Group Control System Research Articles

ДОСЛІДЖЕННЯ ТА АЛГОРИТМИ ІНТЕГРОВАНОГО КЕРУВАННЯ ГРУПОЮ БЕЗПІЛОТНИХ ЛІТАЛЬНИХ АПАРАТІВ

The main prospects for the group use of unmanned aerial vehicles (UAVs) were analyzed, the problems were identified, and the stages of its solution were proposed. The basic algorithms are analyzed, which are designed to achieve joint and coordinated actions of all elements of the system of integrated control of a group of UAVs, and are used in most approaches to ensure stable interaction between system components. The analysis of the most common mathematical models for controlling groups of UAVs was carried out, in particular: the Reynolds cohesion model, the artificial potential field model, and the leader-following model. The peculiarities of the operation of these models, common and distinctive features, advantages and disadvantages are determined. Based on the considered models and basic interaction algorithms, the algorithms of the components of the UAV group control system are proposed, and the sequence of actions during the interaction between the system components is also described. The main details of UAV group control algorithms are considered. The main aspects of the interaction of the system elements are given, which include messages of various categories used at different stages of the system's operation, in particular: synchronizing messages; group-wide control messages for managing the group as a whole; messages for correcting the flight of a specific UAV; notification of the transfer of statistical information for flight correction; notification of the launch and coordination of the task execution by a specific UAV. The use of joint messages for group management is described, which allows you to relieve the transmission channel, because the flight trajectories are calculated by each UAV individually, according to predetermined criteria. The main stages of the system's operation, the peculiarities of their implementation, the sequence of message transmission in each stage, as well as the basic details of the operation of some system components are described. Block diagrams are provided to display the process of system operation at various stages of flight, with a detailed description of the steps of system operation. The process of collecting statistical data from UAVs for dynamic flight adjustment of the group is described.

Exploring Heuristic and Optimization Approaches for Elevator Group Control Systems

This paper undertakes an examination of elevator car dispatching methods in response to hall calls. Firstly, our study focuses on the establishment of a representation of an elevator group control system as a finite-state machine to understand the dynamics of elevator group control. Secondly, two primary heuristics are explored, with the first advocating directional continuity unless the highest or lowest floor has been reached, while the second permits direction change upon completing the final call, regardless of floor extremes. Identified inefficiencies in these heuristic solutions lead us to explore enhanced alternatives. Consequently, we delve into genetic algorithm (GA) and simulated annealing (SA) methodologies. Our focus initially centers on devising solution representations and determining fitness evaluations for both approaches. We employ a simulation-based optimization approach to identify the optimal parameter values for both simulated annealing and genetic algorithms. A subsequent comparative analysis is conducted to ascertain the most effective approach among these diverse solutions. A comparative analysis reveals that the GA-based approach significantly outperforms both existing heuristics and the SA-based method in minimizing average passenger waiting time at the cost of longer computational time.

A Multi-Objective Optimization Approach for Elevator Group Control Systems Based on Particle Swarm Algorithm

Currently, elevator group control systems containing multiple elevators are used in high-rise buildings, where elevator groups are centrally dispatched according to a set dispatching plan to provide vertical transportation services for the passengers. However, if these elevators work independently, once passengers send out a call signal, multiple elevators will respond to the same request at the same time, and the elevator group will repeatedly go back and forth, which seriously affects the system operation efficiency and also increases energy consumption, so it is a key issue to solve the dispatching problem in the elevator group control system. Elevator group control scheduling is an NP-hard problem with explosive combination characteristics. In this paper, a multi-objective model is formulated based on the criteria of average passenger travel time, average waiting time and system energy consumption for the scheduling problem. Then the particle swarm optimization algorithm is proposed to solve the scheduling problem. Finally, the performance of the proposed algorithm is compared with the genetic algorithm-based elevator group control scheduling, the simulation results show that the proposed algorithm with fast convergence while the waiting time of passengers is significantly decreased.

NEURO-FUZZY MODELING OF A DECISION SUPPORT SYSTEM BY A GROUP OF UNMANNED AERIAL VEHICLES UNDER THE INFLUENCE OF THE EXTERNAL ENVIRONMENT

The work discusses the relevance and concept of building an intelligent decision support system based on the use of elements of the theory of fuzzy sets together with the technology of neural networks. The existing requirements for the intellectualization of the control system of a heterogeneous group of unmanned aerial vehicles force developers to switch to the use of artificial intelligence technologies that allow for the training of complex energy-consuming algorithms and ensure their self-organized functioning when solving tasks for their intended purpose. Due to the fact that the process of functioning of a heterogeneous group of unmanned aerial vehicles is described by complex models that take into account the heterogeneity of the group, versatility and multiscenarity when performing tasks for their intended purpose, there is a need to model these processes on a computer. The purpose of the simulation is to obtain a digital version of the double of a real process or object. The probabilistic nature of the description of environmental influences encourages the construction of a model that takes into account all possible behavioral strategies as much as possible and the development of various counteraction scenarios. The most suitable technology capable of analyzing all strategies of the external environment and forming the resulting vector of all situations arising in the process of performing tasks by a group of UAVS for their intended purpose is the well-proven technology of neural networks. However, just getting one solution at the output of the system is not enough, so they resort to modeling situations based on the rule base. Currently, it is easy to form such a base and build a fuzzy model of actions based on it, when the outcomes of alternatives are known inaccurately and the probabilities of their occurrence are estimated using the membership function. Decision-making is based on the method of an ideal point, and the range of acceptable solutions is constructed using a genetic algorithm. The introduction of neural network technology will maximize the viability of the functioning of a group of unmanned aerial vehicles when controlling it both with the help of an operator and offline. The article considers the model of functioning of a group of unmanned aerial vehicles in the conditions of destructive environmental influences.

Research on Synthesis of Multi-Layer Intelligent System for Optimal and Safe Control of Marine Autonomous Object

The article presents the synthesis of a multi-layer group control system for a marine autonomous surface vessel with the use of modern control theory methods. First, an evolutionary programming algorithm for determining the optimal route path was presented. Then the algorithms—dynamic programming with neural state constraints, ant colony, and neuro-phase safe control algorithms—were presented. LMI and predictive line-of-sight methods were used for optimal control. The direct control layer is implemented in multi-operations on the principle of switching. The results of the computer simulation of the algorithms were used to assess the quality control.

Metaheuristic algorithms for elevator group control system: a holistic review

Metaheuristic algorithms for elevator group control system: a holistic review

Application of supervisor synthesis methods in designing a discrete-event group control system for mobile robots

This paper discusses the features of methods used to synthesize a discrete-event supervisor for a group control system of mobile robots. We consider the upper levels of a group control system as a discrete-event system. Su-pervisor synthesis methods for such a system and the evaluation of their computational complexity are presented. The paper highlights the limitations of the best method in terms of computational complexity and proposes a modi-fication that allows to remove these limitations without increasing the computational complexity. The application of the modified method is demonstrated on the example of synthesizing a supervisor for the group action of a fire-fighting operation by three robots.

About Some Aspects of Finite State Machine Models Application to Group Control

The article discusses using discrete-event models to control the coordinated behavior of groups of agents and proposes a mechanism for organizing behavior control based on a hierarchy of finite state machines (FSM). The basic level of agent movement control uses a continuous model of joint movement — the method of potentials, in which the direction of movement is determined by the total sum of the forces of "repulsion", "attraction" and "course alignment". The paper describes a set of independent rules for the group movement of agents — cohesion, distribution, leader following, movement along a chain of landmarks, pursuit, etc. Moreover, all of them are interpreted in terms of the results of the operation of FSM models. Mathematical modeling of controlling a group of agents was carried out using the independent rules of group movement, which confirmed their efficacy. Computational experiments were also carried out using the Gazebo 3D simulator. In addition, the paper describes an experimental group of three underwater vehicles that were used to test the independent rules of group movement: cohesion, distribution, leader following. Communication between the devices was carried out using a system of infrared transceivers via a pseudo-analog channel. The experiments were carried out in a laboratory pool and were successful.The paper shows that the technology for creating group control systems based on the hierarchy of FSM (meta-FSM) allows solving control problems not only at the level of abstract models and simulation modeling, but also at the level of creating real robotic complexes.

Problem of synthesis of the control system for a group of unmanned ground vehicles

The substantive and formal formulation of the control system synthesis problem for a group of unmanned ground vehicles (UGV) are considered in the article. The decomposition of this problem into a number of particular scientific problems is carried out, such as the problem of substantiating a structure of the control system, developing a method for determining the emergence of the control system, and developing a method for accessing the quality of a control system and the effectiveness of its application. It is shown that the distinctive features of the problem are the stochastic nature of the performance indicator (a probability of achieving the goal of the mission), the uncertainty of the conditions for applying the UGV’s group, and the large size of the design space of the control system. The problem of design space growth is demonstrated by a methodical example. To reduce the complexity of analyzing the size of the design space, we propose the decomposition approach consisting in substantiating the basic version of the structure of the control system and its initial decomposition. The novelty in the implementation of this approach lies in the joint consideration of the «group control method» and the principle of hierarchical of the group control system. This approach allows us to obtain basic solutions for the structure of the control system, and then carry out a parametric synthesis of the control system and conduct a comparative assessment of solutions according to the «Quality-Costs» concept.

A Hierarchical Approach to Intelligent Mission Planning for Heterogeneous Fleets of Autonomous Underwater Vehicles

The rapid development of marine robotic technology in recent decades has resulted in significant improvements in the self-sufficiency of autonomous underwater vehicles (AUVs). However, simple scenario-based approaches are no longer sufficient when it comes to ensuring the efficient interaction of multiple autonomous vehicles in complex dynamic missions. The necessity to respond cooperatively to constant changes under severe operating constraints, such as energy or communication limitations, results in the challenge of developing intelligent adaptive approaches for planning and organizing group activities. The current study presents a novel hierarchical approach to the group control system designed for large heterogeneous fleets of AUVs. The high-level core of the approach is rendezvous-based mission planning and is aimed to effectively decompose the mission, ensure regular communication, and schedule AUVs recharging activities. The high-level planning problem is formulated as an original acyclic variation of the inverse shift scheduling problem, which is NP-hard. Since regular schedule adjustments are supposed to be made by the robots themselves right in the course of the mission, a meta-heuristic hybrid evolutionary algorithm is developed to construct feasible sub-optimal solutions in a short time. The high efficiency of the proposed approach is shown through a series of computational experiments.

Improving waiting time and energy consumption performance of a bi-objective genetic algorithm embedded in an elevator group control system through passenger flow estimation

Improving waiting time and energy consumption performance of a bi-objective genetic algorithm embedded in an elevator group control system through passenger flow estimation

ФОРМАЛИЗОВАННЫЙ ПОДХОД К СИНТЕЗУ АРХИТЕКТУРЫ СИСТЕМЫ АДАПТИВНОГО ГРУППОВОГО УПРАВЛЕНИЯ РОБОТОТЕХНИЧЕСКИМИ КОМПЛЕКСАМИ В УСЛОВИЯХ НЕДЕТЕРМИНИРОВАННОЙ ДИНАМИЧЕСКОЙ СРЕДЫ

The rapid development of "multi-agent systems" as an independent and multifaceted sectionof artificial intelligence attracts many researchers in various fields of activity. The pace of progressin the development of information technologies, distributed information systems, and computertechnology determines the possibilities of using robotics technologies in the Armed Forces ofthe Russian Federation. The factors presented in the article authorize the need to introduce newintelligent technologies into the troops - autonomous robotic complexes (systems). The developmentof artificial intelligence methods makes it possible to take a new step towards changing thestyle of interaction of complexes with each other as part of a robotic system. The idea of creatingso-called "autonomous complexes" arose, which gave rise to a new style of adaptive group management.Instead of interaction initiated by the user-operator through commands and direct manipulations,complexes are independently involved in the joint process of solving a common problemin a non-deterministic dynamic environment. The article proposes a formalized approach tothe design of architectures for group interaction of autonomous robotic complexes in a systembased on the law of open control, i.e. induced and reliable preferences of each complex for action,satisfying the conditions of perfect coordination of their activities, by identifying parameters atwhich the objective function is maximized in various modes of functioning of the robotic system. Aformalized formulation of the problem of synthesis of the adaptive group control system of autonomousrobotic complexes under conditions of a priori uncertainty is presented. The architecture ofgroup interaction of complexes is adaptively built based on the conditions of the external environmentand the internal state of the system, in which each complex of the group functions to achievea common goal (solving a system problem) at the time under consideration.

Design of Lift Group Control Systems based on PLC

Aims and Study Design: Lifts are presently inevitable requirements in modern day building complexes especially multiple floor buildings to keep up with human transportation. Due to this fact, efficient, cost-effective and reliable lift systems are required in these buildings. In this work, the design of a robust lift group control system based on programmable logic controllers (PLC) is introduced.
 Methodology: To achieve this goal, good choices of a hardware platform comprising a PLC complex and a software platform to feed instructions (ladder logic) to the PLC are needed. These platforms are linked via a network connection over an Ethernet. The development of the control network based on fuzzy control is illustrated, which the lift controller uses to recognize traffic and respond to the detected traffic patterns. A simplified form of the algorithm from the moment of a hall call to the point of execution of this call command is presented with simulations demonstrating lift prioritization and scheduling based on dependence on interplay between the input fuzzy variables.
 Results: The presented design method shows advantages in accounting for numerous factors such as priority fitting, space availability and lift proximity when calculating and implementing quicker hall call responses, which helps in minimizing waiting time of passengers as demonstrated with the 5 floor double lift system. .
 Conclusion: The work is believed to set a basis for future PLC-based lift group control systems (PLC-LGCS).

Elevator Group Control System Based on Fuzzy Reasoning

Due to the characteristics of the elevator system itself, traditional control algorithms are difficult to apply to elevator dispatching. However, the fuzzy control algorithm does not require the establishment of an accurate mathematical model, and can be well integrated with the elevator system. Based on fuzzy control, the elevator dispatch can be optimized. The traffic fuzzy inference controller is designed to identify the elevator traffic mode according to the flow of people; the elevator fuzzy inference device is designed to construct the objective function according to the evaluation index. Siemens S7-1200 series PLC is used to control the elevator, the elevator model uses Elevator Simulation Pro for simulation debugging, and Portal WINCC is used as the host computer for debugging to monitor.

Nonparametric control algorithm for metal temperature mode on site BOF – CCM

A two-level control system for the temperature mode of smelting, out-of-furnace processing and preparation for casting of low-carbon steel G/ET is proposed in the conditions of BOF shop-2 of JSC “United West Siberian Metallurgical Combine”. Depending on the technological scheme, it is possible to design various control systems for the steelmaking complex with sequential, parallel and combined inclusion of individual operations and processes. The control system of a sequential group of objects is considered on the example of steel G/ET. The control system includes an external control loop that allows coordinated control of the shop departments by optimizing the mode of technological process conducting at the facility, taking into account the actual operation performed at the previous facility. The implemented nonparametric algorithm of dual control allows the decision-maker to perform joint operational adjustment of control actions for local control loops. The temperature mode of the melts of low-carbon steel G/ET is analyzed and it is revealed that the processing time of the steel ladle at each stage of the BOF – CCM technological route has a significant impact on the steel temperature mode. In accordance with this, the criteria for temperature control quality are formed. The results of computational experiment showed that the introduction of a control unit with a decision-maker contributes to the rational control of metal temperature mode in the BOF – CCM site, and as a result, obtaining a given chemical composition and temperature of steel within narrower limits. It allows one to eliminate deviations from the contact schedule of the main units, and to increase the number of melts in the series and the rate of continuous casting.

Coordinated Control and Energy Management System of Microgrid Group

As an important part of the smart grid, the operation control of the microgrid group is the key technology to maintain its safe and stable operation. The reasonable distribution of microgrid energy is closely related to the stability of system control. Multiple micro-grids in a certain area are uniformly regulated and controlled to form a micro-grid group operation mode. Aiming at the operation and management requirements of the multi-energy complementary microgrid group, the article proposes a design scheme for the energy management system of the microgrid group. The entire system has high operational reliability and economy, and has certain engineering value for improving the comprehensive benefits of the microgrid.

Optimization of group control strategy and analysis of energy saving in refrigeration plant

This study proposes a group control system optimization strategy coupled with a refrigeration plant model for a primary pump variable flow system, in order to improve the automation level of the refrigeration plant and maximize the energy saving potential. First, the control variables, optimization objectives, and operational constraints of the entire system were analyzed. Then, by collecting the operational data for each component and combining these data with theoretical analysis, the sub models were designed and the input parameters, output parameters, and optimization variables of each sub-model were defined. Next, the sub-models were coupled and the control variables of the operational combination, leading to the lowest overall system energy consumption, were obtained using a particle swarm optimization algorithm. Finally, considering a medical building in North China as an example, the application effectiveness of the optimal control strategy of the refrigeration plant was analyzed. The results showed that the energy-savings of the group control system after the optimization of the refrigerator, chilled water pump, cooling water pump, and cooling tower could reach 9.42, 8.04, 5.67, and 14.64%, respectively. This is a remarkable energy-savings benefit. The research described in this study also provides some reference for the design of group control systems in refrigeration plants.

Double deck elevator group control systems using evolutionary algorithms: Interfloor and lunchpeak traffic analysis

The continuous development of high-rise buildings around the world requires the installation of efficient elevator systems able to vertically transport the different passengers along the buildings in their daily journeys. Double deck elevators can increase the efficiency of these vertical transportation systems. Double deck elevators consist of two adjacent cabins that are joined and travel together along the same shaft, so the handling capacity of the system can be improved by allowing the dispatch of passengers with destination to two consecutive floors at the same instant. This type of architecture emerges as especially appropriate for uppeak traffic conditions. However, its suitability has not been sufficiently analysed for non-dominant (up or down) traffic patterns, such as interfloor and lunchpeak traffic. Our paper deals with conventionally controlled double deck elevators, where the Elevator Group Control System (EGCS) requires specific car-landing call allocation algorithms able to manage such special car architectures. Along this line, we propose a genetic algorithm that demonstrated a good performance when compared to a tabu search algorithm that was used as benchmark for comparison, taking into account different fitness evaluation functions (overall dispatching time and nearest call). The analysis was undertaken for interfloor and lunchpeak traffics and the average waiting, transit and journey times, and the energy consumption are reported as performance indexes of the vertical transportation system. The algorithms produced efficient results outperforming the considered benchmark and emerged as very competitive algorithms considering all the performance indexes as a whole. Results were tested using ELEVATE, the standard simulation software for vertical transportation.

МОДЕЛИ И МЕТОДЫ ПРОЕКТИРОВАНИЯ ДИНАМИЧЕСКИ РЕКОНФИГУРИРУЕМОЙ СИСТЕМЫ ГРУППОВОГО УПРАВЛЕНИЯ МОБИЛЬНЫМИ РОБОТАМИ

High adaptability is an important requirement for the control system over a group of mobile robots operating in a nondeterministic changing environment. The group control system must ensure that the group task is completed when the structure of the group or the environment changes. Such adaptability can be achieved through dynamic reconfiguration of the control system. The article discusses the mathematical models of a dynamically reconfigurable system from the standpoint of computer-aided design. A review of mathematical models of variable structure system, reconfigurable control systems and their design methods is presented. The paper deals with set-theoretic, analytical, discrete-event models of variable structure systems and methodologies of designing reconfigurable systems. It is shown that the existing design methods do not fully provide the required adaptability of designed group control system. The paper compares the group control system and the reconfigurable multiprocessor computing system and shows how to increase adaptability and autonomy of designed control system using principles of reconfigurable computing systems designing.

Optimal Elevator Group Control via Deep Asynchronous Actor-Critic Learning.

In this article, a new deep reinforcement learning (RL) method, called asynchronous advantage actor-critic (A3C) method, is developed to solve the optimal control problem of elevator group control systems (EGCSs). The main contribution of this article is that the optimal control law of EGCSs is designed via a new deep RL method, such that the elevator system sends passengers to the desired destination floors as soon as possible. Deep convolutional and recurrent neural networks, which can update themselves during applications, are designed to dispatch elevators. Then, the structure of the A3C method is developed, and the training phase for the learning optimal law is discussed. Finally, simulation results illustrate that the developed method effectively reduces the average waiting time in a complex building environment. Comparisons with traditional algorithms further verify the effectiveness of the developed method.