No abstract

681.513A method of constructing a selective measurement complex with variable structure is investigated. A measurement complex with intelligent component consisting of algorithms for the construction of predictive models and the comparison of a prediction with the current result of measurements is developed for precision determination of the parameters of a dynamic object. The algorithmic support of the complex is constructed on the basis of the Anokhin theory of functional systems with the use of a scalar estimation algorithm, self-organization algorithm with trend back-up, and criterion expressed as the degree of observability of the state variables. Models with elevated degrees of observability of the state variables are used in the information processing algorithms. Keywords: measurement complex, navigation system, intelligent system, self-organization algorithm, predictive model, degree of observability criterion.The precision with which the parameters of dynamic objects, in particular, aircraft, is measured depends on the service conditions and the structural features of the measurement systems and their algorithmic support [1,2]. The algorithmic support of aircraft measurement systems comprises estimation, control, prediction, and integration algorithms [1]. The most precise information can be obtained with the use of integration algorithms. By means of these algorithms, measurement systems based on entirely different physical principles are combined into measurement complexes, the methods of construction of which depend on the type and conditions of operation of the dynamic object, structural and fi nancial potential, and the required measurement precision. For example, the measurement complexes of small pilotless aircraft consist of inertial navigation systems and the receivers of GLONASS/GPS satellite systems, while reusable vehicles returning into the atmosphere are equipped with inertial navigation systems, GLONASS, radar and laser measurement systems, and stellar monitors, the signals of which undergo joint processing.All the functional features of the measurement systems and the aircraft fl ight conditions must be taken into account to obtain the maximally possible measurement precision. Therefore, we have developed a method of constructing a measurement complex based on elements of the theory of intelligent systems, the method of self-organization, and numerical observability criteria of the parameters of the particular system. The results obtained in studies of aircraft measurement complexes may also be used in other practical applications.Selective measurement complex. There now exist two approaches to the solution of the problem of integration, either placement onboard the aircraft of the greatest number of sensors information from which undergoes joint processing, and the use of a minimum number of sensors in a navigation complex. The fi rst approach requires an enhanced degree of performance of the onboard computers. Theoretically, such navigation complexes must assure a high degree of precisi...

No abstract

More accurate navigation systems are always required for autonomous unmanned underwater vehicles (AUUV)s under various circumstances. In this paper, a measuring complex of a heavy unmanned underwater vehicle (UUV) was investigated. The measuring complex consists of an inertial navigation platform system, a Doppler lag (DL) and an estimation algorithm. During a relatively long-term voyage of an UUV without surfacing and correction from buoys and stationary stations, errors of the measuring complex will increase over time. The increase in errors is caused by an increase in the deviation angles of the gyro platform relative to the accompanying trihedron of the selected coordinate system. To reduce these angles, correction is used in the structure of the inertial navigation system (INS) using a linear regulator. To increase accuracy, it is proposed to take into account the nonlinear features of INS errors; an adaptive nonlinear Kalman filter and a nonlinear controller were used in the correction scheme. Considering that, a modified nonlinear Kalman filter and a regulator in the measuring complex are proposed to improve the accuracy of the measurement information, and modification of the nonlinear Kalman filter was performed through a genetic algorithm, in which the regulator was developed by the State Dependent Coefficient (SDC) method of the formulated model. Modeling combined with a semi-natural experiment with a real inertial navigation system for the UUV demonstrated the efficiency and effectiveness of the proposed algorithms. Doppler lag, which are combined into a measuring complex (MC). The INS and lag signals are processed together using the Kalman filter [8][9][10][11].When the UUV is working under ice fields in the Arctic, there is no possibility of periodic ascent to the surface of the sea; thus, INS correction from a gyro-stabilized platform (GSP) is not provided. In the case of long-term autonomous navigation with the use of a strapdown INS, errors increase over time due to the instability of sensitive elements. Moreover, when UUVs perform maneuvers to complete tasks with long-term autonomous navigation, even for platform INS, errors will reach large values. This is due to an increase in the deviation angles of the gyro-stabilized platform (GSP) relative to the accompanying coordinate system (SC). Even with the correction of INS from a lag and information processing by the Kalman filter, errors of the navigation information increase as the model of INS errors in the Kalman filter becomes inadequate for the real process.Considering the prospective applications, scientists have been interested in AUUVs and all the particular constraints in different media have been formulated into mathematical problems. Wu et al.[12] generated the optimal paths based on the Particle Swarm Optimization (PSO) algorithm and the Kalman filter to finish an underwater target strike mission; Batista et al. [13] proposed a filtering method with applications to estimate the linear motion of underwater vehicles, taking into considert...

The paper presents a method of developing a variable structure measurement system with intelligent components for flight vehicles. In order to find a distinguishing feature of a variable structure, a numerical criterion for selecting measuring sensors is proposed by quantifying the observability of different states of the system. Based on the Peter K. Anokhin’s theory of functional systems, a mechanism of “action acceptor” is built with intelligent components, e.g. self-organization algorithms. In this mechanism, firstly, prediction models of system states are constructed using self-organization algorithms; secondly, the predicted and measured values are compared; thirdly, an optimal structure of the measurement system is finally determined based on the results of comparison. According to the results of simulation with practical data and experiments obtained during field tests, the novel developed measurement system has the properties of high-accuracy, reliable operation and fault tolerance.