Generic integrated PHM/controller system

Brotherton, T.; Luppold, R.; Padykula, P.; Wade, Robert

doi:10.1109/aero.2005.1559645

Cited by 5 publications

(4 citation statements)

References 4 publications

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“…A proposed FPGA based design to allow for a system capable of integrating a propulsion health system with a control system for VTOLs is presented [35]. This design has recognized strength of both FPGA architecture and integration with Simulink for programming.…”

Section: Implementations Using Fpgasmentioning

confidence: 99%

“…Many of the designs implementing FPGAs still utilized a separate DSP/microcontroller processor for the majority of the processing, such as the Microbot and the GTSpy, once again neglecting the benefits of parallel processing. Two of the full FPGA designs require the programmer to implement the majority of the programming in a PowerPC [8,35], rather then allowing the programmer has the option of implementing some or all of the processing utilizing a real time operating system, or taking full advantage of parallel processing capabilities. Although the presented work in [39] implemented a full FPGA processing system for control of a Satellite rather than an small scale or micro vehicle, the full parallel processing capabilities of the Xilinx chip was utilized and analysis indicated good timing and parallel communication could be maintained.…”

Section: Comparison Of Proposed Design With Existing Onesmentioning

confidence: 99%

“…Because these advances are fairly recent, there are only a few publications that are following the same philosophy of utilization of FPGAs [8,[35][36][37][38][39].…”

Section: Implementations Using Fpgasmentioning

confidence: 99%

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FPGA based flexible autopilot platform for unmanned systems

Alvis

Murthy

Valavanis

et al. 2007

2007 Mediterranean Conference on Control &Amp; Automation

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Diversity in unmanned vehicle designs and control as well as diversity in existing autopilots lead to major compatibility issues among different platforms. The small number of different autopilot designs currently available in the market and their limitations, justifies the rationale for proposing design of a new modular, flexible, easy to integrate autopilot, suitable for multiple platforms. A discussion and comparison of the state of the art in autopilot research is presented, followed by the introduction of an autopilot design that will provide a novel and wellneeded processing platform for unmanned systems. The design is fully integrated with MATLAB/Simulink allowing for both simplified high level programming and hardwarein-the-loop capabilities, and provides a flexible FPGAbased platform that maximizes parallel processing and allows for analog signal conditioning that can be modified through digital communication from FPGA processor.

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Section: Implementations Using Fpgasmentioning

confidence: 99%

Section: Comparison Of Proposed Design With Existing Onesmentioning

confidence: 99%

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FPGA based flexible autopilot platform for unmanned systems

Alvis

Murthy

Valavanis

et al. 2007

2007 Mediterranean Conference on Control &Amp; Automation

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“…That h/w includes a Xilinx Virtex-IV FPGA processor and two embedded PowerPC's each rated at 450 MHz. In addition the IAC-1134 hardware architecture will include PCI buses for passing data between memory and the processing elements [12].…”

Section: Figure 14 Hardware System Summarymentioning

confidence: 99%

Adaptive On-Wing Gas Turbine Engine Performance Estimation

Luppold

Brotherton

Volponi

2007

2007 IEEE Aerospace Conference

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A key technological concept for producing reliable engine diagnostics and prognostics exploits the benefits of fusing sensor data, information, and/or processing algorithms. In this paper, we consider a real-time physics based model of a commercial turbofan engine called STORM: Self Tuning On-board, Real-time engine Model.The STORM system provides a means for tracking engine module performance changes in real-time. However, modeling error can have a corruptive effect on STORM's estimation of performance changes. Fusing an empirical neural network based model with STORM forms a unique hybrid model of the engine called enhanced STORM (eSTORM). This approach can eliminate the STORM engine diagnostic errors.A practical consideration for implementing the hybrid engine model, involves the application of some form of sequential model building to construct and specify the empirical elements. A methodology for constructing the empirical model (EM) in a sequential manner without the requirement for storing all of the original data has been developed. This paper describes the development of the adaptive hybrid model scheme for a commercial turbofan engine. This adaptive hybrid-modeling scheme has been implemented in real-time on an Intelligent Automation Corporation (IAC) computational platform. Model performance achieved with the automated update algorithm using real on-wing commercial aircraft engine data will be presented.

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