The possibility of synthesizing terminal control in the form of feedback by a nonlinear first-order object with parameters depending on the state of the object and additive control action is investigated. The use of a linear-quadratic regulator for this purpose is not possible due to the essentially nonlinear nature of the object’s dynamics. The SDRE method is used to construct the control law. It is theoretically proved that the constructed nonlinear SDRE controller ensures the transfer of an object from an arbitrary initial state to a small neighborhood of a given state in a predetermined finite time. The terminal error of regulation tends to zero when the penalty coefficient of the terminal term of the quality criterion is increased. A similar reduction in error is also achieved regardless of the value of the penalty factor by increasing the control time. The terminal properties of the regulator are demonstrated by the example of controlling the shutdown of an electric drive with a DC motor of sequential excitation, which is widely used in industrial robot drives. This electric motor belongs to devices with nonlinear dynamic characteristics. Calculations show that the regulator stops the electric drive in a short designated time with a favorable course of the transient shutdown process. The feedback of the control law helps to overcome the disturbing effect of possible uncontrolled loads on the shaft

The modern theory of automatic control is faced with the problem of complexity of synthesis of regulators for nonlinear control objects in conditions of incomplete information. The existing methods and approaches can no longer satisfy the needs of developers of automatic control systems for complex dynamic objects. In many cases, control objects are essentially nonlinear, nonstationary and require the use of digital control with specified quality indicators. In this case, obtaining an accurate mathematical model is not always possible. We propose an approach to solving this problem using regulators based on artificial neural networks. They can be effectively applied in the case when there is no adequate verified and sufficiently accurate mathematical model of the control object, but experimental data can be obtained. The advantage of such regulators is their ability to learn and adapt to the object based on the obtained data. In addition, there are no theoretical stability guarantees for closed-loop neural network control systems, which significantly reduces the possibility of their application in critical or hazardous facilities. To solve this problem, the paper proposes a method for synthesizing a neural controller that guarantees the stability of a closed loop. Systems with the most frequently encountered in practice nonlinearities (saturation type limiters, rigid mechanical stop type limiters, etc.) are considered as control objects. This paper proposes theoretical approaches to the solution of these problems, and also carries out a comparative analysis with experimental studies to assess the effectiveness of the proposed methods.

The author’s modification of a multidimensional fuzzy controller with a block for optimizing mode parameters and a block for predicting terms is considered. A block diagram of the controller, fuzzification and defuzzification schemes for continuous quantities are presented. The mechanism of operation of the logical inference block, which forms the identification number of the general composite production rule from the serial numbers of the terms of input and output variables with feedback, is described. The identification number is used as a key to retrieve information from the database about how to obtain specific numerical values of control actions, which is then transmitted to the controller defuzzification block. The general purpose of the term prediction block and the optimization block is shown. The prediction block is designed to transmit to the controller fuzzification block recommendations for a set of terms with which to begin processing the values of input variables in each scanning cycle. The optimization block is used to develop recommendations for optimizing operating parameters in accordance with specified criteria. The optimization block implements the author’s optimization algorithms, based on the use of evolutionary modeling methods and evolutionary algorithms adapted to a specific technological process. The formulation of the problem of optimal control of a dynamic process and an algorithm for its solution are presented. As an example, the problem of searching the optimal temperature regime in a batch ideal mixing reactor for the catalytic dimerization reaction of α-methylstyrene in the presence of a NaHY zeolite catalyst is considered. As a result of calculations using a genetic algorithm with real coding, where the genome is a real number, the suboptimal temperature of the refrigerant for the dimerization process of α-methylstyrene lasting 2 and 3 hours, and the corresponding concentrations of reagents, were calculated. The conducted computational experiment demonstrates the process of obtaining and issuing recommendations by a remote module for changing mode parameters and/or the system of production rules

To increase the capacity of the intersection and simultaneously reduce the travel time of the vehicle, optimization of traffic light control is necessary. The existing traffic light control systems cannot control dynamic systems in which several factors influence the decision-making process. The determination of factors (output variables) and the fuzzification process are the main problem of the fuzzy logic algorithm, and the quality of the compilation of the term set of input linguistic
variables and the definition of the function of belonging affect the optimal control of the light signals. The article provides an analytical overview of the ways of using linguistic variables for fuzzy inference systems when controlling traffic light signals. The subject of the article is the input linguistic variables for decision-making in a fuzzy management model. The analysis of modern research is presented and the main input linguistic variables are described. In the first section of the work, the general principle of building a rule base for fuzzy inference systems based on the Mamdani and Takagi-Sugeno methods is considered. The following sections are devoted to the peculiarities of such output linguistic variables that affect the operation of a fuzzy traffic light, such as: the number of vehicles, the current time of the green signal, road users (pedestrians), weather conditions and the number of lanes (width) of intersected roads. Accounting for these variables, their fuzzification and the formation of an appropriate rule base for the design of fuzzy systems is a very difficult task. In this regard, one of the key problems is precisely the problem of choosing the necessary input parameters depending on the type of intersection.
A review of the literature has shown that the research of the fuzzy controller in traffic management is still at the initial stage of development. Many of the unresolved issues raised in ozor can be addressed in further research

A new approach to the processing of satellite navigation measurements for the stable positioning of unmanned vehicles moving along program trajectories under conditions of interference is proposed. Modern methods of processing noisy satellite measurements mainly use various modifications of the least squares method, providing stability and the required positioning accuracy, as a rule, for stationary objects. At the same time, application of stochastic filtration theory methods that take into account both the dynamics of the object’s movement, and the presence of object disturbances and measurement noise are the most effective methods to assess the state of highly dynamic unmanned vehicles operating under conditions of uncertain disturbances. In this regard, the proposed approach to the positioning of unmanned vehicles is based on the application of nonlinear stochastic filtering methods, in particular, the robust nonlinear filtration method considered in the article that ensures the stability of the positioning process. At the same time, it is proposed to use digital path model to increase the accuracy of positioning an unmanned vehicle. This model is formed on the basis of high-precision geodetic measurements and providing the ability of approximation with the required accuracy of the program trajectory of the unmanned vehicle by a set of orthodromic trajectory intervals, which have an analytical relationship of the spatial coordinates of the object. This, in turn, ensures high positioning accuracy and a sharp reduction in computing costs. In general, the fusion of digital path model information and robust stochastic filtering algorithms for processing noisy satellite measurements has ensured both the stability of the process of estimating the current coordinates of an unmanned vehicle and a sharp reduction in computational costs compared with known methods of processing satellite measurement. The efficiency of the proposed method is shown by a numerical example.

One of the actual problems of modern cosmic dynamics is the development of systems for controlling the angular orientation of spacecrafts with respect to their centers of mass. To solve this problem, magnetic control systems based on the interaction of their executive devices with the Earth’s magnetic field are widely and effectively used. An important class of problems for controlling the angular spacecraft orientation is that of problems of monoaxial stabilization. This paper considers a satellite whose center of mass moves in a circular equatorial low Earth orbit. It is assumed that it is equipped with a controlled electrostatic charge distributed over a certain volume and a controlled magnetic moment. The rotational motion of a satellite with respect to its center of mass in the orbital frame is studied. The problem of monoaxial stabilization of a satellite in an arbitrary equilibrium position is solved. The electrodynamic control method is used which is based on the simultaneous application of a magnetic moment and the moment of Lorentz forces. Each of these moments is selected as a sum of damping, restoring and compensating components. To improve the characteristics of transient processes (damping unwanted oscillations and increasing the speed of convergence to program motion), PID controller of a special type is constructed. The stability analysis of the closed-loop system is carried out on the basis of the Lyapunov direct method. An original construction of the Lyapunov—Krasovskii functional is proposed, with the help of which the conditions on the control parameters are determined that guarantee the asymptotic stability of the program motion. The results of numerical simulation are presented confirming the obtained theoretical conclusions and demonstrating the advantage of the developed approach compared to the use of previously constructed controllers. It is shown that due to the appropriate choice of control parameters, the characteristics of transient processes can be significantly improved