Performance Seeking Control Research Articles

Sensitivity analysis of aero-engine performance optimization control system and its hardware test verification

Abstract The performance seeking control (PSC) method is aimed at improving the performance of aircraft engine without changing hardware devices. However, the complexity of the system makes it difficult to achieve its application in engineering. Thus, the key issues in PSC of turbofan engine are studied based on global sensitivity analysis, and the improvements are made to the optimization system according to the analysis. Firstly, the principle of PSC is analyzed based on traditional frameworks. And an improved linear programming method based on task scheduling is proposed to reduce the time consumption within a single closed-loop simulation. The impact of the estimation model accuracy on optimization results is studied adopting a sensitivity analysis method based on Monte Carlo sampling to address the optimization lose efficacy. Finally, the hardware-in-the-loop simulation is carried out utilizing model-based design method in a certain type of turbofan engine electronic controller.

Research on performance seeking control of turbofan engine in minimum hot spot temperature mode

Abstract The uneven temperature distribution at the combustion chamber outlet seriously affects the working life of the engine. In order to reduce the heat spot temperature at the combustion chamber outlet, a performance optimization control method of the engine minimum heat spot temperature pattern is proposed. Firstly, based on CFD method, the temperature distribution characteristics of combustion chamber outlet under different working conditions were obtained, and a component-level model of turbofan engine was established to characterize the heat spot temperature at combustion chamber outlet. Secondly, the high precision and high real-time engine on-board model is established by deep neural network. Compared with the component-level model, the average relative error of each performance parameter is less than 0.3 %, and the real-time performance is improved by 12 times. Finally, based on the feasible sequential quadratic programming algorithm, the performance optimization control of the minimum hot spot temperature model in the typical flight envelope is simulated and verified. The simulation results show that under the condition of ensuring the safe and stable operation of the engine and constant thrust, the heat spot temperature at the combustion chamber outlet decreases by 21 K maximum. Compared with the conventional minimum turbine front temperature optimization mode, the minimum heat spot temperature mode significantly reduces the heat spot temperature at the combustion chamber outlet.

A new schedule method for compact propulsion system model

Abstract The PSC (Performance Seeking Control) based on CPSM (Compact Propulsion System Model) has been verified by NASA. However, the CPSM has poor accuracy at off-design points. Therefore, a new basepoint schedule method is proposed to improve the CPSM accuracy at off-design points. At the off-design point, the thermodynamic parameters which is a function of temperature is an importance factor that influence the accuracy of model based on parameter corrections. Therefore, the temperature of fan inlet is taken into account during scheduling the basepoint vector. The simulations have shown that the accuracy of CPSM is at its best when the engine operates at a point where the temperature of the fan inlet is equal to the one of the basepoint. With the increase or decrease of the temperature of the fan inlet, the modeling errors of CPSM will increase. The simulations also demonstrate that the relative errors of the improved CPSM decrease significantly compared to those of the conventional CPSM at the off-design point.

Performance seeking control method for minimum pollutant emission mode for turbofan engine

The research shows that the impact of aero-engine pollutant emission on the environment cannot be ignored. In addition to fuel type and combustor structure, aero-engine exhaust pollutants are also related to engine operating conditions and control systems. In this context, taking CO and NOx as the main exhaust pollutants of the engine as the research object, a performance optimization control method of the minimum pollutant emission mode of the engine is proposed. Firstly, the CO and NOx emission characteristics of the engine under different operating conditions at sea level are calculated by CFD numerical simulation method, and the engine component-level model applicable to all flight envelope and all conditions is established according to the characteristic data. Secondly, in order to improve computational efficiency, an airborne model with high accuracy and real-time performance is established through the deep neural network. Compared to the component-level model, the average relative error of each performance parameter is less than 0.5 %, and the real-time performance is improved by about 12 times. Finally, the optimization principle of the minimum pollutant emission mode is discussed. The feasible sequential quadratic programming (FSQP) algorithm is selected, and the fuel quantity and throat area of the tail nozzle are taken as control variables. Numerical simulations are performed in the typical mission envelope. Simulation results show that the CO emission index decreases by 5 % and the NOx emission index decreases by more than 6.5 % through performance optimization control of the minimum pollutant emission mode under the premise of ensuring constant engine thrust and meeting other constraints.

Installed performance seeking control based on supersonic variable inlet/engine coupling model

Abstract With the advancement of the supersonic aero propulsion system, optimizing the combined performance of inlet/engine integration has become increasingly crucial. To solve the coupling inlet/engine problem, a quasi-one-dimensional inlet modeling and drag calculation method are proposed, integrated performance seeking control (PSC) based on the neighborhood-based speciation differential evolution-grey wolf optimizer (NSDE-GWO) is presented and quantitatively analyses the influence of variable geometry inlet regulation on performance. The results reveal that the optimization effect of the ramp angle adjustment is generally better than that of the bleed adjustment, and the NSDE-GWO hybrid algorithm achieves remarkable optimization solutions in all three different modes. The PSC with variable geometry inlet adjustment provides more additional potential for optimization compared with fixed geometry inlet, and the performance can be maximized by adjusting both the bleed adjustment and the ramp angle. This study maximizes the exploitation of potential and has theoretical guidance and practical engineering significance.

Performance-Seeking Control of Aero-Propulsion System Based on Intelligent Optimization and Active Disturbance Rejection Fusion Controller

To investigate the performance potential of the aero-propulsion system and the problem of control mode conversion, this paper takes the inlet/engine integrated component-level model as the research object, and a performance-seeking control (PSC) scheme based on the neighborhood-based speciation differential evolution–grey wolf optimization (NSDE-GWO) algorithm is designed and combined with an active disturbance rejection control (ADRC) to establish a multivariable fusion closed-loop control system. The analysis reveals that the NSDE-GWO hybrid algorithm, which takes advantages of the two algorithms, significantly improves the computing efficiency and optimization accuracy, achieving better optimization solutions in three different modes. The intelligent fusion controller is able to achieve a smooth transition of performance modes to ensure that the engine is provided with a stable thrust during operation under supersonic conditions, and the potential for performance optimization is maintained at a reasonable level. Maximum installed thrust mode capable of achieving no thrust loss and a maximum fluctuation rate within 2000 N/s, with the largest variation in thrust during the conversion being less than 0.9% under the minimum turbine temperature mode and the minimum specific fuel consumption mode. This study presents a theoretical foundation and engineering applications for the design of supersonic propulsion system controllers.

Research on aero-engine performance seeking control based on the NN-PSM on-board model

In order to improve the real-time performance of performance seeking control (PSC), a neural network-propulsion system matrix(NN-PSM) on-board model is proposed and applied to PSC. First, based on NN-PSM, a large-envelope, multi-variable on-board adaptive model is established. The PSM is extracted through a small deviation linearization method. The neural network is used to map the relationship between the flight conditions, engine control parameters, and the engine performance parameters, and designed a Kalman filter to estimate engine health parameters in real-time. Then, four PSC mode of maximum thrust, minimum fuel consumption, minimum high-pressure turbine inlet temperature, and minimum infrared radiation intensity are designed using LP optimization algorithm as optimize algorithm. Finally, the simulation results show that NN-PSM has much higher precision than Compact Propulsion System Model (CPSM). The PSC simulations show that compared with the PSC based on the conventional CPSM, the proposed method has much better real-time performance and get better engine performance, such as more thrust, less specific fuel consumption, and less turbine inlet temperature.

Optimal Output Tracking of Aircraft Engine Systems: A Data-Driven Adaptive Performance Seeking Control

In this technical note, a data-driven adaptive performance seeking control (DAPSC) technique is first developed for the optimal output tracking of an aircraft engine with nonlinear and non-analytical dynamics. Firstly, an optimal output tracking description for the aircraft engine is given mathematically, and then the optimal output tracking of aircraft engines is converted by a dynamic programming method, and a Hamilton-Jacoby-Bellman equation is further derived. Secondly, by introducing a function approximation mechanism, a data-driven adaptive optimization algorithm for the Hamilton-Jacoby-Bellman equation is designed. The developed algorithm can update the optimal control law online, and convergence results of the optimal control law are presented. Finally, by comparing DAPSC scheme and proportional-integral-derivative (PID) method, simulation results of an aircraft engine component level model are shown to reveal the potential of the designed technique.

HNN-based generalized predictive control for turbofan engine direct performance optimization

The aero-engine performance optimization control is to find the appropriate control variables to maximize or minimize the comprehensive index of one or more performance parameters while ensuring its safe operation. Traditional performance seeking control is mainly based on indirect control, with complex structure and large error. This paper develops an HNN (Hopfield Neural Network)-based generalized predictive control strategy for turbofan engine direct performance optimization. With a Hopfield network as the optimization algorithm and a CARIMA model based on generalized prediction criterion as the optimization model, an optimal controller is designed. Together with the identification module and the self-adaptive module, an integrated performance optimization control system is formed. Namely, the adaptive model calculates the unmeasurable performance parameters of the engine for the CARIMA model identification. Then HNN performs the iterative operation to obtain the optimal control variables for a given objective. The main contribution of this paper is to propose a direct performance control structure to avoid the nonlinear conversion of control reference; meanwhile, the linear CARIMA model is combined with HNN algorithm to build a neural network for solving constrained nonlinear programming problems, and mutation operation is introduced to improve its global search capability, which is evident in the minimum turbine temperature optimization mode. The method is evaluated on a turbofan model from the aspects of model verification, performance optimization control simulation at multiple operating points and under degeneration conditions. The results confirm the satisfactory performance of the proposed strategy at the selected points and conditions. And compared with the traditional algorithms, the computer calculation time is greatly shortened.

Performance seeking control of minimum infrared characteristic on double bypass variable cycle engine

In order to study the performance seeking control of double bypass variable cycle engine, a Feasible Sequential Quadratic Programming (FSQP) algorithm with global and local superlinear convergence characteristics is adopted. The engine model established a backward infrared radiation intensity prediction method for exhaust system, which could calculate the infrared radiation intensity of the high temperature components and nozzle stream in the system. The infrared radiation absorption effect of the nozzle stream on the high temperature components is considered in the method. According to the above model, a minimum infrared characteristic performance seeking control based on infrared prediction model is proposed. The optimal control simulations of the maximum thrust, minimum specific fuel consumption and minimum infrared characteristic mode on the double bypass variable cycle engine are carried out. Finally, the simulation results show that the algorithm could obtain the optimal operating conditions of double bypass variable cycle engine in different working conditions, which achieves the steady-state performance seeking control of the engine.

Research on performance seeking control based on Beetle Antennae Search algorithm

A novel performance seeking control) method based on Beetle Antennae Search algorithm is proposed to improve the real-time performance of performance seeking control. The Beetle Antennae Search imitates the function of antennae of beetle. The Beetle Antennae Search has better real-time performance because of the objective function only calculated twice in Beetle Antennae Search at each iteration. Moreover, the Beetle Antennae Search has global search ability. The performance seeking control simulations based on Beetle Antennae Search, Genetic Algorithm and particle swarm optimization are carried out. The simulations show that the Beetle Antennae Search has much better real-time performance than the conventional probability-based algorithms Genetic Algorithm and particle swarm optimization. The simulations also show that these three probability-based algorithms can get better engine performance, such as more thrust, less specific fuel consumption and less turbine inlet temperature.

Research on hybrid optimization and deep learning modeling method in the performance seeking control

A novel performance seeking control method based on hybrid optimization algorithm and deep learning modeling method is proposed to get a better engine performance. The deep learning modeling method, deep neural network, which has strong representation capability and can deal with big training data, is adopted to establish an on-board engine model. A hybrid optimization algorithm—genetic algorithm particle swarm optimization–feasible sequential quadratic programming—is proposed and applied to performance seeking control. The genetic algorithm particle swarm optimization–feasible sequential quadratic programming not only has the global search ability of genetic algorithm particle swarm optimization, but also has the high local search accuracy of feasible sequential quadratic programming. The final simulation experiments show that, compared with feasible sequential quadratic programming, genetic algorithm particle swarm optimization, and genetic algorithm, the proposed optimization algorithm can get more installed thrust, decrease fuel consumption between 2% to 3%, and decrease turbine blade temperature larger than 15k, while meeting all of the constraints. Moreover, it also shows that the proposed modeling method has high accuracy and real-time performance.

Research on aero-engine steady model based on an improved compact propulsion system model

The aero-engine steady model is the basis of the modern advanced control method such as performance seeking control. An improved compact propulsion system model is proposed to improve the steady model accuracy. The improved compact propulsion system model mainly contains linear model, such as steady-state variable model, and physical-based models, such as inlet model, nonlinear model, and nozzle model. The improved compact propulsion system model applied to full envelop by parameter corrections. The basepoint control vector and basepoint output vector of improved compact propulsion system model are four-dimensional interpolation instead of two-dimensional interpolation as conventional compact propulsion system modeling does. The improved compact propulsion system model not only considers the change of engine state but also take the flight parameter into account. The simulations of the conventional compact propulsion system modeling and the improved compact propulsion system model are conducted in subsonic and transonic flight envelop. The simulations show that, compared with the conventional compact propulsion system modeling, the relative testing errors of the improved compact propulsion system model decrease greatly. Moreover, the testing time of the conventional compact propulsion system modeling and the improved compact propulsion system model are both almost equal to 0.027 ms.

A study on global optimization and deep neural network modeling method in performance-seeking control

In this article, a novel performance-seeking control method based on deep neural network and interval analysis is proposed to obtain a better engine performance. A deep neural network modeling method which has stronger representation capability than conventional neural network and can deal with big training data is adopted to establish an on-board model in the subsonic and supersonic cruising envelops. Meanwhile, a global optimization algorithm interval analysis is applied here to get a better engine performance. Finally, two simulation experiments are conducted to verify the effectiveness of the proposed methods. One is the on-board model modeling which compares the deep neural network with the conventional neural network, and the other is the performance-seeking control simulations comparing interval analysis with feasible sequential quadratic programming, particle swarm optimization, and genetic algorithm, respectively. These two experiments show that the deep neural network has much higher precision than the conventional neural network and the interval analysis gets much better engine performance than feasible sequential quadratic programming, particle swarm optimization, and genetic algorithm.

Research on Performance Seeking Control of Turbofan Engine at Cruise Status Based on Sequential Quadratic Programming Algorithm

The sequential quadratic programming algorithm was applied to the performance seeking control of multi-variable turbofan engine. According to the actual condition of aircraft cruise status, the constant thrust condition is added in the constraint functions of nonlinear programming. Based on the mathematical modes of turbofan engine, the SQP algorithm is adopted to the minimum fuel consumption mode of cruise condition. The simulation results show that after the optimization process, the engine fuel consumption rate decreased by nearly 2%, and the turbine temperature dropped by about 1.2%, the dual optimization benefits have been achieved. There are great potentials in engineering applications.

A Study on the Installed Performance Seeking Control for Aero-Propulsion under Supersonic State

AbstractAn integrated model including inlet, engine and nozzle with their internal and external characteristics was built to simulate the propulsion installed performance. With the integrated model, a new performance seeking control scheme under supersonic state is firstly proposed, taking inlet ramp angle as optimizing variable, which is equally important to fuel flow rate, nozzle throat area, guided vane angle of fan and compressor. Specially, engine installed thrust replaces its total thrust as one crucial factor for performance seeking control. Installed performances under supersonic state are significantly improved with the new scheme, as installed thrust increases of up to 4.9% in the maximum thrust mode, installed specific fuel consumption improvements of up to 3.8% in the minimum fuel consumption mode, and turbine temperature decreases of up to 0.6% in the minimum turbine temperature mode. The simulation results also indicates that, the performance seeking control scheme proposed shows superiority in restraining of the increasing of rotational speed and turbine temperature in performance seeking control.

Precision control of mechatronic systems with electromagnetically-steered moving masses

We perform fundamental and applied research in design of electromagnetic mechatronic systems to reposition an electromagnetically-suspended moving mass in three dimensions. Analysis, dynamic optimisation, hardware designs and other tasks are accomplished. Optimal minimal-complexity performance-seeking control laws are synthesised and experimentally verified using a proof-of-concept system. Accurate, fast and frictionless electromagnetically-steered repositioning of the suspended mass is achieved. This paper further enables a concept of path control of flight and underwater vehicles by changing the centre of gravity or centre of mass by varying the position of the moving mass. The proposed minimal-complexity performance-seeking control guarantees significant advantages in the design of closed-loop multi-input/multi-output electromechanical and mechatronic systems. The concept enables engineering solutions, provides new inroads in design of practical control schemes, advances technologies, fosters modular hardware organisations, relaxes algorithmic drawbacks, and results in high technology readiness levels. Our findings are applicable in various aerospace, automotive, naval and robotic applications.

Improved Hybrid Kalman Filter for In-Flight Aircraft Engine Performance Estimation

This paper proposes an improved hybrid Kalman filter(IHKF) for in-flight aircraft engine performance estimation. It’s a special hybrid structure of nonlinear on-board engine model(NOBEM) and piecewise linear Kalman filter(PWKF). The outputs of NOBEM are regarded as the baseline of PWKF, while its performance deterioration factors(PDF) regarded as the augmented state vector of PWKF are on-line estimated by the deviation of measured outputs, and fed back to NOBEM which can be on-line updated next time. In addition, the finite state machine logic of work mode has been established, and makes the IHKF work better. By this approach applied to a turbofan engine, a series of simulation results show that the model can estimate the real engine performance effectively within the whole flight envelope, different engine states and severe performance deterioration condition, which lays the foundation for intelligent engine control(IEC), performance seeking control(PSC) and in-flight fault detection, isolation and accommodation (FDIA).

Parameters Nonlinear Estimation of the Propulsion System Performance Seeking Control Using Improved PSO

The estimation of aeroengine component deviation parameters (CDP) is an important portion of aeronautical propulsion system performance-seeking control (PSC), which employs linear Kalman filter based on piecewise state variable model (SVM) traditionally. But it's not easy to get SVM, and the process of linearizing the nonlinear model to get the SVM will introduce errors. So parameters nonlinear estimation was introduced based on the nonlinear aeroengine model directly. The nonlinear estimation model is established according to aeroengine operation balance and the measured and calculated values matching of measurable parameters. The nonlinear estimation was changed to a problem of solving complex nonlinear equations, which is equal to an optimization problem. Time-varying inertia weight particle swarm optimization (PSO) with constriction factor was employed to solve the problem in order to satisfy the requirement of precision and calculation speed. The simulation results of a given turbofan engine show that utilizing the improved PSO algorithm can estimate the CPD precisely with satisfied converging speed.

飛行推進統合制御対応航空エンジン制御装置の研究

The Integrated Flight and Propulsion Control (IFPC) for a highly maneuverable aircraft and a fighter-class engine with pitch/yaw thrust vectoring is described. Of the two IFPC functions the aircraft maneuver control utilizes the thrust vectoring based on aerodynamic control surfaces/thrust vectoring control allocation specified by the Integrated Control Unit (ICU) of a FADEC (Full Authority Digital Electronic Control) system. On the other hand in the Performance Seeking Control (PSC) the ICU identifies engine's various characteristic changes, optimizes manipulated variables and finally adjusts engine control parameters in cooperation with the Engine Control Unit (ECU). It is shown by hardware-in-the-loop simulation that the thrust vectoring can enhance aircraft maneuverability/agility and that the PSC can improve engine performance parameters such as SFC (specific fuel consumption), thrust and gas temperature.