Generation of optimal linear parametric models for LFT-based robust stability analysis and control design

Pfifer, Harald; Hecker, Simon

doi:10.1109/cdc.2008.4738780

Cited by 26 publications

(20 citation statements)

References 4 publications

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“…, N, interpolation techniques can be employed to determine parametric approximations (e. g., a©ne or polynomial) of matrices B Q (ρ) and D Q (ρ). The above parametric approximations can be determined along the lines of the techniques developed in [9].…”

Section: Preliminary Synthesis Of An Lpv-filtermentioning

confidence: 99%

Optimization-based tuning of LPV fault detection filters for civil transport aircraft

Ossmann

Varga

2013

Progress in Flight Dynamics, Guidance, Navigation, Control, Fault Detection, and Avionics

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In this paper, a two-step optimal synthesis approach of robust fault detection (FD) ¦lters for the model based diagnosis of sensor faults for an augmented civil aircraft is suggested. In the ¦rst step, a direct analytic synthesis of a linear parameter varying (LPV) FD ¦lter is performed for the open-loop aircraft using an extension of the nullspace based synthesis method to LPV systems. In the second step, a multiobjective optimization problem is solved for the optimal tuning of the LPV detector parameters to ensure satisfactory FD performance for the augmented nonlinear closed-loop aircraft. Worst-case global search has been employed to assess the robustness of the fault detection system in the presence of aerodynamics uncertainties and estimation errors in the aircraft parameters. An application of the proposed method is presented for the detection of failures in the angle-of-attack sensor.

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Section: Preliminary Synthesis Of An Lpv-filtermentioning

confidence: 99%

Optimization-based tuning of LPV fault detection filters for civil transport aircraft

Ossmann

Varga

2013

Progress in Flight Dynamics, Guidance, Navigation, Control, Fault Detection, and Avionics

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“…to scale B a and F a in (30) and (32). This extra freedom will be used to introduce a design penalty, trading-off performance against uncertainty in the plant ξ(t, y, u) and corruption to the measurements fromφ(t).…”

Section: B Observer Designmentioning

confidence: 99%

“…The model is derived from the ADDSAFE benchmark model using an LPV generation procedure described in [32]. The method involves the generation of a set of linearized models at equilibrium/trim points which are enclosed within the range of the LPV parameters.…”

Section: A Addsafe Benchmark Designmentioning

confidence: 99%

“…The resulting polynomials can be used to create a single LPV model which approximates the whole set of LTI equilibrium models [39], [23]. Further details of the LPV model and its generation are available in [39], [23], [32].…”

Section: A Addsafe Benchmark Designmentioning

confidence: 99%

“…For example, in aircraft systems, a nonlinear FDI observer design often requires information about the aerodynamic coefficients, which is hard to obtain in the required form. However an LPV representation can easily be built from available families of linear systems over the entire flight envelope (see [8], [29] for examples of LPV system development and [32] for a recent generalized LPV model generation scheme). Through polynomial fitting, the LPV models may explicitly contain information about the aerodynamic coefficient variations over the flight envelope.…”

Section: Introductionmentioning

confidence: 99%

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Robust fault reconstruction for linear parameter varying systems using sliding mode observers

Alwi

Edwards

2013

Intl J Robust & Nonlinear

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This paper presents a robust FDI scheme using a sliding mode observer based on an LPV system, with fault reconstruction capability. Both actuator and sensor fault reconstruction schemes are considered which possess robustness against a certain class of uncertainty and corrupted measurements. For actuator fault reconstruction, the input distribution matrix (associated with the actuators being monitored) is factorized into fixed and varying components. LMIs are used to design the key observer parameters in order to minimize the effect of uncertainty and measurement corruption on the fault reconstruction signal. The faults are reconstructed using the output error injection signal associated with the nonlinear term of the sliding mode observer. For sensor fault reconstruction, the idea is to re-formulate the problem into an actuator fault reconstruction scenario so that the same design procedure can be applied. This is achieved by augmenting the original system with the filtered sensors being monitored. Simulations using a full nonlinear model of a large transport aircraft are presented, and show good fault reconstruction performance.

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Reduced-Order Modeling

Valášek

Šika

Vampola

et al. 2014

Modeling and Control for a Blended Wing Body Aircraft

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Generation of optimal linear parametric models for LFT-based robust stability analysis and control design

Cited by 26 publications

References 4 publications

Optimization-based tuning of LPV fault detection filters for civil transport aircraft

Optimization-based tuning of LPV fault detection filters for civil transport aircraft

Robust fault reconstruction for linear parameter varying systems using sliding mode observers

Reduced-Order Modeling

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