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This article describes research on the classification of flight phases using a fuzzy inference system and an artificial neural network. The aim of the research was to identify a small set of input parameters that would ensure correct flight phase classification using a simple classifier, meaning a neural network with a low number of neurons and a fuzzy inference system with a small rule base. This was done to ensure that the created classifier could be implemented in control units with limited computational power in small affordable UAVs. The functionality of the designed system was validated by several experimental flights using a small fixed-wing UAV. To evaluate the validity of the proposed system, a set of special maneuvers was performed during test flights. It was found that even a simple feedforward artificial neural network could classify basic flight phases with very high accuracy and a limited set of three input parameters.

The presented study offers a comprehensive insight in design and application of the Kalman filters for improvement of control efficiency of small unmanned aerial vehicles with fixed wing. The presented control scheme of the UAV includes its model, a servo drive model, and an optimal proportional-integral-derivative (PID) controller for the selected flight speed. Subsequently, process noise and measurement noise components were introduced into the whole constructed UAV model. The process noise was formed by disturbances caused by horizontal and vertical airflow in the atmosphere. The measurement noise contained deterministic and stochastic errors of the inertial measurement unit (IMU) UAV sensors and engine noise. The obtained results showed that the designed optimal Kalman filter was able to eliminate the influence of interfering signals from the control process and increased the phase safety, controllability, and stability of UAV control as a lean design can be applied in real-time control systems like Pixhawk PX4.

One way to efficiently control the fuel flow of small turbojet engines is to use direct control of the speed of a suitable electric fuel pump, as opposed to using relief valves. Brushless drive electric (BLDC) motors are suitable for this application, especially for small turbojet engines. This, however, comes with certain problems, which are caused by the backpressure of the engine on the fuel system, delays in data acquisition, sensor noise and so on. This paper presents a comprehensive approach in the methodological design of a dynamic model of a fuel system with a BLDC fuel/oil pump and control algorithms, which can overcome the mentioned problems. Three approaches are evaluated to obtain a stable and precise fuel metering of the engine. This article describes the design of an inverse control model, a standard PID controller and an adaptive fuzzy controller for fuel-flow control during engine operation. The main scientific contribution of the presented study is a simple, yet robust, fuzzy adaptive controller with a lean and comprehensive rule base that can precisely and efficiently meter fuel for small turbojet engines, validated in laboratory conditions using the iSTC-21v turbojet engine.

An original swarm-based method for coordination of groups of mobile robots with a focus on the self-organization and self-adaptation of the groups is presented in this paper. The method is a nature-inspired decentralized algorithm that uses artificial pheromone marks and enables the cooperation of different types of independent reactive agents that operate in the air, on the ground, or in the water. The advantages of our solution include scalability, adaptability, and robustness. The algorithm worked with variable numbers of agents in the groups. It was resistant against failures of the individual robots. A transportation control algorithm that ensured the spreading of different types of agents across exploration space with different types of environments was introduced and tested. We established that our swarm control algorithm was able to successfully control three basic behaviors: space exploration, population management, and transportation. The behaviors were able to run simultaneously, and space exploration (the main goal) was never stopped or interrupted. All these features combined in a single algorithmic package represent a framework for future development of swarm-based agent systems applicable in a broad scope of environments. The results confirmed that the algorithm can be applied to monitoring, surveillance, patrolling, or search and rescue tasks.

Improvements in reliability, safety, and operational efficiency of aeroengines can be brought in a cost-effective way using advanced control concepts, thus requiring only software updates of their digital control systems. The article presents a comprehensive approach in modular control system design suitable for small gas turbine engines. The control system is based on the methodology of situational control; this means control of the engine under all operational situations including atypical ones, also integrating a diagnostic system, which is usually a separate module. The resulting concept has been evaluated in real-world laboratory conditions using a unique design of small turbojet engine iSTC-21v as well as a state-of-the-art small turbojet engine TJ-100. Our results show that such advanced control system can bring operational quality of an engine with old turbocompressor core iSTC-21v on par with state-of-the-art engines.

There are only a few applications of infrared thermal imaging in aviation. In the area of turbojet engines, infrared imaging has been used to detect temperature field anomalies in order to identify structural defects in the materials of engine casings or other engine parts. In aviation applications, the evaluation of infrared images is usually performed manually by an expert. This paper deals with the design of an automatic intelligent system which evaluates the technical state and diagnoses a turbojet engine during its operation based on infrared thermal (IRT) images. A hybrid system interconnecting a self-organizing feature map and an expert system is designed for this purpose. A Kohonen neural network (the self-organizing feature map) is successfully applied to segment IRT images of a turbojet engine with high precision, and the expert system is then used to create diagnostic information from the segmented images. This paper represents a proof of concept of this hybrid system using data from a small iSTC-21v turbojet engine operating in laboratory conditions.

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.