Stability of Fiber Optic Networked Decentralized Distributed Engine Control Under Time Delays

Belapurkar, Rohit K.; Yedavalli, Rama K.; Moslehi, Behzad

doi:10.2514/6.2009-4885

Cited by 8 publications

(6 citation statements)

References 9 publications

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“…Hence, it is very important to study control of safety-critical distributed systems under these communication constraints. Decentralized distributed full authority digital engine control was proposed and studied for stability under time delays and packet dropouts in [11,12]. As each of the smart nodes will be operating at adverse environmental conditions including harsh vibrations and high temperatures, it is necessary to develop reliable electronics capable of operating at these harsh conditions with low maintenance requirement.…”

Section: Distributed Aircraft Engine Controlmentioning

confidence: 99%

“…By improving PDM, which can be viewed as a measure of stability robustness for a system with packet dropouts, the stability of networked control systems with packet dropouts can be improved. A new framework, labeled decentralized distributed full authority digital engine control (D 2 FADEC) was proposed in [11,12] and was studied for stability under time delays and packet dropouts. It was shown that the PDM is dependent on a closed-loop system matrix structure and that a controller design based on a decentralized framework further improves the PDM.…”

Section: Control Under Communication Constraintsmentioning

confidence: 99%

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Application of wireless sensor networks to aircraft control and health management systems

Yedavalli

Belapurkar

2011

J. Control Theory Appl.

113

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Use of fly-by-wire technology for aircraft flight controls have resulted in an improved performance and reliability along with achieving reduction in control system weight. Implementation of full authority digital engine control has also resulted in more intelligent, reliable, light-weight aircraft engine control systems. Greater reduction in weight can be achieved by replacing the wire harness with a wireless communication network. The first step towards fly-by-wireless control systems is likely to be the introduction of wireless sensor networks (WSNs). WSNs are already finding a variety of applications for both safety-critical and nonsafety critical distributed systems. Some of the many potential benefits of using WSN for aircraft systems include weight reduction, ease of maintenance and an increased monitoring capability. This paper discusses the application of WSN for several aircraft systems such as distributed aircraft engine control, aircraft flight control, aircraft engine and structural health monitoring systems. A brief description of each system is presented along with a discussion on the technological challenges. Future research directions for application of WSN in aircraft systems are also discussed.

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Section: Distributed Aircraft Engine Controlmentioning

confidence: 99%

Section: Control Under Communication Constraintsmentioning

confidence: 99%

Application of wireless sensor networks to aircraft control and health management systems

Yedavalli

Belapurkar

2011

J. Control Theory Appl.

113

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“…These mechanics have been studied and the results of these investigations offer initial impressions as to the importance of modeling the network with the controller. [3][4][5][6][7] A final, more encompassing obstacle that must be addressed is related to the design process as a whole. Control design, testing, and implementation are often the final steps in the engine development process and are therefore subject to strict time and budget constraints.…”

Section: Introductionmentioning

confidence: 99%

A Modular Framework for Modeling Hardware Elements in Distributed Engine Control Systems

Zinnecker¹,

Culley²,

Aretskin-Hariton³

2014

50th AIAA/ASME/SAE/ASEE Joint Propulsion Conference

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Progress toward the implementation of distributed engine control in an aerospace application may be accelerated through the development of a hardware-in-the-loop (HIL) system for testing new control architectures and hardware outside of a physical test cell environment. One component required in an HIL simulation system is a high-fidelity model of the control platform: sensors, actuators, and the control law. The control system developed for the Commercial Modular Aero-Propulsion System Simulation 40k (C-MAPSS40k) provides a verifiable baseline for development of a model for simulating a distributed control architecture. This distributed controller model will contain enhanced hardware models, capturing the dynamics of the transducer and the effects of data processing, and a model of the controller network. A multilevel framework is presented that establishes three sets of interfaces in the control platform: communication with the engine (through sensors and actuators), communication between hardware and controller (over a network), and the physical connections within individual pieces of hardware. This introduces modularity at each level of the model, encouraging collaboration in the development and testing of various control schemes or hardware designs. At the hardware level, this modularity is leveraged through the creation of a Simulink R library containing blocks for constructing smart transducer models complying with the IEEE 1451 specification. These hardware models were incorporated in a distributed version of the baseline C-MAPSS40k controller and simulations were run to compare the performance of the two models. The overall tracking ability differed only due to quantization effects in the feedback measurements in the distributed controller. Additionally, it was also found that the added complexity of the smart transducer models did not prevent real-time operation of the distributed controller model, a requirement of an HIL system. NomenclatureVariables N shaft speed P pressure P 50 pressure at station 50 (exit of low-pressure turbine) T temperature T s update rate/sampling time W flow rate (·) sens sensed variable * Control Systems Engineer, alicia.m.zinnecker@nasa.gov, AIAA Member. † Research Engineer, dennis.e.culley@nasa.gov, AIAA Senior Member.

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“…Due to the advantages of sliding-mode control, there has been some research results achieved about its application in time-delay system and time-delay network control system [6][7][8]. In view of the time-delay in the system, the conventional design idea of the existing research results is to make full use of the invariance of the sliding-mode control, to design a sliding surface satisfying the progressive stability condition by LMI [9], free weight matrix [10] and other methods, and to improve both the approach law and the controller design [11,12] so that ensuring the stability and robustness of the system.…”

Section: Introductionmentioning

confidence: 99%

Multi-packet transmission aero-engine DCS neural network sliding mode control based on multi-kernel LS-SVM packet dropout online compensation

Wang

Ren

2020

PLoS ONE

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In view of the strong nonlinear characteristics of the multi-packet transmission Aero-engine DCS with induced delay and random packet dropout, a neural network PID approach law sliding-mode controller using sliding window strategy and multi-kernel LS-SVM packet dropout online compensation is proposed. Firstly, the time-delay term in the system model is transformed equivalently, to establish the discrete system model of multi-packet transmission without time-delay; furthermore, the construction of multi-kernel function is transformed into kernel function coefficient optimization, and the optimization problem can be solved by the chaos adaptive artificial fish swarm algorithm, then the online predictive compensation will be made for data packet dropout of multi-packet transmission through the sliding window multi-kernel LS-SVM. After that, a sliding-mode controller design method of proportional integral differential approach law based on neural network is proposed. And online adjustment of PID approach law parameters can be achieved by nonlinear mapping of neural network. Finally, Truetime is used to simulate the method. The results shows that when the packet dropout rate is 30% and 60%, the average error of packet dropout prediction of multikernel LS-SVM reduces 29.21% and 44.66% compared with that of combined kernel LS-SVM, and the chattering amplitude of the proposed neural network PID approach law sliding-mode controller is decreased compared with other five approach law methods respectively. This controller can ensure a fast response speed, which shows that this method can achieve a better tracking control of the aeroengine network control system.

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Stability of Fiber Optic Networked Decentralized Distributed Engine Control Under Time Delays

Cited by 8 publications

References 9 publications

Application of wireless sensor networks to aircraft control and health management systems

Application of wireless sensor networks to aircraft control and health management systems

A Modular Framework for Modeling Hardware Elements in Distributed Engine Control Systems

Multi-packet transmission aero-engine DCS neural network sliding mode control based on multi-kernel LS-SVM packet dropout online compensation

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