Minimization of Degradation Through Prognosis Based Control for a Damaged Aircraft Actuator

Mahulkar, Vishal; Adams, Douglas E.; Derriso, Mark M.

doi:10.1115/dscc2009-2787

Cited by 5 publications

(3 citation statements)

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“…In reality the load on a flight control actuator is depends on many variables related to the dynamics of the aircraft and the desired flight path (for e.g. see Mahulkar et al (2010), Karpenko andSepeheri (2003), andMcCormick (1995)).…”

Section: Mathematical Behavior Modelsmentioning

confidence: 99%

Dynamic Bayesian Networks for Prognosis

Bartram

Mahadevan

2013

PHM_CONF

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In this paper, a methodology for probabilistic prognosis of a system using a dynamic Bayesian network (DBN) is proposed. Dynamic Bayesian networks are suitable for probabilistic prognosis because of their ability to integrate information in a variety of formats from various sources and give a probabilistic representation of a system. Further, DBNs provide a platform naturally suited for seamless integration of diagnosis, uncertainty quantification, and prediction. In the proposed methodology, a DBN is used for online diagnosis via particle filtering, providing a current estimate of the joint distribution over the system variables. From this state estimate, future states of the system are predicted using the DBN and sequential Monte Carlo sampling. Prediction in this manner provides the necessary information to estimate the distribution of remaining use life (RUL). The DBN-based recursive prediction procedure may be used to estimate the system state between available measurements, when filtering is not possible. The prognosis procedure, which is system specific, is validated using a suite of offline hierarchical metrics. The prognosis methodology is demonstrated on a hydraulic actuator subject to a progressive seal wear that results in internal leakage between the chambers of the actuator.

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Section: Mathematical Behavior Modelsmentioning

confidence: 99%

Dynamic Bayesian Networks for Prognosis

Bartram

Mahadevan

2013

PHM_CONF

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“…For this purpose, several strategies have been proposed in the literature. Estimates of degradation of hydraulic actuators were employed in Mahulkar et al (2009) to minimize the degradation rate by means of control strategies with feedforward action. Reference model and reference management strategies were used in Jiang and Zhang (2002) to provide a form of reconfiguration in the event of failures caused by degradation of actuators.…”

Section: Introductionmentioning

confidence: 99%

Predictive Control for Systems with Loss of Actuator Effectiveness Resulting from Degradation Effects

Vieira

Galvão

Yoneyama

2015

J Control Autom Electr Syst

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Predictive control is a promising strategy for the mitigation of faults that result in more stringent operational constraints on the inputs and outputs of a system. In this context, the present work proposes a predictive control approach for accommodation of faults associated with the loss of actuator effectiveness. More specifically, it is assumed that the actuator is subject to degradation effects with progress rate proportional to the control effort. The loss of effectiveness occurs when the degradation reaches an intermediate threshold between the nominal operating condition and the total failure of the actuator. The resulting predictive control law involves the solution of a mixed-integer programming problem to be solved at each sampling instant. By imposing a suitable terminal constraint, convergence to equilibrium and recursive feasibility of the input and state constraints are ensured, provided that the optimization problem is initially feasible. Simulation results are presented for illustration.

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“…8 In a control perspective, the RUL or other PHM information can be used to compensate for the degradation effects of the actuators by smartly balancing the control effort over them. 2,9,10 This is achieved by reconfiguring the control actions so as to preserve the desired dynamic and static performance of the system, as well as its stability, while protracting the life of the actuators until maintenance operations are carried out.…”

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confidence: 99%

Predictive maintenance of actuators in linear systems: A receding horizon set‐theoretic approach

Tedesco

Akram

Forouzanfar

et al. 2022

Intl J Robust & Nonlinear

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This work is focused on a model predictive control (MPC) based approach for the regulation of systems whose actuators are subject to loss of effectiveness. Such a degradation process, whose evolution is assumed to be measurable and predictable, can lead to actuators breakdown whenever certain thresholds are exceeded. Within this context, if the control problem is defined with ad hoc operational constraints and/or running costs, the intrinsic MPC capability of mitigating faulty events arises. To this end, the proposed control scheme consists of two modules: the former is in charge of performing a prognostic activity on actuators' health, while the second one reconfigures the control law on the basis of actuators' degradation information. The core of the proposed method relies on modeling healthy and faulty plant configurations via a switching systems paradigm that is instrumental to offline determine sequences of precomputed inner approximations of one-step ahead controllable sets. Such regions are online selected by a switching logic in order to determine a control signal properly weighted on the basis of the measured actuators degradation level.

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Minimization of Degradation Through Prognosis Based Control for a Damaged Aircraft Actuator

Cited by 5 publications

References 0 publications

Dynamic Bayesian Networks for Prognosis

Dynamic Bayesian Networks for Prognosis

Predictive Control for Systems with Loss of Actuator Effectiveness Resulting from Degradation Effects

Predictive maintenance of actuators in linear systems: A receding horizon set‐theoretic approach

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