Decentralized PI control of a rolls-royce jet engine

Tavakoli, Saeed; Griffin, Ian; Fleming, Peter J.

doi:10.1109/cca.2005.1507153

Cited by 6 publications

(2 citation statements)

References 19 publications

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“…The first applications were reported by Fleming and his research group in the University of Sheffield in 1996 [60] in which a multi-objective genetic algorithm was used to extract a Pareto front set of solution for multivariable control of a gas turbine engine. Later on, they published two other research studies on tuning of decentralised PI/PID controllers for gas turbine engine control purposes [61][62] where they showed the ability and effectiveness of the global optimization approaches in industrial control applications.…”

Section: Meta-heuristic Global Optimization Algorithms In Control Gain Tuning Of Gas Turbine Enginesmentioning

confidence: 99%

Meta-heuristic global optimization algorithms for aircraft engines modelling and controller design; A review, research challenges, and exploring the future

Jafari

Nikolaidis

2019

Progress in Aerospace Sciences

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Utilizing meta-heuristic global optimization algorithms in gas turbine aero-engines modelling and control problems is proposed over the past two decades as a methodological approach. The purpose of the review is to establish evident shortcomings of these approaches and to identify the remaining research challenges. These challenges need to be addressed to enable the novel, cost-effective techniques to be adopted by aero-engine designers. First, the benefits of global optimization algorithms are stated in terms of philosophy and the nature of different types of these methods. Then, a historical coverage is given for the applications of different optimization techniques applied in different aspects of gas turbine modelling, controller design, and tuning fields. The main challenges for the application of meta-heuristic global optimization algorithms in new advanced engine designs are presented. To deal with these challenges, two efficient optimization algorithms, Competent Genetic Algorithm in single objective feature and aggregative gradient-based algorithm in multi-objective feature are proposed and applied in a turbojet engine controller gaintuning problem as a case study. A comparison with the publicly available results show that optimization time and convergence indices will be enhanced noticeably. Based on this comparison and analysis, the potential solutions for the remaining research challenges for application to aerospace engineering problems in the future include the implementation of enhanced and modified optimization algorithms and hybrid optimization algorithms in order to achieve optimal results for the advanced engine modelling and controller design procedure with affordable computational effort.

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Section: Meta-heuristic Global Optimization Algorithms In Control Gain Tuning Of Gas Turbine Enginesmentioning

confidence: 99%

Meta-heuristic global optimization algorithms for aircraft engines modelling and controller design; A review, research challenges, and exploring the future

Jafari

Nikolaidis

2019

Progress in Aerospace Sciences

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“…Fleming and his research group at the University of Sheffield started working on this approach in 1996 [11], in which they used a multi-objective Genetic Algorithm to obtain a Pareto front set of solutions for multivariable control of an engine. More results were reported on decentralized PI/PID controllers for engine control in [12,13], where applying the global optimization methods in industrial control applications was evaluated.…”

Section: Introductionmentioning

confidence: 99%

Advanced Control Algorithm for FADEC Systems in the Next Generation of Turbofan Engines to Minimize Emission Levels

et al. 2022

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New propulsion systems in aircrafts must meet strict regulations and emission limitations. The Flightpath 2050 goals set by the Advisory Council for Aviation Research and Innovation in Europe (ACARE) include reductions of 75%, 90%, and 65% in CO2, NOx, and noise, respectively. These goals are not fully satisfied by marginal improvements in gas turbine technology or aircraft design. A novel control design procedure for the next generation of turbofan engines is proposed in this paper to improve Full Authority Digital Engine Control (FADEC) systems and reduce the emission levels to meet the Flightpath 2050 regulations. Hence, an Adaptive Network–based Fuzzy Inference System (ANFIS), nonlinear autoregressive network with exogenous inputs (NARX) techniques, and the block-structure Hammerstein–Wiener approach are used to develop a model for a turbofan engine. The Min–Max control structure is chosen as the most widely used practical control algorithm for gas turbine aero engines. The objective function is considered to minimize the emission level for the engine in a pre-defined maneuver while keeping the engine performance in different aspects. The Genetic Algorithm (GA) is applied to find the optimized control structure. The results confirm the effectiveness of the proposed approach in emission reduction for the next generation of turbofan engines.

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