## 1 Introduction  
One of the dynamically developing areas of robotics is unmanned aerial technologies, while UAVs are used in various fields of human activity [1; 2]. The expansion of the range of areas of application of UAVs, their number, nomenclature and specialization necessitate the creation of UTM. Examples of such systems are the American Unmanned Aircraft System Traffic Management (UTM), the Chinese Aviation Operation Management System (UOMS) and the European U-Space [3; 4; 5]. These and other promising UAV control systems, which have a distributed structure and contain ground and space segments, should provide the following:  
* Safety of UAV flights on the scale of one or several countries, including in the area of civil aviation flights;
* The possibility of solving various functional tasks within a single system;
* The possibility of using a UAV of various types and manufacturers to solve these problems. From the point of view of system analysis, UAV control systems are CTSs and represent a set of functionally independent subsystems interacting with each other under the conditions of the stochastic nature of the external environment, and they are designed to achieve a common goal [6; 7]. The most universal characteristic of a CTS is the efficiency of its functioning; however, in the scientific and technical literature, there is no single interpretation of this term and, as a rule, CTS efficiency is understood as follows [8; 9; 10; 11]:* Degree of compliance of the system with the purpose and adaptability to achieve the goals set during its creation;
* Possibility of achieving maximum results at given costs for the creation of the system. The first definition assumes that the evaluation of the effectiveness of a CTS is based on the achievement of the targets for which it is developed, and the second one reflects its economic feasibility, which is not the subject of this article. In this article, we will use the first definition, while we assume that the quality of solving the target problems facing a complex system is determined by the result of achieving specific goals. In other words, through the effectiveness of a CTS, we will understand the property of the system to fulfill the set goal under given conditions and with a certain quality. At the same time, the performance indicators of a CTS should characterize the degree of adaptability of the system to solve the tasks assigned to it and are generalizing indicators, which in turn depend on local indicators (reliability, safety, etc.). In turn, a quantitative measure of quality of solving target problems is the magnitude of the criterion for the effectiveness of solving functional problems assigned to a CTS. There can be quite a lot of such criteria, while they, as a rule, are stochastic and represent probabilistic quantities; for example, reliability is the probability that the system/subsystem will be operational for a certain time, efficiency is the probability that the system/subsystem response time will not exceed a given value, etc. Therefore, they are often called partial criteria or performance indicators (we will use the first term). Thus, the assessment of the effectiveness of a CTS as a whole should be carried out taking into account the global criterion which is a vector quantity and in a certain way integrates a particular criterion. This problem belongs to the class of multicriterial problems [12; 13; 14] and the most common way to solve it in practice is convolution, i.e., transition from a vector function to a scalar one. Such a transition from a multicriterial problem to a single-criterion one is carried out by combining all particular criteria of \(f_{l}(x)\) into one global criterion of \(f(x)=F(f_{1}(x),f_{2}(x),\,\ldots\,\,f_{n}(x))\). In this case, the solution of the problem is reduced to finding the extremum of the only function \(f(x)\), and the form of the function is determined by how the contribution of each particular criterion can be represented.