Application of h∞ robust control to the RM12 jet engine

Harefors, M.

doi:10.1016/s0967-0661(97)84358-4

Cited by 32 publications

(13 citation statements)

References 5 publications

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“…Notation e the system output deviation e k + 1 (t) the output deviation k the number of iteration k i , k p the controller gain N iterative learning gain N H the speed of the compressor N L the speed of the fan P 3 the compressor outlet total pressure P 6 the connotation export total pressure T 5 the low pressure turbine outlet total temperature T 22 the fan outlet total temperature u the control vector of the system u k (t) last value of control variable u k + 1 (t) the current control variable W f the fuel supply of main chamber x the state vector of the system y the output vector of the system y d system desired signal y d (t) the desired trajectory y k (t) system output variable y n the output signal of system G i , G p iterative learning gain…”

Section: Appendixmentioning

confidence: 99%

“…1 To solve this problem, the common approach is splitting the flight envelop into different operating points; thus, the approximated linear models at each operating point are employed in aero-engine controller, such as finite impulse response model 2 or small perturbation state space model. [2][3][4] Consequently, traditional linear control methods, such as H ' optimal control, [5][6][7] linear quadratic regulator (LQR)/loop transfer recovery (LTR), 8 and proportional integral derivative (PID) control, are then utilized for each linear model. Finally, gain-scheduling method is employed to control aircraft engines over the full flight envelope.…”

Section: Introductionmentioning

confidence: 99%

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Research on self-learning control method for aircraft engine above idle state

Shen

Huang

et al. 2016

Advances in Mechanical Engineering

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The iterative learning control for aircraft engine above idle state is studied. An approach combining the proportional integral iterative learning with the traditional proportional integral derivative controller is proposed and then this hybrid iterative learning controller is constructed to control the speed of three typical engine models. In the simulation study, the proposed method is applied to the nonlinear component level engine model, state variable engine model, and linear parameter-varying engine model; the results show that the performance of the proposed hybrid iterative learning controller is much better than the traditional proportional integral derivative controller.

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Section: Appendixmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on self-learning control method for aircraft engine above idle state

Shen

Huang

et al. 2016

Advances in Mechanical Engineering

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“…The H ∞ control theory attracts many researchers in robust control design, especially in the noise elimination by setting a disturbance rejection level from noises to outputs. The research works in this field are numerous [10][11][12][13]. The proportion integral derivative (PID) control method is practically applied to turbofan engines; while the three parameters, proportional, integral and differential, need to be adjusted at different operating conditions [14,15].…”

Section: Introductionmentioning

confidence: 99%

A New Robust Tracking Control Design for Turbofan Engines: H∞/Leitmann Approach

et al. 2017

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In this paper, a H ∞ /Leitmann approach to the robust tracking control design is presented for an uncertain dynamic system. This new method is developed in the following two steps. Firstly, a tracking dynamic system with simultaneous consideration of parameter uncertainty and noise is modeled based on a linear system and a reference model. Accordingly, a "nominal system" from the tracking system is defined and controlled by a H ∞ control to obtain the asymptotical stability and noise resistance. Secondly, by making use of a Lyapunov function and the norm boundedness, a new robust control with the "Leitmann approach" is designed to cope with the uncertainty. The two controls collaborate with each other to achieve "uniform tracking boundedness" and "uniform ultimate tracking boundedness". The new approach is then applied to an aircraft turbofan control design, and the numerical simulation results show the prescribed performances of the closed-loop system and the advantage of the developed approach.

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“…Monitoring of gas turbines, and especially aircraft engines, is a widely studied topic in the gas turbine diagnosis literature (Volponi, 2014;Luppold et al, 1989;Doel, 2003). For control applications, model based feedback control strategies are studied in Watts et al (1992); Härefors (1997). In these papers, the control strategies are based on linear models which are derived from a thermodynamic engine model.…”

Section: Introductionmentioning

confidence: 99%

Model Based Diagnosis and Supervision of Industrial Gas Turbines

Larsson¹

2014

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Supervision of performance in gas turbine applications is important in order to achieve: (i) reliable operations, (ii) low heat stress in components, (iii) low fuel consumption, and (iv) efficient overhaul and maintenance. To obtain good diagnosis performance it is important to have tests which are based on models with high accuracy. A main contribution of the thesis is a systematic design procedure to construct a fault detection and isolation (FDI) system which is based on complex nonlinear models. These models are preliminary used for simulation and performance evaluations. Thus, is it possible to use these models also in the FDI-system and which model parts are necessary to consider in the test design? To fulfill the requirement of an automated design procedure, a thermodynamic gas turbine package GTLib is developed. Using the GTLib framework, a gas turbine diagnosis model is constructed where component deterioration is introduced. In the design of the test quantities, equations from the developed diagnosis models are carefully selected. These equations are then used to implement a Constant Gain Extended Kalman filter (CGEKF) based test quantity. The number of equations and variables which the test quantity is based on is significantly reduced compared to the original reference model. The test quantity is used in the FDI-system to supervise the performance and the turbine inlet temperature which is used in the controller. An evaluation is performed using experimental data from a gas turbine site. The case study shows that the designed FDI-system can be used when the decision about a compressor wash is taken. When the FDI-system is augmented with more test quantities it is possible to diagnose sensor and actuator faults at the same time the performance is supervised. Slow varying sensor and actuator bias faults are difficult diagnose since they appear in a similar manner as the performance deterioration, but the FDI-system has the ability to detect these faults. Finally, the proposed model based design procedure can be considered when an FDI-system of an industrial gas turbine is constructed.iii Populärvetenskaplig sammanfattning Diagnostik och prestandaövervakning förekommer inom många industriella applikationer. Detta område är viktigt att beakta för att: (i) upprätthålla hög tillförlitlighet, (ii) undvika onödig belastning på komponenter, (iii) minimera energiförbrukningen, och (iv) effektivt kunna planera underhåll. Eftersom prestandan i en applikation oftast inte är direkt mätbar behövs metoder för att kunna skatta dessa prestandaparametrar utifrån kända mätsignaler. Detta kan vara svårt eftersom: (i) sambandet mellan mätsig-naler och prestandaparametrar kan vara komplicerat, (ii) mätsignaler innehåller brus, och (iii) mätsignaler kan vara opålitliga och visa ett felaktigt värde. Dessa aspekter bör beaktas när ett diagnos-och övervakningssystem utvecklas. Eftersom många system är komplexa kan det vara nödvändigt att ha effektiva och automatiserade metoder för att skatta prestanda och bestämma diag...

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Application of h∞ robust control to the RM12 jet engine

Cited by 32 publications

References 5 publications

Research on self-learning control method for aircraft engine above idle state

Research on self-learning control method for aircraft engine above idle state

A New Robust Tracking Control Design for Turbofan Engines: H∞/Leitmann Approach

Model Based Diagnosis and Supervision of Industrial Gas Turbines

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