scholarly journals Adaptive control systems for marine thermal installations

2021 ◽  
pp. 201-205
Author(s):  
С.А. Гордин ◽  
И.В. Зайченко ◽  
К.Д. Хряпенко ◽  
В.В. Бажеряну
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Control System ◽  
Control Systems ◽  
Pid Controller ◽  
Heat Supply System ◽  
Heating System ◽  
Thermal Loads ◽  
Quality Of Control

В статье рассмотрен вопрос повышения точности и качества управления приводом сетевых насосов в составе судовых тепловых установок в системе отопления судна путем применения адаптивной системы автоматического управления. При использовании классических систем управления на основе ПИД-регуляторов для управления мощностью электродвигателя по критерию обеспечения заданного давления в системе теплоснабжения в условиях резкопеременных тепловых нагрузок могут возникать ситуации разрегулирования системы вследствии возникновения дополнительного давления в тепловой установке при термическом расширении теплоносителя. Для обеспечения надежности и безаварийности работы судовых тепловых установок при резкоперменных нагрузках авторами рассматривается возможность использования для управления мощностью электропривода адаптивной системы управления. В статье рассмотрена схема управления с адаптацией коэффициентов ПИД-регулятора на базе нейронной сети (нейросетевой оптимизатор). Нейросетевой оптимизатор был применен как надстройка над ПИД-регулятором в схеме управления мощностью сетевого насоса в составе судовой тепловой установки. Рассмотрены зависимости характеристик систем управления от структуры и параметров модифицированных критериев точности и качества управления. Адаптация параметров регулирования позволяет обеспечить достижение желаемых параметров с меньшими затратами мощности при сохранении уровня надежности и исключить разрегулирование системы управления при резкопеременных тепловых нагрузках. The article discusses the issue of improving the accuracy and quality of control of the drive of network pumps as part of ship thermal installations in the ship's heating system by using an adaptive automatic control system. When using classical control systems based on PID regulators to control the power of the electric motor according to the criterion of providing a given pressure in the heat supply system under conditions of sharply varying thermal loads, situations of system maladjustment may occur due to the appearance of additional pressure in the thermal installation during thermal expansion of the coolant. To ensure the reliability and trouble-free operation of ship thermal installations under abruptly variable loads, the authors consider the possibility of using an adaptive control system to control the power of an electric drive. The article describes a control scheme with adaptation of the PID controller coefficients based on a neural network (neural network optimizer). The neural network optimizer was used as a superstructure over the PID controller in the power control circuit of a network pump as part of a ship's thermal installation. The dependences of the characteristics of control systems on the structure and parameters of the modified criteria for the accuracy and quality of control are considered. Adaptation of control parameters allows achieving the desired parameters with lower power consumption while maintaining the level of reliability and eliminating deregulation of the control system at abruptly varying thermal loads.

scholarly journals DEVELOPMENT AND RESEARCH OF A NEURAL NETWORK AUTOTUNING UNIT FOR A PID CONTROLLER OF THE ENERGY FACILITIES AUTOMATED PROCESS CONTROL SYSTEM

2021 ◽  
pp. 71-83
Author(s):  
I.A. Shcherbatov ◽  
◽  
V.A. Artushin ◽  
A.N. Dolgushev ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Control System ◽  
Process Control ◽  
Pid Controller ◽  
Process Control System ◽  
Adaptive Control System ◽  
Automated Process Control System ◽  
Automated Process

An adaptive control system based on a neural network autotuning unit has been developed. A method for training a neural network for an autotuning block has been examined. A comparison between a control system with a PID-controller and a control system with a PID controller and an autotuning unit has been made.

scholarly journals DEVELOPMENT OF STRUCTURAL SCHEMES AND OPERATION ALGORITHMS FOR THE AUTOMATIC CONTROL SYSTEM OF A MULTIFUNCTIONAL ENERGY COMPLEX

2021 ◽  
Vol 445 (1) ◽  
pp. 82-90
Author(s):  
G. Kalimbetov ◽  
A. Toigozhinovа ◽  
W. Wojcik
Keyword(s):  
Control System ◽  
Automatic Control ◽  
Control Systems ◽  
Dynamic Control ◽  
Control Object ◽  
Control Device ◽  
Automatic Control Systems ◽  
Operating Modes ◽  
Quality Of Control

Among the promising automatic control systems, logical-dynamic control systems that change both the structure and parameters of the control device using switches formed on the basis of a certain logical algorithm have proven themselves well. The use of logical algorithms as part of MACS subsystems for complex technical objects makes it possible to increase the static and dynamic accuracy of control due to purposeful qualitative and quantitative changes in the control signal. This approach will give the control system fundamentally new properties that allow to fully take into account the nature and dynamics of the movement of the control object. When developing existing logical control algorithms, the issues of their application for multi-connected and multifunctional objects control were not considered. Common to existing logical algorithms is that when switching the structure and/or changing parameters, only the dynamics of its own subsystem is taken into account, which is unacceptable in the case of multi-connected dynamic object control, since cross-links have a significant impact on the quality of control. Thus, the problem of synthesis of logical algorithms for multi-connected objects control is an actual theoretical and applied problem. Despite the considerable amount of research conducted in this area, the application of logical algorithms for complex multidimensional objects control is not sufficiently considered, and there is no unified design concept for this type of MACS, taking into account the required quality of functioning in various operating modes. In this regard, there is a need to synthesize algorithms for logical multi-connected control that form control signals in order to coordinate the actions of all separate MACS subsystems in accordance with new external conditions and operating modes. The problem under consideration determined the purpose of this work and the research objectives.

scholarly journals Relative control of an underactuated spacecraft using reinforcement learning

2020 ◽  
Vol 2020 (4) ◽  
pp. 43-54
Author(s):  
S.V. Khoroshylov ◽  
◽  
M.O. Redka ◽  
Keyword(s):  
Neural Network ◽  
Control System ◽  
Reinforcement Learning ◽  
Control Theory ◽  
Learning Algorithm ◽  
Control Algorithms ◽  
Iteration Algorithm ◽  
Quality Of Control ◽  
Control Actions

The aim of the article is to approximate optimal relative control of an underactuated spacecraft using reinforcement learning and to study the influence of various factors on the quality of such a solution. In the course of this study, methods of theoretical mechanics, control theory, stability theory, machine learning, and computer modeling were used. The problem of in-plane spacecraft relative control using only control actions applied tangentially to the orbit is considered. This approach makes it possible to reduce the propellant consumption of reactive actuators and to simplify the architecture of the control system. However, in some cases, methods of the classical control theory do not allow one to obtain acceptable results. In this regard, the possibility of solving this problem by reinforcement learning methods has been investigated, which allows designers to find control algorithms close to optimal ones as a result of interactions of the control system with the plant using a reinforcement signal characterizing the quality of control actions. The well-known quadratic criterion is used as a reinforcement signal, which makes it possible to take into account both the accuracy requirements and the control costs. A search for control actions based on reinforcement learning is made using the policy iteration algorithm. This algorithm is implemented using the actor–critic architecture. Various representations of the actor for control law implementation and the critic for obtaining value function estimates using neural network approximators are considered. It is shown that the optimal control approximation accuracy depends on a number of features, namely, an appropriate structure of the approximators, the neural network parameter updating method, and the learning algorithm parameters. The investigated approach makes it possible to solve the considered class of control problems for controllers of different structures. Moreover, the approach allows the control system to refine its control algorithms during the spacecraft operation.

scholarly journals Vessel heading control systems with switchable regulators

2021 ◽  
pp. 169-177
Author(s):  
Alexander A. Dyda ◽  
Nguyen Van Thanh Van Thanh ◽  
Ksenya N. Chumakova
Keyword(s):  
Control System ◽  
Control Systems ◽  
Transient Process ◽  
Control Process ◽  
Variable Structure ◽  
Quality Of Control ◽  
Heading Control ◽  
Course Control ◽  
Switching Regulators

The purpose of this work is to study the possibilities of improving the quality of the processes of controlling the movement of the vessel along the course by combining individual standard controllers. Of the known scientific directions devoted to the problem being solved, the closest is the theory of systems with variable structure, in which, due to switching, a unique useful property is achieved, which are not possessed by individual switched structures. The article is devoted to the approach to the construction of the ship course control system, which is based on the principle of switching regulators during the transient process. This makes it possible to improve the quality of control processes in the system by using the features of individual regulators, in particular, the application of the switching principle made it possible to significantly increase the speed of the system in comparison with systems without switching and ensure the desired monotonic nature of the control process. The proposed approach is illustrated based on switchable P-controllers. The results of modeling the developed ship course control system are presented and discussed.

scholarly journals Performance Assessment of Predictive Control—A Survey

Algorithms ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 97 ◽  
Author(s):  
Paweł D. Domański
Keyword(s):  
Control System ◽  
Model Predictive Control ◽  
Performance Assessment ◽  
Control Systems ◽  
Predictive Control ◽  
Successful Implementation ◽  
Assessment Procedure ◽  
Control Performance ◽  
Quality Of Control

Model Predictive Control constitutes an important element of any modern control system. There is growing interest in this technology. More and more advanced predictive structures have been implemented. The first applications were in chemical engineering, and now Model Predictive Control can be found in almost all kinds of applications, from the process industry to embedded control systems or for autonomous objects. Currently, each implementation of a control system requires strict financial justification. Application engineers need tools to measure and quantify the quality of the control and the potential for improvement that may be achieved by retrofitting control systems. Furthermore, a successful implementation of predictive control must conform to prior estimations not only during commissioning, but also during regular daily operations. The system must sustain the quality of control performance. The assessment of Model Predictive Control requires a suitable, often specific, methodology and comparative indicators. These demands establish the rationale of this survey. Therefore, the paper collects and summarizes control performance assessment methods specifically designed for and utilized in predictive control. These observations present the picture of the assessment technology. Further generalization leads to the formulation of a control assessment procedure to support control application engineers.

Disturbance learning controller design for unmanned surface vehicle using LSTM technique of recurrent neural network

2021 ◽  
pp. 1-11
Author(s):  
Sang-Ki Jeong ◽  
Dea-Hyeong Ji ◽  
Ji-Youn Oh ◽  
Jung-Min Seo ◽  
Hyeung-Sik Choi
Keyword(s):  
Neural Network ◽  
Control System ◽  
Flow Velocity ◽  
Recurrent Neural Network ◽  
Pid Controller ◽  
Short Term Memory ◽  
Ocean Currents ◽  
Ocean Current ◽  
Movement Speed ◽  
Marine Research

In this study, to effectively control small unmanned surface vehicles (USVs) for marine research, characteristics of ocean current were learned using the long short-term memory (LSTM) model algorithm of a recurrent neural network (RNN), and ocean currents were predicted. Using the results, a study on the control of USVs was conducted. A control system model of a small USV equipped with two rear thrusters and a front thruster arranged horizontally was designed. The system was also designed to determine the output of the controller by predicting the speed of the following currents and utilizing this data as a system disturbance by learning data from ocean currents using the LSTM algorithm of a RNN. To measure ocean currents on the sea when a small USV moves, the speed and direction of the ship’s movement were measured using speed, azimuth, and location (latitude and longitude) data from GPS. In addition, the movement speed of the fluid with flow velocity is measured using the installed flow velocity measurement sensor. Additionally, a control system was designed to control the movement of the USV using an artificial neural network-PID (ANN-PID) controller [12]. The ANN-PID controller can manage disturbances by adjusting the control gain. Based on these studies, the control results were analyzed, and the control algorithm was verified through a simulation of the applied control system [8, 9].

Calculation of Bounding Sets for Neural Network Based Adaptive Control Systems

2008 ◽  
Author(s):  
Giampiero Campa ◽  
Marco Mammarella ◽  
Bojan Cukic ◽  
Yu Gu ◽  
Marcello Napolitano ◽  
...  
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Control Systems ◽  
Adaptive Control Systems

Bounding set calculation for neural network-based output feedback adaptive control systems

2010 ◽  
Vol 20 (3) ◽  
pp. 373-387 ◽  
Author(s):  
Giampiero Campa ◽  
Mario Luca Fravolini ◽  
Marco Mammarella ◽  
Marcello R. Napolitano
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Control Systems ◽  
Output Feedback ◽  
Adaptive Control Systems

Neural network modeling of the efficiency of response to emergency situations in a multi-level control system

2021 ◽  
Vol 92 ◽  
pp. 79-93
Author(s):  
N. G. Topolsky ◽  
◽  
S. Y. Butuzov ◽  
V. Y. Vilisov ◽  
V. L. Semikov ◽  
...  
Keyword(s):  
Neural Network ◽  
Control System ◽  
Control Systems ◽  
Hierarchical Control ◽  
Hierarchical Systems ◽  
Level Control ◽  
Emergency Situations ◽  
Expert Assessments ◽  
Multi Level ◽  
The Relationship

Introduction. It is important to have models that adequately describe the relationship between the integral indicators of the functioning of the system with the particular indicators of the lower levels of management in complex control systems, in particular in RSChS. Traditional approaches based on normative models often turn out to be untenable due to the impossibility of covering all aspects of the functioning of such systems, as well as due to the high variability of the environment and the values of the set of target indicators. Recently, adaptive machine-learning models have proven to be productive, allowing build stable and adequate models, one of the variants of which is artificial neural networks (ANN), based on the solution of inverse problems using expert estimates. The relevance of the study lies in the development of compact models that allow assessing the effectiveness of the functioning of complex multi-level control systems (RSChS) in emergency situations, developing according to complex scenarios, in which emergencies of various types can occur simultaneously. Goals and objectives. The purpose of the article is to build and test the technology for creating compact models that are adequate to the system of indicators of the functioning of hierarchically organized control systems. This goal gives rise to the task of choosing tools for constructing the necessary models and sources of initial data. Methods. The research tools include methods for analyzing hierarchical systems, mathematical statistics, machine learning methods of ANN, simulation modeling, expert assessment methods, software systems for processing statistical data. The research is based on materials from domestic and foreign publications. Results and discussion. The proposed technology for constructing a neural network model of the effectiveness of the functioning of complex hierarchical systems provides a basis for constructing dynamic models of this type, which make it possible to distribute limited financial and other resources during the operation of the system according to a complex scenario of emergency response. Conclusion. The paper presents the results of solving the problem of constructing an ANN and its corresponding nonlinear function, reflecting the relationship between the performance indicators of the lower levels of the hierarchical control system (RSChS) with the upper level. The neural network model constructed in this way can be used in the decision support system for resource management in the context of complex scenarios for the development of emergency situations. The use of expert assessments as an information basis makes it possible to take into account numerous target indicators, which are extremely difficult to take into account in other ways. Keywords: emergency situations, hierarchical control system, efficiency, artificial neural network, expert assessments

scholarly journals The sensorless control system for controlling the speed of direct current motor

2019 ◽  
Vol 16 (3) ◽  
pp. 1171
Author(s):  
Khac-Khiem Nguyen ◽  
Trong-Thang Nguyen
Keyword(s):  
Direct Current ◽  
Pid Controller ◽  
Closed Loop ◽  
Dc Motor ◽  
State Equation ◽  
Dynamic State ◽  
Estimation Model ◽  
Loop Control ◽  
Quality Of Control

<p>This research aims to propose an algorithm for controlling the speed of the Direct Current (DC) motor in the absence of the sensor of speed. Based on the initial mathematical model of DC motor, the authors build the dynamic state equation of DC motor, and then build an estimation model to determine the speed of the DC motor without a sensor. The advantages of the proposed method are demonstrated through the closed-loop control model using the PID controller. In order for the results to be objective, we assume that the parameters of the DC motor in the estimation model are not known correctly. The results show that the quality of control in the absence of a sensor is equivalent to the case with the sensor.</p>

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