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Modern turbojet engines mainly use computerized digital engine control systems. This opens the way for application of advanced algorithms aimed at increasing their operational efficiency and safety. The theory of robust control is a set of methods known for good results in complex control tasks, making them ideal candidates for application in the current turbojet engine control units. Different methodologies in the design of robust controllers, utilizing a small turbojet engine with variable exhaust nozzle designated as iSTC-21v, were therefore investigated in the article. The resulting controllers were evaluated for efficiency in laboratory conditions. The aim was to find a suitable approach and design method for robust controllers, taking into account the limitations and specifics of a real turbojet engine and its hardware, contrary to most studies which have used only simulated environments. The article shows the most effective approach in the design of robust controllers and the resulting speed controllers for a class of small turbojet engines, which can be applied in a discrete digital control environment.

The paper aims at the development of fuel supply and electronic control systems for boosted high- and middle-speed transport engines. A detailed analysis of different ways of converting diesel engine to operate on natural gas was carried out. The gas diesel process with minimized ignition portion of diesel fuel injected by the Common Rail (CR) system was selected. Electronic engine control and modular gas feed systems which can be used both on high- and middle-speed gas diesel engines were developed. Also diesel CR fuel supply systems were developed in cooperation with the industrial partner, namely, those that can be mounted on middle-speed diesel and gas diesel engines. Electronic control and gas feed systems were perfected using modeling and engine tests. The high-speed diesel engine was converted into a gas diesel one. After perfection of the gas feed and electronic control systems, bench tests of the high-speed gas diesel engine were carried out showing a high share of diesel fuel substitution with gas, high fuel efficiency and significant decrease of NOх and СО2 emissions.

Due to the substantial disparities in physical attributes between hydrogen fuel and conventional fuels, the development of an efficient controller presents a formidable challenge. In this context, this paper delves into the utilization of a model-based design (MBD) methodology for the purpose of conceiving and enhancing control systems for hydrogen-fueled turbojet engines. The investigation commences by adopting an established physical model of a hydrogen-fueled turbojet engine and subsequently validates its performance through rigorous simulation exercises. Consequently, this research undertakes a systematic deconstruction of the design process into discrete sub-phases, thus facilitating a seamless progression from system requirement analysis to system verification. This approach engenders a concurrent design and optimization of the control system. The ultimate confirmation of the controller’s efficacy and reliability is achieved through exhaustive simulations and Hardware-In-the-Loop testing. The research findings not only serve to augment design efficiency and mitigate design expenditures, but also propose avenues for further performance ameliorations in the realm of hydrogen-fueled turbojet engines. The control system accuracy of MBD is compared with the experimental results, and under high hydrogen fuel flow conditions, the errors reach an extremely low level of 0.1%. This affords a novel design paradigm within the domain of aero-engine control.

In this study, a real-time engine model and a test bench were developed to verify the performance of the EECU (electronic engine control unit) of a turbofan engine. The target engine is a DGEN 380 developed by the Price Induction company. The functional verification of the test bench was carried out using the developed test bench. An interface and interworking test between the test bench and the developed EECU was carried out. After establishing the verification test environments, the startup phase control logic of the developed EECU was verified using the real-time engine model which modeled the startup phase test data with SIMULINK. Finally, it was confirmed that the developed EECU can be used as a real-time engine model for the starting section of performance verification.

Due to the strong representation ability and capability of learning from data measurements, deep reinforcement learning has emerged as a powerful control method, especially for nonlinear systems, such as the aero-engine control system. In this paper, a novel application of deep reinforcement learning (DRL) is presented for aero-engine control. In addition, transition dynamic characteristic information of the aero-engine is extracted from the replay buffer of deep reinforcement learning to train a neural-network dynamic prediction model for the aero-engine. In turn, the dynamic prediction model is used to improve the learning efficiency of reinforcement learning. The practical applicability of the proposed control system is demonstrated by the numerical simulations. Compared with the traditional control system, this novel aero-engine control system has faster response speed, stronger self-learning ability, and avoids the complicated manual parameter adjustment without sacrificing the control performance. Moreover, the dynamic prediction model has satisfactory prediction accuracy, and the model-based method can achieve higher learning efficiency than the model-free method.

With the continuous development of industrial automation, diesel engines play an increasingly important role in various types of construction machinery and power generation equipment. Improving the dynamic and static performance of the speed control system of single-cylinder diesel engines can not only significantly improve the efficiency of the equipment, but also effectively reduce energy consumption and emissions. Particle swarm optimization (PSO) fuzzy PID control algorithms have been widely used in many complex engineering problems due to their powerful global optimization capability and excellent adaptability. Currently, PSO-based fuzzy PID control research mainly integrates hybrid algorithmic strategies to avoid the local optimum problem, and lacks optimization of the dynamic noise suppression of the input error and the rate of change of the error. This makes the algorithm susceptible to the coupling of the system uncertainty and measurement disturbances during the parameter optimization process, leading to performance degradation. For this reason, this study proposes a new framework based on the synergistic optimization of the untraceable Kalman filter (UKF) and PSO fuzzy PID control for the speed control system of a single-cylinder diesel engine. A PSO-optimized fuzzy PID controller is designed by obtaining accurate speed estimation data using the UKF. The PSO is capable of quickly adjusting the fuzzy PID parameters so as to effectively alleviate the nonlinearity and uncertainty problems during the operation of diesel engines. By establishing a Matlab/Simulink simulation model, the diesel engine speed step response experiments (i.e., startup experiments) and load mutation experiments were carried out, and the measurement noise and process noise were imposed. The simulation results show that the optimized diesel engine speed control system is able to reduce the overshoot by 76%, shorten the regulation time by 58%, and improve the noise reduction by 25% compared with the conventional PID control. Compared with the PSO fuzzy PID control algorithm without UKF noise reduction, the optimized scheme reduces the overshoot by 20%, shortens the regulation time by 48%, and improves the noise reduction effect by 23%. The results show that the PSO fuzzy PID control method with integrated UKF has superior control performance in terms of system stability and accuracy. The algorithm significantly improves the responsiveness and stability of diesel engine speed, achieves better control effect in the optimization of diesel engine speed control, and provides a useful reference for the optimization of other diesel engine control systems. In addition, this study establishes the GT-POWER model of a 168 F single-cylinder diesel engine, and compares the cylinder pressure and fuel consumption under four operating conditions through bench tests to ensure the physical reasonableness of the kinetic input parameters and avoid algorithmic optimization on the distorted front-end model.

The article presents the development of a lean approach for virtual electronic control unit (ECU) calibration. In this calibration method, virtual models are used to improve the calibration quality or reduce the calibration effort. Unlike state-of-the-art approaches, no dedicated engine simulation models for hardware-in-the-loop (HiL) operation are utilized. The developed engine simulation consists of physical ECU real-time capable 0D models. Major benefit of this approach is the multiple use of the developed models for virtual calibration of customer ECUs and vehicle operation using rapid-control-prototyping-ECUs (RCP-ECUs). The engine model consists of a physical air path, an air charge model, a gas exchange and a torque model as well as a novel mathematical combustion and exhaust gas temperature model. The configuration of the engine model was done for a turbo-charged four-cylinder gasoline reference engine. For the virtual calibration, the sensor values and actuator set points of the customer ECU are exchanged with a HiL real-time computer. Besides the ECU and a wiring harness, no additional hardware components are required. The virtual calibration was performed using only a proportion of the available experimental data. The result shows a satisfying accuracy of the nominal and actual path, compared to a conventional calibration dataset created with all available experimental test bench data. Applying the generated maps for air path actuator set points, air charge, spark timing, and efficiency, the required effort in terms of test bench time can be reduced significantly. For the operation of prototype vehicles, the models can be executed in real-time with a RCP-ECU. Due to the physical and mathematical modeling approach, the required calibration effort for engine and vehicle operation can be reduced significantly as well.

The speed of the engine–propeller directly determines the power output for Unmanned Aerial Vehicles (UAV) with internal combustion engines. However, variable air pressure can impact the engine’s air exchange and combustion processes, causing minor changes that affect the engine speed and result in variations in propeller thrust. A single-loop control strategy was proposed incorporating a feed-forward air-intake model with throttle feedback for small UAVs equipped with a two-stroke scavenging internal combustion engine and propeller. The feed-forward model was built with a simplified model of the airpath based on the scavenging measurement, which combined the tracer gas method and CFD simulation by a two-zone combustion chamber model. The feed-forward control strategy was built by a simplified crankcase–scavenging–cylinder model with CFD results under different air pressures, demonstrating a 1% error compared with CFD simulation. An iterative method of feed-forwarding was suggested for computing efficiency. A feedback controller was constructed using fuzzy PID for minimal instrumentation in engine control for small aircraft. Finally, the single-loop control strategy was validated through simulation and experimentation. The results indicate an 89% reduction in average speed error under varying air pressure and an 83.7% decrease in average speed overshoot in continuous step speed target experiments.

Distributed control architecture can bring many benefits to the engine control system, but the delay and packet dropout introduced by network communication will bring negative effects to the control system. The aging and deterioration of the engine are also obstacles in the design of the engine control system. This paper is concerned with the problem of guaranteed cost control for a distributed engine control system (DECS) with these negative constraints. Firstly, a model of DECS with multiple delays, packet dropouts and uncertainties is built. Secondly, a multiple delay-dependent guaranteed cost controller design method is proposed in the form of a set of linear matrix inequalities (LMIs). The non-convex optimal controller design problem is transformed into a convex optimization problem through the cone complementarity linearization (CCL) method, and the suboptimal controller is designed iteratively. Thirdly, turboshaft engine aging and deterioration are treated as sources of uncertainties, and the norm-bounded uncertain model of the turboshaft engine is modeled. Finally, the numerical simulations demonstrate the effectiveness and applicability of the guaranteed cost controller designed for DECS with multiple delays, packet dropouts, engine aging and deteriorations.

To meet greenhouse gas (GHG) emission target, automotive manufacturers should promote low carbon emission technology, including gasoline direct injection (GDI) systems. However, recent studies have shown that excessive levels of nanoparticles were emitted from GDI vehicles compared to port fuel injection (PFI) vehicles. One of the many ways to decrease nanoparticle emissions from GDI engine is to use alternative fuel This study used turbo charged 2.0 L 4-cylinder LPG direct injection engine (T-LPDi) that was converted from a turbo charged gasoline direct injection engine (T-GDI) with dedicated LPG fuel supply and control system. To analysis on combustion phenomena and nanoparticle emissions, in-cylinder pressure and exhaust gas were measured under engine dynamometer test. Additionally, various engine control parameters were swung to understand the effects of the control parameters on combustion and nanoparticle characteristics. Throughout this study, T-LPDi engine, compared to T-GDI engine, showed ∼ 9 % and 76 % reduction of CO2 and PN emissions respectively. By optimizing engine parameters based on parametric study, PN emissions were improved 70 % from the baseline of T-LPDi engine emissions.