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.

On-board electrical systems are the key components of each modern aircraft. They enable its safer, more comfortable, and environmentally friendlier operation. The strict regulations to reduce pollution and noise are produced by aircraft eventuated in projects like Clean Sky or ICAO Global Coalition for Sustainable Aviation. One solution to environmentally friendlier operation is the full electric propulsion of the aircraft, which enables the reduction of both noise and pollution. Such a concept requires a total change of all on-board power systems and enables the profound change in aircraft design. This paper presents the evolution of aircraft power systems into the so-called more electric aircraft (MEA) and discusses the state-of-the-art electrical systems. Furthermore, the concept of all-electric aircraft (AEA) is presented here.

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 article was motivated by the design of adaptive control algorithms for the control of a a fixed wing unmanned aerial vehicle (UAV). An adaptive system is a system that, with its structure or parameters, adapts to changes in the behavior of the object and based on the knowledge of variable properties, maintains the quality of its regulatory transition. The knowledge gained on this small UAV can be applied to larger aircraft. The creation of the proposed adaptive control into the UAV consisted of the creation of a simulation model of the aircraft based on known physical laws, the properties of the aircraft and a mathematical description. An adaptive PID controller for stabilization with changing coefficients based on the airspeed of the aircraft was designed and simulated. A validated control of the mathematical model of an unmanned aircraft was designed and simulated using the methods of estimation and identification of the UAV model parameter based on measured data from flight tests. Identifying dynamic parameters is a challenging task due to several factors, such as random vibration noise, interference, and sensor measurement uncertainty. The designed adaptive UAV control provides very promising results in improving the controllability of the aircraft while reducing the effect of speed changes on the stability and controllability of the system compared to the conventional PID controller. The comparison was performed on three selected types of PID controllers. The first type had fixed coefficients for the entire range of speeds calculated using the Control toolbox in MatLab. The second type also had constant coefficients over the entire range of speeds calculated using the Naslin method. The third adaptive type of PID controller had variable coefficients based on approximate polynomials dependent on the change in flight speed. The reason for the comparison was to show an increase in margin of stability using the method of variable coefficients of the PID controller based on the change of flight speeds. The obtained results show that the proposed adaptive control algorithm is robust enough to control the movement of the aircraft in the longitudinal plane and due to the introduced process and measurement errors, while the used Kalman filter effectively eliminates these errors.

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.