Research On Model-based Optimal Control For Aero-engines

Traditionally, the aero-engine control system is designed to operate independently of aircraftflight control system. This design philosophy often produces a system where performance is compro-mised greatly to ensure operability and simplicity. Now, by the aid of advances in control, estimationand system modeling techniques, integrated flight/propulsion control (IFPC) has become an inevitabletrend, which can improve overall aircraft performance. As the heart of IFPC, model-based optimalcontrol enables an aero-engine to achieve its full potential using only control algorithms, withoutadding onboard hardware. This paper takes model-based optimal control as the main line, focusing onboth integrated control for fixed-wing aircraft/turbofan engine system and integrated control for hel-icopter/turbo-shaft engine system. In detail, some techniques of onboard engine modeling, perfor-mance seeking control (PSC), high stability engine control (HISTEC) and disturbance rejection con-trol (DRC) are designed respectively, and moreover, some satisfactory results are obtained.In order to improve real-time capability of PSC for fixed-wing aircraft/turbofan engine system, asteady-state hybrid engine model is proposed based on similarity theory in the whole flight envelope,which takes engine’s working under non-nominal condition into account. An estimation module ofturbofan engine performance deterioration, meanwhile, is developed using improved Kalman filter.After combining the hybrid engine model with the estimation module, an onboard simplified enginemodel is built for PSC. With this model and feasible sequential quadratic programming (FSQP) algo-rithm, PSC for fixed-wing aircraft/turbofan engine system is designed and performed, including themaximum thrust mode, the minimum fuel-consumption mode and the minimum turbine temperaturemode. At last, these PSC modes are applied to various flying missions of aircraft, such as climbing,speeding up, cruising and so on. And simulation results demonstrate that flight performance can beimproved obviously with good real-time ability of optimizing process.As to HISTEC for super-maneuvering flight mission, two control schemes are put forward basedon engine surge margin (Sm) estimation model, viz. direct Smcontrol and engine inlet distortion com-pensation control. The modeling process consists of two parts: Smbenchmark value model under rou-tine flight condition and Smloss value model under super-maneuvering flight condition. The former isdeveloped using nonlinear fitting method, and its input is fixed by Smfeature selection algorithmbased on least square support vector regression (LSSVR). The latter is obtained utilizing an attackangel predictive model which is established with online sliding parsimonious LSSVR (OSP-LSSVR).Direct Smcontrol for engine, which is different from conventional control scheme, puts Sminto engineclosed-loop and takes Smas controlled variable directly. In this way, direct Smcontrol can exploit po-tential performance of engine more effectively with the decrease of engine stability margin. As wellas direct Smcontrol, engine inlet distortion compensation control also can achieve a good control level based on the prediction and compensation of engine inlet distortion. To be more important, engineinlet distortion compensation control is able to correct turbine expansion ratio command on engineoriginal closed-loop, which can be realized easier than direct Smcontrol.PSC for helicopter/turbo-shaft engine system is another important research topic. As the same asPSC for fixed-wing aircraft/turbofan engine system, it is necessary to design an onboard simplifiedturbo-shaft engine model with adaptive ability. In this paper, both the simplified engine model and theestimation module of engine performance deterioration are put forward with data-based method. Thesimplified engine model is performed using block partition of flight envelop, and engine mappingmodule in every block is trained by BP Neural Network; the estimation module of engine perfor-mance deterioration is trained offline using muti-input muti-output recursive reduced LSSVR(MRR-LSSVR), and the input of model is determined by feature selection algorithm. Considering thecomprehensive actions of multi-output variables to select support vectors, MRR-LSSVR is proposedto solve multi-output problems, and holds better sparseness and generalization performance thancommon algorithms due to combining reduced technique with iterative strategy. On the basis of thesimplified engine model and the estimation module, PSC for helicopter/turbo-shaft engine system isdesigned with FSQP algorithm, including the minimum fuel-consumption mode, the cascaded mini-mum fuel-consumption mode, the maximum power mode and the minimum turbine temperature mode.At last, the above control modes are carried out respectively based on UH-60A helicopter/T700tur-bo-shaft engine simulation platform, and simulation results show that the proposed optimizationscheme is feasible and valid.Finally, constrained nonlinear model predictive control (NMPC) is applied to the engine controlsystem to improve dynamic disturbance rejection ability of integrated aircraft/turbo-shaft engine sys-tem. Taking the torque of helicopter rotor as the related variable, NMPC utilizes a predictive modeland a rolling optimizer to solve the time-lag effect caused by measuring lag of rotor torque, dynamicresponse of engine, data transmission and so on. In this essay, two kinds of NMPC schemes for tur-bo-shaft engine are put forward, i.e. NMPC based on offline predictive model and cascade+NMPChybrid predictive control based on online predictive model. As for the former, the predictive enginemodel is trained by adopting MRR-LSSVR algorithm, and the rolling optimizer is realized using SQPalgorithm libray. In comparison with cascade controller, NMPC based on offline predictive model candecrease the droop or overshoot of rotor speed remarkably during helicopter’s maneuver flight. Butfor the latter, OSP-LSSVR algorithm is used for the predictive engine model, and gives the model aself-renewal capacity and higher accuracy. Cascade+NMPC hybrid predictive control, therefore, hasbetter dynamic disturbance rejection ability and robustness in a large flight envelop, and enables thehelicopter to exhibit much greater maneuverability than the conventional control method.