In this study, the Pareto optimal strategy problem was investigated for multi-player mean-field stochastic systems governed by Itô differential equations using the reinforcement learning (RL) method. ...A partially model-free solution for Pareto-optimal control was derived. First, by applying the convexity of cost functions, the Pareto optimal control problem was solved using a weighted-sum optimal control problem. Subsequently, using on-policy RL, we present a novel policy iteration (PI) algorithm based on the
ℌ
-representation technique. In particular, by alternating between the policy evaluation and policy update steps, the Pareto optimal control policy is obtained when no further improvement occurs in system performance, which eliminates directly solving complicated cross-coupled generalized algebraic Riccati equations (GAREs). Practical numerical examples are presented to demonstrate the effectiveness of the proposed algorithm.
The Pareto-based guaranteed cost control (GCC) of discrete-time uncertain stochastic systems in the infinite horizon is studied. Firstly, the convexity of the weighted sum objective function is ...proved. Based on this convexity, we demonstrate the relationship between Pareto optimality and minimization of the weighted sum objective function. This relationship confirms that the Pareto-based GCC strategy can be obtained by optimizing the weighted sum objective function. Secondly, to address the Pareto-based GCC issue for discrete-time uncertain stochastic systems, we derive the generalized algebraic Riccati inequality (GARI) specific to these systems. Thirdly, the necessary condition for a Pareto-based guaranteed cost controller is derived using the Karush–Kuhn–Tucker (KKT) condition. Lastly, we introduce a method based on linear matrix inequality (LMI) to determine the Pareto-based GCC strategy. This method reduces computational complexity and establishes the sufficient conditions for a Pareto-based guaranteed cost controller. To validate our findings, we provide a example implemented in MATLAB, confirming the accuracy of the proposed approach.
Energy consumption in transportation industry is increasing. Transportation has become one of the fastest energy consumption industries. Transportation energy consumption variation and the main ...influencing factors of decomposition contribute to reduce transportation energy consumption and realize the sustainable development of transportation industry. This paper puts forwards an improved decomposition model according to the factors of change direction on the basis of the existing index decomposition methods. Transportation energy consumption influencing factors are quantitatively decomposed according to the transportation energy consumption decomposition model. The contribution of transportation turnover, transportation structure and transportation energy consumption intensity changes to transportation energy consumption variation is quantitatively calculated. Results show that there exists great energy-conservation potential about transportation structure adjustment, and transportation energy intensity is the main factor of energy conservation. The research achievements enrich the relevant theory of transportation energy consumption, and help to make the transportation energy development planning and carry out related policies.
The novel coronavirus outbreak has significantly heightened environmental costs and operational challenges for civil aviation airlines, prompting emergency airport closures in affected regions and a ...substantial decline in ridership. The consequential need to reassess, delay, or cancel flight itineraries has led to disruptions at airports, amplifying the risk of disease transmission. In response, this paper proposes a spatial approach to efficiently address pandemic spread in the civil aviation network. The methodology prioritizes the use of a static gravity model for calculating route-specific infection pressures, enabling strategic flight rescheduling to control infection levels at airports (nodes) and among airlines (edges). Temporally, this study considers intervals between takeoffs and landings to minimize crowd gatherings, mitigating the novel coronavirus transmission rate. By constructing a discrete space–time network for irregular flights, this research generates a viable set of routes for aircraft operating in special circumstances, minimizing both route-specific infection pressures and operational costs for airlines. Remarkably, the introduced method demonstrates substantial savings, reaching almost 53.4%, compared to traditional plans. This showcases its efficacy in optimizing responses to pandemic-induced disruptions within the civil aviation network, offering a comprehensive solution that balances operational efficiency and public health considerations in the face of unprecedented challenges.
Rail ineffective transport refers to meaningless and unvalued transportation in the rail freight system, which including convective transportation, roundabout transportation, and unprocessed ...transportation. Ineffective transport does not only result in a strain on transportation capacity and increase costs but also imposes significant external pollution on low-carbon development. This paper attempts to study ineffective transport among rail stations. Base on defining the concept of ineffective transportation, it analyzes the ineffective transport volume and ineffective transport propagation on each station, which helps to seek more efficiently the core ineffective transport stations in the rail freight network. To better understand the mechanism of ineffective transport propagation effect at the rail freight transport system-level, an ineffective transport causality network (ITCN) was built based on the Granger causality test. Through the topology model of ineffective transport at 50 stations in China from 2013 to 2016, the results show that the ineffective transport of each station affects approximately 12 stations and also affected by 12 stations on average. Large-sized stations are affected by more stations than downstream. Small-sized and medium-sized stations are opposed. There are four core stations in this ITCN, and optimization of the ineffective transport at these four stations will save nearly fifty million kilogram of transport volume per year.
This paper is concerned with the open-loop linear-quadratic (LQ) Stackelberg game of the mean-field stochastic systems in finite horizon. By means of two generalized differential Riccati equations, ...the follower first solves a mean-field stochastic LQ optimal control problem. Then, the leader turns to solve an optimization problem for a linear mean-field forward-backward stochastic differential equation. By introducing new state and costate variables, we present a sufficient condition for the existence and uniqueness of the Stackelberg strategy in terms of the solvability of some Riccati equations and a convexity condition. Furthermore, it is shown that the open-loop Stackelberg equilibrium admits a feedback representation involving the new state and its mean. Finally, two examples are given to show the effectiveness of the proposed results.
This paper is concerned with the stochastic H∞ state feedback control problem for a class of discrete-time singular systems with state and disturbance dependent noise. Two stochastic bounded real ...lemmas (SBRLs) are proposed via strict linear matrix inequalities (LMIs). Based on the obtained SBRLs, a state feedback H∞ controller is presented, which not only guarantees the resulting closed-loop system to be mean square admissible but also satisfies a prescribed H∞ performance level. A numerical example is finally given to illustrate the effectiveness of the proposed theoretical results.
This paper mainly investigates the stochastic finite-time annular domain stability and asynchronous H ∞ control for nonlinear stochastic switching Markov jump systems. Firstly, the criterion of ...stochastic finite-time annular domain stability of the system is given by the modedependent average dwell time method, and two results, which consider particular cases with no switching signal and no Markov jump, are obtained. Secondly, when there are asynchronous phenomena in both deterministic switching and Markov jump, the asynchronous H ∞ controller is given to make the nonlinear switching Markov jump system finite-time annular domain bounded with a prescribed disturbance attenuation level. In addition, two degradation results with no deterministic asynchrony and no stochastic asynchrony are derived, respectively. As a special case, the corresponding result of linear switching Markov jump systems is obtained. Finally, an example is given to illustrate the results.
This article considers event-triggered security consensus of continuous-time multi-agent systems (MASs) subject to complex cooperative attacks. First, false data injection attacks (FDIAs) and ...denial-of-service (DoS) attacks are modeled under a unified framework through the Bernoulli process, which provides a comprehensive understanding of the challenges posed by these attacks in MASs. Second, an improved event-triggered mechanism (ETM) based on aperiodic sampling is constructed, which avoids the Zeno phenomenon and has the potential to improve the efficiency and resilience of MASs. In addition, it can be combined with detection signals to address the challenge of determining trigger times under DoS attacks, which is an important contribution. Third, to overcome the impact of complex cooperative attacks, this article analyses the security consensus of MASs in three steps by combining the characteristics of trigger time and attack time. Based on this, security consensus is guaranteed. Finally, a practical example is used to demonstrate the feasibility of the proposed ETM.
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