower and upper constraint limits to the locked value. Alternatively, the preferred control vector could also be set to locked actuator values as proposed in (Schierman, Ward, Hull, Gandhi, Oppenheimer & Doman 2004). A systematic method that also changes the weights in the pseudo-inverse in order to ensure that faults are well distributed among the fault-free effectors without reconfiguring the high level controller is proposed in (Alwi & Edwards 2008).…”

Section: Dynamics and Fault Tolerancementioning

confidence: 99%

“…Flight tests with the Boeing X-40A reusable launch vehicle using reconfigurable control allocation based linear programming is reported in (Schierman et al 2004). In case of faulty locked actuators, it is in (Schierman et al 2004) proposed to set the associated elements of the preferred control vector u p to the locked actuator positions.…”

Section: Spacecraftmentioning

confidence: 99%

“…In case of faulty locked actuators, it is in (Schierman et al 2004) proposed to set the associated elements of the preferred control vector u p to the locked actuator positions.…”

Section: Spacecraftmentioning

confidence: 99%

“…(Yang, Zhong & Shen 2009). Using the pseudo-inverse solution as a preferencevector u p allows one to analytically represent the control allocator in a robustness analysis of the system that is valid as long as no single axis is saturated and the commanded accelerations are feasible, (Schierman et al 2004). This facilitates the verification and validation process that must be completed prior to flight testing when using optimization based control allocation methods.…”

Section: Aircraftmentioning

confidence: 99%

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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: Dynamics and Fault Tolerancementioning

confidence: 99%

Section: Spacecraftmentioning

confidence: 99%

“…In case of faulty locked actuators, it is in (Schierman et al 2004) proposed to set the associated elements of the preferred control vector u p to the locked actuator positions.…”

Section: Spacecraftmentioning

confidence: 99%

Section: Aircraftmentioning

confidence: 99%

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Control allocation—A survey

2013

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“…Schierman et al [11] [12] [13] proposed an A & L guidance system that creates a set of optimal trajectories offline based on indirect optimization techniques. Then, the best trajectory is selected from the family of optimum paths to guide the RLV from the current state to the desired state.…”

Section: Several Researchers Have Developed Guidance Systems For the mentioning

confidence: 99%

Automatic Approach and Landing Trajectory Planner for Unpowered Reusable Launch Vehicle

Al-Bakri¹,

Kluever²

2017

AAST

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A new guidance scheme for the approach and landing (A & L) phase of an unpowered reusable launch vehicle (RLV) has been developed. The main advantage of the new guidance is the use of glide-efficiency factor as the independent variable to compute the geometrical flare parameters by a set of analytical functions. The trajectory-planning algorithm generates its reference geometry based on the steep and shallow subphases, respectively. During the steep segment, the quasi-equilibrium glide (QEG) solution, which assumes a constant dynamic pressure and flight-path angle during the flight, is used to create the flight reference while the shallow segment is defined by polynomial functions for altitude and dynamic pressure profiles. Standard linearization methods are used to design a closed-loop command in order to track the QEG profile. Furthermore, proportion-derivative (PD) control is used to modulate the lift coefficient during the flare flight. Once the reference trajectory is created, a closed-loop simulation is obtained to track the reference. Off-nominal conditions, in terms of change in initial glide-efficiency factor, dynamic pressure, flight-path angle, and altitude are tested using a Monte-Carlo simulation. The simulated results demonstrate the effectiveness of the proposed algorithm to land the vehicle successfully under large dispersions of glide-efficiency factor.

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