Distributed control for heterogeneous multi-agent systems (MAS) under senor faults and time-varying control coefficients with directed communication graphs is investigated. Firstly, a novel type of functions named dom...
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Most of the existing exact formation algorithms for swarm systems are fully label-specified,*** desired position for each individual in the formation is predetermined by its label,which would inevitably make the forma...
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Most of the existing exact formation algorithms for swarm systems are fully label-specified,*** desired position for each individual in the formation is predetermined by its label,which would inevitably make the formation algorithms vulnerable to individual *** address this issue,in this paper,we propose a dynamic leader-follower approach to solving the line marching problem for a swarm of planar kinematic *** contrast to the existing results,the desired positions for the robots in the line are no longer predetermined by the robots'labels,but the relativity of the robots'labels as well as their real-time relative *** constantly forming the leader-follower chain,exact line marching can be realized by pairwise leader-following *** the order of the leader-follower chain keeps updating,the proposed algorithm shows strong robustness against robot *** numerical results are provided to evaluate the performance of the proposed formation algorithm.
Robots are increasingly being deployed in densely populated environments, such as homes, hotels, and office buildings, where they rely on explicit instructions from humans to perform tasks. However, complex tasks ofte...
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Robots are increasingly being deployed in densely populated environments, such as homes, hotels, and office buildings, where they rely on explicit instructions from humans to perform tasks. However, complex tasks often require multiple instructions and prolonged monitoring, which can be time-consuming and demanding for users. Despite this, there is limited research on enabling robots to autonomously generate tasks based on real-life scenarios. Advanced intelligence necessitates robots to autonomously observe and analyze their environment and then generate tasks autonomously to fulfill human requirements without explicit commands. To address this gap, we propose the autonomous generation of navigation tasks using natural language dialogues. Specifically, a robot autonomously generates tasks by analyzing dialogues involving multiple persons in a real office environment to facilitate the completion of item transportation between various *** propose the leveraging of a large language model(LLM) through chain-of-thought prompting to generate a navigation sequence for a robot from dialogues. We also construct a benchmark dataset consisting of 625 multiperson dialogues using the generation capability of LLMs. Evaluation results and real-world experiments in an office building demonstrate the effectiveness of the proposed method.
In this paper, the control problem of outlet moisture content is investigated for the tobacco leaf loosening and moisture regain process. Unlike existing strategies that heavily depend on manual expertise for adjustin...
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According to the combustion spectra of hydrocarbons, three infrared flame sensors (RD-913FB1, RD-913FB2, RD-913FB3) based on pyroelectric effect and ultraviolet flame sensor (R2868) based on photoelectric effect are u...
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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 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.
The dynamic positioning of marine intelligent vehicles is gradually becoming the core technique to perform herculean tasks in complex sea conditions. To cope with the composite ocean disturbances and the constraint of...
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The dynamic positioning of marine intelligent vehicles is gradually becoming the core technique to perform herculean tasks in complex sea conditions. To cope with the composite ocean disturbances and the constraint of communication resources, this paper proposes an event-triggered anti-disturbance dynamic positioning control strategy with performance optimization via reinforcement learning. Firstly, to overcome the composite disturbances caused by external environmental disturbance and internal uncertainties, the disturbance estimator with a fuzzy logic system is established. By constructing an event-triggered mechanism that relies on position and heading information, an event-triggered optimized dynamic positioning controller is designed by combining modified backstepping and reinforcement learning methods. The designed controller consists of a composite anti-disturbance controller and an optimized compensator. Thus, unnecessary communication can be avoided and the performance index is minimized simultaneously. Furthermore, the errors of the closed-loop system are rigorously proved to be bounded. At last, the validity of the proposed control scheme for a marine intelligent vehicle is illustrated by simulation and comparison studies. IEEE
The development of small-diameter vascular grafts that can meet the long-term patency required for implementation in clinical practice presents a key challenge to the research *** techniques such as the braiding of sc...
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The development of small-diameter vascular grafts that can meet the long-term patency required for implementation in clinical practice presents a key challenge to the research *** techniques such as the braiding of scaffolds can offer a tunable platform for fabricating vascular grafts,the effects of braided silk fiber skeletons on the porosity,remodeling,and patency in vivo have not been thoroughly investigated.
This study considers the issue of nonfragile state estimation (SE) for memristor-based bidirectional associative memory (MBAM) neural networks with probabilistic time-varying delays. The primary objective is to develo...
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The existing Maximum Power Point Tracking(MPPT)method has low tracking efficiency and poor *** is easy to fall into the Local Maximum Power Point(LMPP)in Partial Shading Condition(PSC),resulting in the degradation of ...
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The existing Maximum Power Point Tracking(MPPT)method has low tracking efficiency and poor *** is easy to fall into the Local Maximum Power Point(LMPP)in Partial Shading Condition(PSC),resulting in the degradation of output power quality and *** was found that various bio-inspired MPPT based optimization algorithms employ different mechanisms,and their performance in tracking the Global Maximum Power Point(GMPP)***,a Cuckoo search algorithm(CSA)combined with the Incremental conductance Algorithm(INC)is proposed(CSA-INC)is put forward for the MPPT method of photovoltaic power *** method can improve the tracking speed by more than 52%compared with the traditional Cuckoo Search Algorithm(CSA),and the results of the study using this algorithm are compared with the popular Particle Swarm Optimization(PSO)and the Gravitational Search Algorithm(GSA).CSA-INC has an average tracking efficiency of 99.99%and an average tracking time of 0.19 s when tracking the GMPP,which improves PV power generation’s efficiency and power quality.
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