Non-cooperative Game Approach Research Articles

Adaptive Fault-Tolerant Fuzzy Containment Control for Networked Autonomous Surface Vehicles: A Noncooperative Game Approach

Adaptive Fault-Tolerant Fuzzy Containment Control for Networked Autonomous Surface Vehicles: A Noncooperative Game Approach

Optimal design of robust control for belt conveyor systems based on fuzzy dynamic model and Nash game

An optimized robust control method is proposed in this paper to deal with the disturbances of nonlinear factors such as uncertainty in belt conveyor and to realize the speed control of belt conveyor. By controlling the subsystems to track the same desired speed, belt breaks and overlaps caused by varying belt speeds from one section to another are limited. First, a fuzzy model is developed by applying fuzzy set theory to describe uncertainties of belt conveyor such as masses, stiffness, damping and frictions. Second, a robust control is designed for the nominal system and compensating for the uncertainty. With the robust control, the belt conveyor system is proved to be uniformly bounded and uniformly ultimately bounded by the Lyapunov approach. Third, the parameters of controller are optimized based on a non-cooperative game approach. The optimal solution is the Nash equilibrium. Optimal solution allows the controller system to perform better and decrease the cost of control. Finally, by comparing with LQR control and different parameters, the feasibility of robust control and the rationality of the optimal parameters are proven.

A non-cooperative game approach on isolated water-energy microgrids

The integration of renewable energies into water systems emerges as a crucial challenge and an opportunity within the water-energy nexus, particularly in isolated communities, emphasizing the importance of developing sustainable and self-sufficient isolated water-energy microgrids (IWEMGs). This study addresses a significant gap in existing research by proposing a comprehensive model that simultaneously considers operation and dispatch in IWEMGs. Our work introduces a framework based on game theory to model and optimize resource management in IWEMGs, where actors, including consumers, producers, or prosumers, engage in a non-cooperative game (Cournot competition), enabling an effective resource allocation mechanism for water and energy that considers strategic interactions among participants. The application of this model to a case study in a rural community, known as Ranchería, in La Guajira, Colombia, demonstrates the practicality and effectiveness of IWEMGs in meeting the water and energy demands of users. The model is implemented and solved using the GAMS software. Our quantitative results show optimal performance in resource management, highlighting the model’s ability to facilitate informed decision-making. Through a detailed analysis of water and energy transactions and the net benefits achieved, the study illustrates the potential of game theory to enhance the operational performance of IWEMGs. Furthermore, this research contributes to the broader discourse on sustainable resource management, offering insights that could guide the development of policies and strategies for implementing IWEMGs in similar contexts globally.

Design and Pricing of Maintenance Service Contract Based on Nash Non-Cooperative Game Approach

Design and Pricing of Maintenance Service Contract Based on Nash Non-Cooperative Game Approach

A Game Theoretic Approach to Computation Offloading Strategy Optimization for Non-cooperative Users in Mobile Edge Computing

Computation offloading from a user equipment (UE, also called mobile user, mobile subscriber, or mobile device) to a mobile edge cloud (MEC) provides an effective way to virtualize an ordinary smart mobile device (e.g., smartphone, tablet, handheld computer, wearable device, and personal digital assistant) into a formidable equipment, which is able to provide more and stronger functionalities than that of a laptop or a desktop computer. It is conceivable that there can be several MECs with different processing capabilities in a geographic area, and each MEC many serve many UEs with endless sequences of computation tasks, various application characteristics, and diversified communication requirements and bandwidths. Furthermore, the mobile users are competitive and selfish, which means that computation offloading strategy optimization needs to be carried out for each individual mobile user to optimize the performance of only his applications. In this paper, we conduct a mathematical study of computation offloading strategy optimization for non-cooperative users in mobile edge computing by using a game theoretic approach. The main contributions of this paper can be summarized as follows. We establish an M/G/1 queueing model to characterize multiple heterogeneous UEs and MECs, so that the average response time of all offloadable and non-offloadable tasks generated on a UE can be calculated analytically and the optimal computation offloading strategy of a UE can be defined rigorously. We construct a non-cooperative game framework for a mobile edge computing environment, in which each player (i.e., a UE) can selfishly minimize his payoff by choosing an appropriate strategy in his strategy space. We prove the existence of the Nash equilibrium of the above game. We develop algorithms to find the Nash equilibrium, including an algorithm to find the best response of a mobile user and an iterative algorithm to find the Nash equilibrium. We demonstrate numerical examples and data of our game, including numerical data for the Nash equilibrium and numerical data for the convergence of the Nash equilibrium. To the best of the author's knowledge, this is the first paper that effectively investigates computation offloading strategy optimization for multiple, heterogeneous, and competitive mobile users and multiple heterogeneous mobile edge clouds by using a non-cooperative game approach. Hence, the paper makes noticeable contributions towards the understanding of a competing mobile edge computing environment and its stabilization.

Robust adaptive fault-tolerant control for path maneuvering of autonomous surface vehicles with actuator faults based on the noncooperative game strategy

This paper investigates a parameterized path-guided following/maneuvering control problem of an autonomous surface vehicle (ASV) subject to actuator faults via a noncooperative game strategy. Unlike the existing path maneuvering control methods, the noncooperative game of this paper includes two sub-games. On one hand, the path update is considered to be against the effort of kinematic control. On the other hand, the kinetic control is considered to be against the total disturbances consisting of actuator faults and external disturbances. A robust adaptive fault-tolerant path maneuvering controller is designed based on a noncooperative game approach. Specifically, we design a kinematic control law using an improved dynamic surface control approach to achieve the geometric objective. An improved neural predictor is constructed, in which a concurrent learning-based adaptation is designed to identify unknown fault coefficients. A path update law and a kinetic control law are calculated to cover noncooperative games by using an adaptive dynamic programming approach. Theoretical analysis shows that the closed-loop system is input-to-state stable, and the proposed method can meet the geometric objective, the dynamic objective, and the fault-tolerant objective. Finally, simulation results demonstrate the efficacy of the proposed robust adaptive fault-tolerant control method for path maneuvering.

Toward Green and Efficient Blockchain for Energy Trading: A Noncooperative Game Approach

Toward Green and Efficient Blockchain for Energy Trading: A Noncooperative Game Approach

Participation of strategic district heating networks in electricity markets: An arbitrage mechanism and its equilibrium analysis

Combined electricity-heat operation improves social welfare compared with the traditional heat-driven operation. However, it is left open how to achieve such an improvement in the current electricity market. Therefore, an arbitrage mechanism enabling strategic district heating networks (DHNs) to participate in electricity markets is designed, which settles the payment concerning the power deviation and the locational marginal price (LMP). Individual interests of both energy sectors are addressed via a bi-level model, while analytical results of the arbitrage equilibrium are proved theoretically. Although the mechanism intends to allow DHNs to profit selfishly from utilizing energy storage, it is strikingly guaranteed that electric power networks also benefit from this. A Pareto improvement is thus achieved by the mechanism with enhanced social welfare efficiency at the arbitrage equilibrium. The proposed mechanism is numerically compared with the traditional heat-driven operation, combined operation and existing cooperative and non-cooperative game approaches, while the merits are verified in two integrated electricity and heat systems with different scales.

Noncooperative and Cooperative Urban Intelligent Systems: Joint Logistic and Charging Incentive Mechanisms

Autonomous vehicles (AVs) have become an emerging crucial component of the intelligent transportation system (ITS) in modern smart cities. In particular, coordinated operations of AVs can potentially enhance the quality of public services, e.g. logistic and AV charging services. However, the joint logistic and AV charging scenario involves the sophisticated interactions between a large number of complicated agents, dynamic logistic, and electricity prices in real-world systems. Since AVs are individuals owned by different parties, the design of attractive incentive to motivate them to provide multiple public services becomes a fundamental issue. In this paper, we develop an urban intelligent system (UIS) by exploiting the efficient incentive mechanisms, e.g. non-cooperative and cooperative game theoretic approaches, to motivate the AVs to provide logistic and charging services in UIS. For the non-cooperative game approach, we formulate the interaction between the selfish AVs and the aggregator as a Stackelberg game. Meanwhile, the aggregator, known as the leader in the game, aims to decide the logistic and electricity trading prices, and then the AVs, executed as the followers, determine their service schedules. Furthermore, considering that all the players are willing to cooperate, we develop a cooperative potential game for the selfless AVs to maximize the social welfare of the UIS. These case studies demonstrate the effectiveness and practicability of proposed incentive mechanisms that can motivate EVs to provide high quality logistic and charging services by maximizing their utilities. Also, both the proposed schemes provide significant system revenues than that of conventional system optimization-based approaches.

Deep Learning with Game Theory Assisted Vertical Handover Optimization in a Heterogeneous Network

Problem: In next-generation networks, users can optimize or tune their preferences with a seamless transfer of diverse access methodologies for maximizing the Quality of Service (QoS) and cost savings. In these heterogeneous wireless environments, users are prepared with several multimode wireless devices for maximizing media services through several access networks. Such networks may vary regarding energy usage, available bandwidth, technology, coverage range, monetary cost, etc. In recent days, vertical handover has attained higher performance owing to the improvements in mobility models through adopting the Fourth Generation (4G) technologies. On the other hand, these improvements are restricted to some cases, so, it does not offer support for generic mobility. Consequently, diverse strategies were implemented by considering these mobility models. However, it suffers from improper network selection, late and too-early handovers, repeated handovers, high packet loss, etc. Aim: This paper tackles the problem of vertical handover problem in the heterogeneous network using deep learning with game theory. Methods: The proposed model develops a non-cooperative game approach, in which all base stations compete selfishly to transmit at higher power. The overall performance in terms of throughput, handover, energy consumption, and load balancing is attained by optimizing the transmission power by the game theory. For performing this model, the required data like path loss, SINR, data rate, load, etc are generated by the deep learning called Recurrent Neural Network (RNN). Results: From the simulation findings, the handoff probability of the recommended RNN+Game Theory is correspondingly secured at 6.9%, 22.6%, and 8.2% superior to TOPSIS, ABC-PSO, and game theory when taking the time like 5 secs for user velocity as 30 km/h. Conclusion: Results show that the proposed game theoretical approach with deep learning provides a throughput enhancement while reducing the power consumption, in addition, to minimizing the unnecessary handover and balancing the load between base stations.

Robust game-theoretic optimization for energy management in community-based energy system

• A system scheme is proposed to optimize the ESS capacity and energy consumption for multi-community in an individual-oriented way considering the uncertainty of distributed generation. • A robust non-cooperative game approach is formulated for the proposed system scheme, oriented from the pursuit of the economical operation facing with the uncertainty. • To solve the optimization problem, the distributed algorithm is proposed by combining PSO and C&CG algorithm, which can converge to the robust Nash equilibrium. It is significant to schedule energy consumption in community-based energy system consisting of distributed generation, energy storage, multi-community. In this paper, a robust game-theoretic approach is proposed to optimize the storage capacity and energy consumption considering the uncertainty of distributed generation. Energy management with energy storage optimization is studied by considering the cost of distributed generations, cost of energy storage, and bidirectional energy trading. In order to search the robust solution for Nash equilibrium and optimal storage capacity, a distributed algorithm combining column and constraint generation and particle swarm optimization is designed. Simulation results show that the proposed approach has a well performance in reducing operation cost of energy system and tackling the uncertainty of distributed generation.

Capacity Allocation of Hybrid Power System with Hot Dry Rock Geothermal Energy, Thermal Storage, and PV Based on Game Approaches

This study utilizes hot dry rock (HDR) geothermal energy, which is not affected by climate, to address the capacity allocation of photovoltaic (PV) -storage hybrid power systems (HPSs) in frigid plateau regions. The study replaces the conventional electrochemical energy storage system with a stable HDR plant assisted by a flexible thermal storage (TS) plant. An HPS consisting of an HDR plant, a TS plant, and a PV plant is proposed. Game approaches are introduced to establish the game pattern model of the proposed HPS as the players. The annualized income of each player is used as the payoff function. Furthermore, non-cooperative game and cooperative game approaches for capacity allocation are proposed according to the interests of each player in the proposed HPS. Finally, the proposed model and approaches are validated by performing calculations for an HPS in the Gonghe Basin, Qinghai, China as a case study. The results show that in the proposed non-cooperative game approach, the players focus only on the individual payoff and neglect the overall system optimality. The proposed cooperative game approach for capacity allocation improves the flexibility of the HPS as well as the payoff of each game player. Thereby, the HPS can better satisfy the power fluctuation rate requirements of the grid and increase the equivalent firm capacity (EFC) of PV plants, which in turn indirectly guarantees the reliability of grid operation.

An Integrated Data Envelopment Analysis and Non-Cooperative Game Approach for Public Transportation Incentive Subsidy Allocation

As an important national initiative, China’s public transport priority development strategy is conducive to the development of the public transport industry and urban economic construction. However, current public transport operation is inefficient due to the information asymmetry between the government and public transport enterprises, thereby inevitably generating losses. To address the issue of information asymmetry, this study designs a public transport subsidy allocation method from the perspective of incentives and discusses the allocation results using this method through a case study. The rationality of the proposed method and the scientificity and authenticity of the conclusions, are fully explained and demonstrated via the experimental simulation. Based on real published data of public transport enterprises, the proposed method can motivate public transport enterprises to improve their performance. In the case of reporting false data, the proposed method can motivate public transport enterprises to report accurate data.

An Integer Non-Cooperative Game Approach for the Transactive Control of Thermal Appliances in Energy Communities

Non-cooperative scheduling games can be used to coordinate residential loads in order to achieve a common goal while accounting for individual consumer’s interests, privacy, and autonomy. However, a significant portion of the residential flexibility—Thermostatically Controlled Loads (TCLs) such as water and space heating/cooling appliances—has not been fully addressed under this game theoretic approach: their comfort constraints and integer control were not considered. This paper presents a method for properly including TCLs in this framework and discusses its application in energy communities. Specifically, we propose a general mathematical formulation for considering users’ comfort in non-cooperative games. We model the integer nature of the TCLs control with binary variables and show that optimal or close to optimal (less than 1%) solutions are reached. Moreover, different total cost functions can be used depending on the market context and the objective of the demand management program. To illustrate and discuss these aspects in practical applications, we used a case study of an energy community in Spain. The results show that the TC solutions are optimal or only 0.80% worse than optimal; different total cost functions result in different results (load curve smoothing or peak load reduction); consumers’ comfort is respected; and the proposed game model cooperates with consumers in order to minimize community’s costs.

A non-cooperative game approach to the robust control design for a class of fuzzy dynamical systems

Aiming at the time-varying bounded uncertainty in the mechanical system and the problem of excessive control gain caused by the “worst case”, a robust control with tunable parameters that can be used to compensate for the uncertainty is proposed. The proposed control can guarantee that the system has uniform boundedness and uniform ultimate boundedness. Besides, the fuzzy set theory is used to describe the uncertainty to avoid introducing any IF-THEN fuzzy logic rules or probability theory. Based on the non-cooperative game theory, the parameters of the proposed control are optimized. We treat mutually independent parameters to be optimized as players, then design performance indexes for each parameter as its cost function, and solve the optimal result by the definition of Nash equilibrium. Finally, taking the trajectory tracking control of omnidirectional mobile platform as an example, the feasibility of the optimization method and the effectiveness of the control are verified by comparing with the other two control methods.

Non-cooperative game based congestion control for data rate optimization in vehicular ad hoc networks

The growth of connected vehicles in smart cities increases the number of information being communicated on the Internet of Vehicle networks. This causes wireless channel congestion problems, which degrades the network performance and reliability due to the low throughput, high average delay and the high packets loss. Therefore, this paper proposes a non-cooperative game approach to control congestion in the vehicular ad-hoc network channel where the nodes behave as selfish players requesting high data transmission rates. Moreover, the satisfaction of the Nash equilibrium condition for the optimum data transmission rate for each vehicle, is proven. A utility function is introduced based on data transmission rates, the priority of vehicles and contention delay in order to obtain the optimal rates. The performance of the proposed approach has been evaluated and validated in comparison with three others approaches over two testing scenarios for highway and urban traffic. The results show that the network performance and efficiency have been improved by an overall average of 35%, 30% and 37.17% in terms of packets loss, channel busy time and number of collision messages, respectively, as compared with the state-of-the-art-strategies for the highway testing scenario. Similar performance is achieved for the urban testing scenario.

Distributed Energy Efficiency Optimization for Multi-User Cognitive Radio Networks Over MIMO Interference Channels: A Non-Cooperative Game Approach

Energy efficiency (EE) optimization is investigated for a multi-user cognitive radio network (CRN) over multiple-input-multiple-output (MIMO) interference channels (ICs). To reduce the system overhead due to information exchange among the secondary CR uses (SUs), the EE optimization problem is formulated as a non-cooperative game, where each SU transmitter competes against the other SU pairs by optimizing its transmit covariance matrix. Specifically, each multi-antenna SU maximizes locally its energy efficiency in terms of the number of bits transmitted per unit energy consumption, subject to the per-SU transmit power constraint and the primary user (PU) perceived total interference constraint. It is proved that the formulated non-cooperative game admits at least one Nash equilibrium (NE), and the sufficient condition for a unique NE is derived subsequently. Primal decomposition is employed in the local EE optimization problem to relax the coupling PU perceived interference constraint such that fully distributed operation is allowed. A distributed iterative EE optimization algorithm (DIEEOA) is then proposed to obtain the unique NE, which is shown to converge to the global optimum. Linear precoding techniques are employed to mitigate the impacts of multi-user interference and imperfect channel state information (CSI). Through numerical simulations, effectiveness of the proposed scheme is validated and the system setting parameters’ impacts on the performance are studied.

Real-Time Optimal Scheduling of Large-Scale Electric Vehicles: A Dynamic Non-Cooperative Game Approach

In the presence of the increasing penetration of electric vehicles (EVs) and conflict of independent optimization objectives among each electric vehicle aggregator (EVA), real-time optimal scheduling (RTOS) of large-scale EVs based on dynamic non-cooperative game approach is proposed for optimal decision makings in a dynamic pricing market. First, real-time optimal scheduling framework is designed to describe the flow of energy and information. Then, equivalent model of large-scale EVs is formulated to address “curse of dimensionality” caused by a large number of decision variables. Then, the potential game theory is used to study the existence and uniqueness of the Nash equilibrium (NE) solution. Finally, a distributed approach based on alternating direction method of multipliers (ADMM) is designed to achieve the equilibrium. Case studies demonstrate that the proposed approach achieves peak load shifting and reduces cost of EVAs significantly. Furthermore, the proposed method obtains higher-quality solution compared with other methods and is more applicable for real-time optimal scheduling of large-scale EVs due to its high computation efficiency and privacy protection.

A Coalitional Graph Game Approach for Minimum Transmission Broadcast in IoT Networks

In this work, we consider the broadcast mechanisms for both reliable links and unreliable links in the Internet of Things (IoT) networks. Focusing on the minimum transmission broadcast (MTB) problem, we propose an efficient algorithm, termed as the Connected Dominating Set Algorithm based on Coalitional Graph Game (CDSA-CGG), to find the minimum connected dominating set (MCDS). Distinct from related work in the literature which adopts a non-cooperative game approach and assumes the interaction among nodes is limited to neighboring nodes, we formulate the interaction and cooperation among nodes over reliable links and over unreliable links as a connected forwarding graph based on coalitional graph game. We prove that the forwarding graph constructed by CDSA-CGG is a Nash network. In addition, we also prove that the final resulting graph is pairwise stable. The simulation results show that the proposed coalition graph game-based algorithm performs well in terms of the number of transmissions, convergence rate, and lifetime of nodes.

Container storage tariff policy analysis using combining game theory and system dynamics approach

This paper examines the storage tariff setting policy. The main purpose of this paper is to give consideration to optimal tariffs based on alternative strategies that reduce dwelling time. The main problem is the basic tariff for container storage in Container Yard is too cheap. This low-cost tariff of the storage container is used by customers so that containers are piled too long in Container Yard and cause dwelling time to be high. This has an impact on the congestion effect and the decline in utilities in the container terminal. This problem has complexity and suitability with System Dynamics. The linkage system between one cause and another cause will be modelled with System Dynamics so that the estimated output of container flows can be obtained. Furthermore, with the Non-Cooperative Game approach, a payoff matrix table will be obtained which contains the total profit obtained by the container terminal management and the total cost incurred by the customer. The main factors considered in the payoff matrix are the estimated container flows, the container storage tariff and the length of time accumulated in Container Yard. Finally, the optimal tariff from both sides was found (Nash Equilibrium), from the point of view of terminal container management and customer. This will help terminal management to make the right decision.