Cooperative Problem Research Articles

The Soviet-Japanese Basic Convention of 1925 (the Historic and Legal Aspect)

The paper is devoted to the problem of international cooperation between the Soviet Union and Japan in the 1920s. The author uses the method of historical and legal analysis to examine the convention on the basic principles of relations between the USSR and Japan that was adopted in 1925. The Convention was one of the first significant historical instruments of the 20th century within the framework of the development of the Russian Far Eastern political program. The author examines the document from a historical and legal point of view in order to reveal its legal component and deepen the understanding of its diplomatic significance and influence on the development of international relations in the first half of the 20th century. The paper presents historical resources on the key reasons and conditions that led to the signing of the Convention. Particular attention is paid to the analysis of the Convention’s content, basic principles of cooperation enshrined in the Convention that laid the foundations for the regulation of relations between the Soviet Union and Japan. The author also examines the consequences the Convention had for both countries both in the short and long run. The study presents the author’s standing regarding the contradictory nature of the Convention of 1925 expressed in realization by the parties of their foreign policy objectives through unequal concessions, particularly for the USSR. The contribution of the Convention to the formation of political and economic relations between Japan and the USSR in the following years is emphasized.

GRRIS: A Real-time Intrasite Observation Scheduling Scheme for Distributed Survey Telescope Arrays

The distributed telescope array offers promise for conducting large-sky-area, high-frequency time-domain surveys. Multiple telescopes can be deployed at each observation site, so intrasite observation task scheduling is crucial for enhancing observation efficiency and quality. Efficient use of observable time and rapid response to special situations are critical to maximize scientific discovery in time-domain surveys. Besides, the competing scientific priorities, time-varying observation conditions, and capabilities of observation equipment, lead to a vast search space of the scheduling. So with the increasing number of telescopes and observation fields, balancing computational time with solution quality in observation scheduling poses a significant challenge. Informed by the seminal contributions of earlier studies on a multilevel scheduling model and global scheduler for a time-domain telescope array, this study is devoted to further exploring the site scheduler. Formulating the observation scheduling of multiple telescopes at the site as a cooperative decision-making problem, this paper proposes GRRIS, a real-time intrasite observation scheduling scheme for the telescope array using graph and reinforcement learning (RL). It employs a graph neural network to learn node features that can embed the spatial structure of the observation scheduling. An algorithm based on multi-agent RL is designed to efficiently learn the optimum allocation policy of telescope agents to field nodes. Through numerical simulations with real-world scenarios, GRRIS can achieve up to a 22% solution improvement over the most competitive scheme. It offers better scalability and subsecond decision speed, meeting the needs of observation scheduling control for future distributed telescope arrays.

Welcoming Speech at The International Conference “Actual Problems of Russian-Mongolian Cooperation”

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Work in Progress: Inculcating Empathy through Team-Based Problem Solving in Cooperative Problem Based Learning Classroom

The role of empathy as a major part of emotional intelligence is widely explored in engineering education research. Despite numbers of research on empathy development during classroom, the link between empathy and cooperative problem-based learning classroom are not highlighted. Thus, in this paper, an attempt of first step in covering this gap emphasis on the qualitative data of their reflective thinking towards their difficulties in solving the problem and seeking help. The right meaning of empathy in the context of team working is developed and conceptual framework based on social learning theory for future qualitative method was presented.

Consensus methods with Nash and Kalai-Smorodinsky bargaining game for large-scale group decision-making

With the significant advancements in communication technology, group decision-making (GDM) can now be implemented online, allowing a large number of decision-makers (DMs) to participate concurrently. However, current methods for large-scale group decision-making (LSGDM) are primarily suitable for 20 to 50 DMs, and their effectiveness in scenarios involving thousands or even tens of thousands of participants has yet to be fully validated. Furthermore, as the number of participants increases, the evaluation information becomes increasingly diverse and complex. At the same time, the social networks associated with the DMs typically become sparse, making information sharing and consensus building more challenging. In light of these challenges, we develop two new methods based on cooperative games to effectively address the challenges in super LSGDM. First, we propose a two-stage semi-supervised fuzzy C-means (FCM) clustering method with trust constraints, which aims to address the issue of sparsity in relationships within large-scale social networks. This method utilizes trust relationships as reliable resources and prior knowledge to guide and supervise the clustering process. On this basis, we discuss three scenarios from the perspective of cooperative games: (i) subgroup optimal consensus adjustments in non-cooperative situations, (ii) group optimal consensus adjustments in cooperative situations, and (iii) subgroup optimal consensus adjustments in cooperative situations. Subsequently, we view the consensus adjustment allocation as a cost cooperative game problem and propose two new LSGDM consensus methods based on Nash Bargaining (NB) and Kalai-Smorodinsky Bargaining (KSB). Finally, experiments on real datasets demonstrate the superiority and reliability of our proposed LSGDM methods.

Robust Cooperative Control for Heterogeneous Uncertain Nonlinear High-Order Fully Actuated Multiagent Systems.

This research is intended to address a robust cooperative control problem of heterogeneous uncertain nonlinear high-order fully actuated multiagent systems (HUN-HOFAMASs). A nonlinear HOFA system model is used to describe the multiagent systems (MASs) with heterogeneous uncertain nonlinear dynamics, which is called the HUN-HOFAMASs. A predictive terminal sliding-mode control-based robust cooperative control scheme is presented to address this problem. In this scheme, heterogeneous nonlinear dynamics of original system are offset to establish a linear constant HOFA system with the help of full actuation feature. Then, a terminal sliding-mode variable for enhancing the system robustness is introduced to handle the uncertainties. Furthermore, a linear incremental prediction model is developed in a HOFA form by means of a Diophantine equation. According to this model, the multistep terminal sliding-mode predictions are yielded to optimize the robust cooperative control performance and compensate for the network-induced communication constraints in the feedback and forward channels. Based on a linear matrix inequality (LMI) method, a necessary and sufficient criterion is derived to discuss the simultaneous consensus and stability of closed-loop HUN-HOFAMASs. The simulation and comparison results of cooperative flying around of multiple spacecraft system are shown to illustrate the capability and advantage of the presented predictive terminal sliding-mode control for robust cooperative control.

Советско-болгарское и российско-болгарское экономическое и социо-культурное взаимодействие на территории Белгородской области в последней трети XX в.

This article deals with the problem of economic and socio-cultural cooperation between the Belgorod region and Bulgaria. Bulgarian builders and workers have made a significant contribution to the formation of the modern industrial structure of the region. We have analyzed five funds of the State Archive of Belgorod region on Soviet-Bulgarian and Russian-Bulgarian cooperation for the specified period. The relations between Belgorod region and Bulgaria in the 1960-s and 1980-s became an important part of the interstate interaction between Bulgaria and the USSR. They also developed in the 1990s already in the context of the changed political and economic situation in both countries.

Robust cooperative hub location optimization considering demand uncertainty and hub disruptions

Amidst the rise of economic globalization and increased commodity trading, the logistics industry is experiencing rapid growth, encountering intricate transportation demands and fierce market competition. Simultaneously, it faces challenges related to infrastructure development and resource allocation efficiency. To enhance the adaptability and robustness of transportation networks when facing uncertain demands and potential hub disruptions, this paper proposes a two-stage robust optimization model for cooperative hub location problem. The model utilizes a hybrid algorithm that effectively combines the global search capability of genetic algorithms with the step-by-step optimization efficiency of Benders decomposition. Case study analyses demonstrate that, irrespective of the uncertainty environment, the cooperative model maintains lower total costs. Particularly in the case of large demand fluctuations, its cost advantages over non-cooperative models become notably prominent, showcasing remarkable performance in meeting service demands and enhancing resource utilization. Additionally, in cooperative networks, hub disruptions have a more significant impact on hub location decisions, with penalty and supplementary costs further exacerbating this influence. These research findings offer crucial insights for management practices: in uncertain market environments, adopting cooperative and robust planning strategies is pivotal for mitigating operational risks. When making decisions on cooperative hub location selection, carriers should comprehensively consider the interaction between uncertainty and economic benefits to achieve an optimal balance between risk and cost, ensuring the sustained economic feasibility of cooperative ventures.

Safety-Preserving Lyapunov-Based Model Predictive Rendezvous Control for Heterogeneous Marine Vehicles Subject to External Disturbances.

This article investigates the cooperative rendezvous control problem for perturbed heterogeneous marine systems composed of an autonomous underwater vehicle (AUV) and an autonomous surface vehicle (ASV). A novel Lyapunov-based model predictive control (LMPC) framework is presented to accomplish safe and precise rendezvous under input limitations and external disturbances. First, by incorporating the prescribed performance control (PPC) technique into the LMPC framework, we transform the original ascending state of the AUV into a self-constrained state, which serves as the decision variable of the model predictive control (MPC) optimization problem. Then, PPC-aided auxiliary control laws based on disturbance observers (DOBs) are designed to establish a robust contractive constraint to provide stability margins. Combining the LMPC with the PPC technique makes the original state-constrained problem an equivalent state-constraint-free problem. By addressing the MPC problem for the equivalent unconstrained system, the proposed method preserves the rendezvous safety. With the robust contractive constraint, the proposed safety-preserving LMPC (SP-LMPC) controller can inherit robustness and stability from the robust auxiliary control laws. Furthermore, theoretical analyses are conducted to assess recursive feasibility and closed-loop stability. With comprehensive theoretical support, the proposed method provides a new framework to simultaneously address state constraints and disturbances for highly nonlinear marine systems. Finally, simulations and comparisons are conducted to demonstrate the effectiveness and advantages of the proposed algorithm.

Data-driven optimal cooperative tracking control for heterogeneous multi-agent systems

This paper presents a novel hierarchical control scheme for solving the data-driven optimal cooperative tracking control problem of heterogeneous multi-agent systems. Considering that followers cannot communicate with the leader, a prescribed-time fully distributed observer is devised to estimate the leader’s state for each follower. Then, the data-driven decentralized controller is designed to ensure that the follower’s output can track the leader’s one. Compared with the existing results, the advantages of the designed distributed observer are that the prescribed convergence time is completely predetermined by the designer, and the design of the observer gain is independent of the global topology information. Besides, the advantages of the designed decentralized controller are that neither the follower’s system model nor a known initial stabilizing control policy is required. Finally, simulation results exemplify the advantage of the proposed method.

Data-Driven-Based Cooperative Resilient Learning Method for Nonlinear MASs Under DoS Attacks.

In this article, we consider the cooperative tracking problem for a class of nonlinear multiagent systems (MASs) with unknown dynamics under denial-of-service (DoS) attacks. To solve such a problem, a hierarchical cooperative resilient learning method, which involves a distributed resilient observer and a decentralized learning controller, is introduced in this article. Due to the existence of communication layers in the hierarchical control architecture, it may lead to communication delays and DoS attacks. Motivated by this consideration, a resilient model-free adaptive control (MFAC) method is developed to withstand the influence of communication delays and DoS attacks. First, a virtual reference signal is designed for each agent to estimate the time-varying reference signal under DoS attacks. To facilitate the tracking of each agent, the virtual reference signal is discretized. Then, a decentralized MFAC algorithm is designed for each agent such that each agent can track the reference signal by only using the obtained local information. Finally, a simulation example is proposed to verify the effectiveness of the developed method.

Distributed adaptive cooperative optimal output regulation via integral reinforcement learning

This paper studies the optimal cooperative output regulation problem for unknown linear multi-agent systems by the integral reinforcement learning technique. Existing results on this problem were obtained by a non-fully distributed learning process. In contrast, we propose a distributed learning algorithm over the jointly connected switching communication networks. Moreover, by modifying the existing algorithm, we reduce the computational cost and weaken the solvability conditions. Two numerical examples are used to illustrate the effectiveness of our approach.

Continuous charging assignment algorithm for heterogeneous robot clusters based on E-CARGO

Forming robot clusters is a common means to improve work efficiency and ensure personnel safety in intelligent logistics warehousing. However, designing a reasonable charging sequence and time for each robot in the cluster poses a major challenge for management personnel. In actual scenarios, the working and charging processes of the robots must be considered simultaneously. This study also considers multiple constraints on robot battery levels and available workstations under different parameters to ensure each team operates normally at any given time. Additionally, to enhance cooperation efficiency and alleviate the workload of management personnel, the problem is transformed from conventional one-time assignments to adaptive continuous assignments within fixed time periods. Accordingly, the ACP-GRAC problem is proposed. Firstly, a group role assignment model with conflicts is introduced to resolve assignment conflicts caused by mismatches between job skills and position requirements. Then, energy information is shared among robots during the charging process, adding adaptive cooperation concepts to address continuous charging issues. Finally, the E-CARGO model is utilized to formalize the ACP-GRAC problem, and the HCCA algorithm is designed for its solution. Furthermore, a classification backtracking strategy is proposed to simultaneously address the constraint of continuous operation at any given time and the problem of constraint violations caused by the HCCA algorithm’s pursuit of maximum team performance. Moreover, since the classification backtracking strategy can compute the theoretical minimum remaining energy of the robot before departing for charging, it further optimizes the overall work efficiency. Additionally, this study addresses the issue of low cluster work efficiency due to the unreasonable initial planning sequence of the algorithm. In the experimental section, comparisons are made with recent excellent heuristic algorithms and deep reinforcement learning algorithms. Experimental results demonstrate that B-HCCA exhibits significant advantages in both solution efficiency and solution quality when addressing adaptive cooperation problems with group role assignment conflicts.

An improved variable neighborhood search algorithm embedded temporal and spatial synchronization for vehicle and drone cooperative routing problem with pre-reconnaissance

Drones are increasingly utilized for transportation reconnaissance due to their expansive field of view, cost-effectiveness, and agility. They can pre-reconnaissance the traveling routes of the ground vehicles carrying valuable goods to ensure the safety of vehicles and their goods. This rises a novel routing problem for the Vehicles and their Pre-Reconnaissance Drones, which is an integrated point and arc routing problem with temporal and spatial synchronization. A mixed integer linear programming model is developed to formulate the problem with complex synchronization constraints. The Variable Neighborhood Search algorithm integrated with the Temporal & Spatial synchronization-based Greedy search and simulated annealing strategy is designed to solve the model. A practical case based on real urban data from Beijing, China, and random instances with different sizes are tested and compared with the proposed algorithm. Computational results indicated that the proposed algorithm can solve the problem efficiently and outperform the simulated annealing algorithm and the greedy algorithm.

A learning-based memetic algorithm for a cooperative task allocation problem of multiple unmanned aerial vehicles in smart agriculture

Smart agriculture aligns with the principles of sustainable development, making it a crucial direction for the future agriculture. This study focuses on a cooperative plant protection task allocation problem (CPPTAP) of multiple unmanned aerial vehicles (UAVs) with a common deadline in smart agriculture. CPPTAP permits multiple UAVs to conduct pesticide spraying on the same field. The completion time for each task fluctuates due to the cooperation among UAVs. We present a mathematical model and learning-based memetic algorithm (L-MA) to maximize the total area of the fields to be sprayed. In the evolutionary stage, mutation and repair operators based on value information are applied to balance the exploration and exploitation, while a problem-specific local search strategy is designed to enhance exploitation capability. A knowledge-based UAV allocation method (KUAM) is employed to maximize UAV utilization efficiency and minimize conflicts. Throughout the search process, Q-learning is utilized to assist the aforementioned operators and make decisions on the number of cooperative UAVs on fields. The effectiveness of L-MA is validated by comparing it against other state-of-the-art algorithms. The results demonstrate that L-MA outperforms the compared algorithms at a considerable margin in a statistical sense.

Self‐organizing cooperative hunting for unmanned surface vehicles with constrained kinematics

SummaryThe article aims at solving a cooperative hunting problem for multiple unmanned surface vehicles (USVs) subject to constrained kinematics. In order to cooperatively trap the evader into the hunting domain, a velocity model with control variable for the pursuers is firstly proposed according to the Apollonius circle. Then, a flexible self‐organizing control strategy is developed, which enables the pursuers to approach the evader while forming an encirclement. The pursuers can dynamically adapt their strategies in real‐time by choosing the optimal control variable. Additionally, take into account the limitation imposed on the vessel's motion, the optimal control variable with constraint can be obtained by using the particle swarm optimization with log‐barrier method. The simulation results ultimately demonstrate the validity and superiority of the proposed cooperative hunting algorithm.

Attitude collaborative control strategy for space gravitational wave detection

This paper proposes a high-precision attitude cooperative control strategy for three spacecraft in space gravitational wave detection. Firstly, based on the layout of optical components on the spacecraft and factors such as external interference and model uncertainty, a relative attitude model based on the Euler angle is constructed for the characteristics of small angle motion in differential wavefront sensing mode. Secondly, aiming at the cooperative control problem of multi drag-free attitude control system after the laser link capture between two spacecraft is completed, an attitude control method based on consensus mode cooperative control strategy is proposed. It combines local neighbourhood error from star sensing and differential wavefront sensing with an adaptive controller for equilateral triangle configuration control. Additionally, a cooperative control strategy based on laser measurement information is established to improve control accuracy by avoiding star sensors with low accuracy. Simulation results indicate that the control accuracy of the consistency mode and its sub-mode laser measurement mode is superior to the master-slave and cyclic modes, achieving better than 5×10−6 rad. The laser measurement mode demonstrates the highest accuracy, with relative attitude control accuracy reaching 1.5×10−7 rad, meeting the requirements for space gravitational wave detection.

Neuroadaptive Cooperative Fault-Tolerant Control of Heterogeneous Multiagent Systems Based on Fully Actuated System Approaches.

The leader-following cooperative problem in heterogeneous multiagent systems (HMASs) with unmodeled dynamics and actuator faults is investigated in this article. The HMASs, which include unmanned ground vehicles and unmanned aerial vehicles, are first described using a fully actuated system model (FASM). The FASM, as opposed to the first-order state-space model, preserves the physical significance of original systems and makes it feasible to apply the control rule entirely. In order to approximate unknown system dynamics, novel neuroadaptive laws with few learning parameters are then suggested. To counteract the negative effects of actuator faults, the Nussbaum function and adaptive approach are utilized. In addition, a cooperative fault-tolerant protocol is suggested, wherein consensus errors are uniformly ultimately bounded. The lack of virtual control variables in the proposed protocol reduces its complexity. The theoretical results are then validated by numerical simulations.

Application of improved grey wolf model in collaborative trajectory optimization of unmanned aerial vehicle swarm

With the development of science and technology and economy, UAV is used more and more widely. However, the existing UAV trajectory planning methods have the limitations of high cost and low intelligence. In view of this, grey Wolf algorithm is being used to achieve collaborative trajectory optimization of UAV groups. However, it is found that the Grey Wolf optimization algorithm (GWO) has the problem of weak cooperation. In this study, based on the traditional GWO pheromone factor is introduced to improve it.. Aiming at the problem of unstable performance of swarm intelligence optimization algorithm under dynamic threat, deep reinforcement learning is used to optimize the model. An unmanned aerial vehicle swarm trajectory planning model was constructed based on the improved grey wolf algorithm. Through experimental analysis, the optimal fitness value of the improved grey wolf algorithm was lower than 0.43 of the grey wolf algorithm. Compared with other algorithms, the fitness value of this algorithm is significantly reduced and the stability is higher. In complex scenarios, the improved grey wolf algorithm had a trajectory length of 70.51 km and a planning time of 5.92 s, which was clearly superior to other algorithms. The path length planned by the research and design model was 58.476 km, which was significantly smaller than the other three models. The planning time was 5.33 s and the number of path extension points was 46. The indicator values of the Unmanned Aerial Vehicle swarm trajectory planning model designed by this research were all smaller than the other three models. By analyzing the results, the model can achieve low-cost trajectory optimization, providing more reasonable technical support for unmanned aerial vehicle mission execution.

Leader-Following Connectivity Preservation and Collision Avoidance Control for Multiple Spacecraft with Bounded Actuation

This paper investigates the distributed formation control of a group of leader-following spacecraft with bounded actuation and limited communication ranges. In particular, connectivity-preserving and collision-avoidance controllers are proposed for the leader with constant or time-varying velocity, respectively. The communication graph between the spacecraft is modeled via a distance-induced proximity graph. By designing a virtual proxy for each spacecraft, the spacecraft–proxy couplings address the actuator saturation constraints. The inter-proxy dynamics incorporated with a bounded artificial potential function fulfill the coordination of all proxies. In addition, the bounded potential function can simultaneously tackle connectivity preservation and collision avoidance problems. The distributed formation controllers are proposed for multiple spacecraft with constant or time-varying velocities relative to the leader. A sliding mode control approach and the proxies’ dynamics are used in the design of a distributed cooperative controller for spacecraft to address the cooperative problem between the followers and the leader. Numerical simulations confirm the effectiveness of the anti-saturation distributed connectivity preservation controller.