To solve the problem of transient control design with uncertainties and degradation in the life cycle, a design method for a turbofan engine’s transient controller based on reinforcement learning is proposed. The method adopts an actor–critic framework and deep deterministic policy gradient (DDPG) algorithm with the ability to train an agent with continuous action policy for the continuous and violent turbofan engine state change. Combined with a symmetrical acceleration and deceleration transient control plan, a reward function with the aim of servo tracking is proposed. Simulations under different conditions were carried out with a controller designed via the proposed method. The simulation results show that during the acceleration process of the engine from idle to an intermediate state, the controlled variables have no overshoot, and the settling time does not exceed 3.8 s. During the deceleration process of the engine from an intermediate state to idle, the corrected speed of high-pressure rotor has no overshoot, the corrected-speed overshoot of the low-pressure rotor does not exceed 1.5%, and the settling time does not exceed 3.3 s. A system with the designed transient controller can maintain the performance when uncertainties and degradation are considered.

To support the advanced controller design and verification of the Aeropropulsion System Test Facility (ASTF), it is necessary to establish a mathematical model of ASTF with high precision and replace the current lumped parameter model. Therefore, a quasi-one-dimensional flow model of ASTF is established considering friction, localized losses, heat transfer, etc. Moreover, a generic modeling method is proposed for quasi-one-dimensional flow. With this method, all component models of ASTF are composed of staggered central control volume (CCV) and boundary control volume (BCV) and connected through virtual control volume. Thus, the properties of quasi-one-dimensional flow, such as spatial effect and time delay, can be easily addressed during the modeling process. The simulation results show that the quasi-one-dimensional flow model has higher accuracy than the lumped parameter model. Comparing the simulation results of the quasi-one-dimensional flow model with the test data, the relative errors of flow and pressure are less than 2.2% and 1.4%, respectively, further verifying the correctness of the proposed modeling method.

To solve the modeling problem of altitude ground test facility (AGTF) exhaust systems, which is caused by nonlinearity along the gas path and the difficulty of ejection factor calculation, a multi-cavity iterative modeling method is presented. The components of exhaust systems, such as the exhaust diffuser and cooler, are built with a series of volumes. It overcomes the disadvantage that traditional lumped-parameter models have, whereby they cannot calculate the dynamic parameters along the gas path. The exhaust system model is built with an iterative method based on multi-cavity components, and simulations are carried out under experimental conditions. The simulation results show that the maximum error of pressure is 2 kPa in the steady state and less than 6 kPa in the transient process compared with experimental data. Closed-loop simulations are also carried out to further verify the accuracy and effectiveness of the multi-cavity iterative exhaust system modeling method.

The onboard adaptive model is essential to the model-based control and diagnosis of the engine. However, current methods, such as the Kalman-based and the data-driven ones, cannot meet the demands of performance estimation well. Their self-tuning processes lead to a long period of model mismatch and, thus, degrade the quality of control and diagnosis, even causing engine failures. To overcome this disadvantage, a novel onboard adaptive model with fast estimation capability is proposed. The proposed method employs a component level model as the benchmark and introduces some scaling factors as the model tuners. These tuners are derived from the measurements and defined to quantify the characteristic deviations of the engine components at a certain operating condition. An algorithm with memory function is introduced to store the correlations between the tuners and the operating condition and, thus, predict these tuners according to the operating condition of inputs. By feeding the predicted tuners to the benchmark model, the engine performance can be estimated rapidly. Simulations are implemented to demonstrate the effectiveness of the proposed model. The results show that it has not only a high estimation accuracy at steady operating states, but also a short dynamic response time and the memory ability to avoid repeated self-tuning processes when the operating state of the engine varies.

Nonlinear control problems in turbofan engines are challenging. No single nonlinear controller can achieve desired control effects in a full flight envelope, but in the case of multiple controllers, there exist problems in the bumpless transfer between different controllers. To this end, this paper presents a bumpless transfer mechanism for an uncertain switched system based on integral sliding mode control (ISMC), and the mechanism can be used for the speed control of turbofan engines. The uncertain switched system is used to describe the turbofan engine dynamics. Then, the ISMC controller is derived for subsystems of the uncertain switched system. A resetting scheme is introduced for the ISMC controller to ensure the continuity of control inputs during the controller transition, as well as the bumpless transfer. In view of the transient behavior caused by controller switching, the global stability of the switched system is analyzed using the multiple Lyapunov function approach and average dwell time condition. Simulation results validate that the designed resetting scheme can ensure the continuity of control input signals and avoid the instability caused by high-frequency controller switching, and increase the control effectiveness of the proposed ISMC method within the full flight envelope.

Modelica is an open-source, object-oriented equation-based modeling language. It is suitable for describing sophisticated dynamic systems (symmetry/asymmetry) as it uses mathematical acausal equations to express physical characteristics. The hydraulic mechanical units (HMU) of gas turbine engine control systems couple the contents of mechanical, hydraulic, symmetry, and other multidisciplinary fields. This paper focuses on the Modelica description method of those HMU models. The content of this work is threefold: firstly, the division form of basic elements in HMU is defined, and the method for describing these element models with Modelica is proposed; secondly, the organization of the element models is defined by using the inheritance characteristics of Modelica, and a lightweight (small code scale) component model is designed; and finally, the causal/acausal connections are designed according to bond graph theory, and the elements and components are integrated into a prototype modeling library. In this paper, the modeling library is verified by comparing simulation results of five typical HMU subsystem models with commercial modeling and simulation software.