Resilient motion planning and control,without prior knowledge of disturbances,are crucial to ensure the safe and robust flight of *** development of a motion planning and control architecture for quadrotors,considerin...
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Resilient motion planning and control,without prior knowledge of disturbances,are crucial to ensure the safe and robust flight of *** development of a motion planning and control architecture for quadrotors,considering both internal and external disturbances(i.e.,motor damages and suspended payloads),is ***,the authors introduce the use of exponential functions to formulate trajectory *** choice is driven by its ability to predict thrust responses with minimal computational ***,a reachability analysis is incorporated for error dynamics resulting from multiple *** analysis sits at the interface between the planner and controller,contributing to the generation of more robust and safe spatial–temporal ***,the authors employ a cascade controller,with the assistance of internal and external loop observers,to further enhance resilience and compensate the *** authors’benchmark experiments demonstrate the effectiveness of the proposed strategy in enhancing flight safety,particularly when confronted with motor damages and payload disturbances.
The stability of complex systems is profoundly affected by underlying structures, which are often modeled as networks where nodes indicate system components and edges indicate pairwise interactions between nodes. Howe...
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The stability of complex systems is profoundly affected by underlying structures, which are often modeled as networks where nodes indicate system components and edges indicate pairwise interactions between nodes. However, such networks cannot encode the overall complexity of networked systems with higher-order interactions among more than two nodes. Set structures provide a natural description of pairwise and higher-order interactions where nodes are grouped into multiple sets based on their shared traits. Here we derive the stability criteria for networked systems with higher-order interactions by employing set structures. In particular, we provide a simple rule showing that the higher-order interactions play a double-sided role in community stability—networked systems with set structures are stabilized if the expected number of common sets for any two nodes is less than one. Moreover, although previous knowledge suggests that more interactions(i.e. complexity) destabilize networked systems, we report that,with higher-order interactions, networked systems can be stabilized by forming more local sets. Our findings are robust with respect to degree heterogeneous structures, diverse equilibrium states and interaction types.
Highly intelligent Unmanned Combat Aerial Vehicle(UCAV)formation is expected to bring out strengths in Beyond-Visual-Range(BVR)air *** Multi-Agent Reinforcement Learning(MARL)shows outstanding performance in cooperati...
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Highly intelligent Unmanned Combat Aerial Vehicle(UCAV)formation is expected to bring out strengths in Beyond-Visual-Range(BVR)air *** Multi-Agent Reinforcement Learning(MARL)shows outstanding performance in cooperative decision-making,it is challenging for existing MARL algorithms to quickly converge to an optimal strategy for UCAV formation in BVR air combat where confrontation is complicated and reward is extremely sparse and *** to solve this problem,this paper proposes an Advantage Highlight Multi-Agent Proximal Policy Optimization(AHMAPPO)***,at every step,the AHMAPPO records the degree to which the best formation exceeds the average of formations in parallel environments and carries out additional advantage sampling according to ***,the sampling result is introduced into the updating process of the actor network to improve its optimization ***,the simulation results reveal that compared with some state-of-the-art MARL algorithms,the AHMAPPO can obtain a more excellent strategy utilizing fewer sample episodes in the UCAV formation BVR air combat simulation environment built in this paper,which can reflect the critical features of BVR air *** AHMAPPO can significantly increase the convergence efficiency of the strategy for UCAV formation in BVR air combat,with a maximum increase of 81.5%relative to other algorithms.
The increasing prevalence of drones has raised significant concerns regarding their potential for misuse in activities such as smuggling, terrorism, and unauthorized access to restricted airspace. Consequently, the de...
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Coalition formation(CF) refers to reasonably organizing robots and/or humans to form coalitions that can satisfy mission requirements, attracting more and more attention in many fields such as multirobot collaboration...
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Coalition formation(CF) refers to reasonably organizing robots and/or humans to form coalitions that can satisfy mission requirements, attracting more and more attention in many fields such as multirobot collaboration and human-robot collaboration. However, the analysis on CF problems remains *** provide a valuable study reference for researchers interested in CF, this paper proposed a capabilitycentric analysis of the CF problem. The key problem elements of CF are firstly extracted by referencing the concepts of the 5W1H method. That is, objects(who) form coalitions(what) to accomplish missions(why) by aggregating capabilities(how) in a specific environment(where-when). Then, a multi-view analysis of these elements and their correlation in terms of capabilities is proposed through various logic diagrams, structure charts, etc. Finally, to facilitate a deeper understanding of capability-centric CF, a general mathematical model is constructed, demonstrating how the different concepts discussed in this analysis contribute to the overall model.
Vertex cover of complex networks is essentially a major combinatorial optimization problem in network science, which has wide application potentials in engineering. To optimally cover the vertices of complex networks,...
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Vertex cover of complex networks is essentially a major combinatorial optimization problem in network science, which has wide application potentials in engineering. To optimally cover the vertices of complex networks, this paper employs a potential game for the vertex cover problem, designs a novel cost function for network vertices, and proves that the solutions to the minimum value of the potential function are the minimum vertex covering(MVC) states of a general complex network. To achieve the optimal(minimum) covering states, we propose a novel distributed time-variant binary log-linear learning algorithm,and prove that the MVC state of a general complex network is attained under the proposed optimization algorithm. Furthermore, we estimate the upper bound of the convergence rate of the proposed algorithm,and show its effectiveness and superiority using numerical examples with representative complex networks and optimization algorithms.
This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking pe...
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This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking performance while satisfying the state and input constraints, even when system matrices are not available. We first establish a sufficient condition necessary for the existence of a solution pair to the regulator equation and propose a data-based approach to obtain the feedforward and feedback control gains for state feedback control using linear programming. Furthermore, we design a refined Luenberger observer to accurately estimate the system state, while keeping the estimation error within a predefined set. By combining output regulation theory, we develop an output feedback control strategy. The stability of the closed-loop system is rigorously proved to be asymptotically stable by further leveraging the concept of λ-contractive sets.
Conventional model predictive current control of permanent magnet synchronous machines (PMSMs) relies heavily on a precise mathematical model, which may be challenging to obtain in certain cases. To address this issue...
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This paper is devoted to event-triggered synchronization of delayed memristive neural networks with H∞and passivity *** aim is to guarantee the exponential synchronization and mixed H∞and passivity control for memri...
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This paper is devoted to event-triggered synchronization of delayed memristive neural networks with H∞and passivity *** aim is to guarantee the exponential synchronization and mixed H∞and passivity control for memristive neural networks by using event-triggered ***,a switching system is constructed under the event-triggered control ***,by adopting a piece-wise Lyapunov functional,a sufficient condition is established for the exponential synchronization and mixed H_(∞)and passivity ***,an event-triggered controller design scheme is proposed using matrix decoupling ***,the effectiveness of the designed controller is exemplified by a numerical example.
In this study, our main objective is to address the issue of sampled-data-based synchronization of complex networks subjected to stochastic scaling attacks using a looped-functional approach. To begin with, the design...
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