Abstract

The state of present technologies in technical and also non-technical practice is represented by growing complexity of systems. A turbojet engine as a complex system is multidimensional highly parametric system with complex dynamics and strong non-linear behavior with stochastic properties. Its particular property is operation in a wide spectrum of changes of its operating environment (e.g., temperatures from -60 to +40 °C, different humidity, different pressures, etc.). If we want to secure optimal function of such system, it is necessary to develop models and control systems implementing the newest knowledge from the areas of automation, control technologies preferably with elements of artificial intelligence (AI). The present control systems and dynamic models are often limited to control or modeling of a complex system in its certain (operational) states. However, in practice the turbojet engine finds itself in very different operating conditions that influence its parameters of operation and characteristics. To create progressive control algorithms for a turbojet engine, it is necessary to design models in the whole dynamic spectrum of the modeled system including its erroneous states. Furthermore we need to design a control system that will secure operation converging towards optimality in all eventual states of working environment and also inner states of the system represented by its parameters. This leads to the need of having increased intelligence of control of turbojet engines that reduces workload of a pilot and also increases safety of operation. Safety represents a decisive factor in design of control systems of turbojet engines and is presently bound with increasing authority of them. The present trend designates such control systems as FADEC – Full Authority Digital Engine Control, however in reality such control systems have different levels of authority, intelligence and come in very different implementations. These are often not presented as they are intellectual properties of commercial companies. The article will be aimed on description of some present trends in development of FADEC systems and own proposals of methodologies leading towards design and implementation of a FADEC system with high level of intelligence able to solve all operational situations of a turbojet engine. This is strictly bound with presentation of modern methods of modeling of turbojet engines and the use of advanced methods of mainly sub-symbolic artificial intelligence. The proposed methods are all tested in real-world environment using a small turbojet engine MPM-20 in our laboratory setup. Therefore the article will also deal with approaches in digital real-time measurement of state parameters of this engine and design of control algorithms from engineering standpoint.

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