This paper studies the connectivity-maintaining consensus of multi-agent *** the impact of the sensing ranges of agents for connectivity and communication energy consumption,a novel communication management strategy i...
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This paper studies the connectivity-maintaining consensus of multi-agent *** the impact of the sensing ranges of agents for connectivity and communication energy consumption,a novel communication management strategy is proposed for multi-agent systems so that the connectivity of the system can be maintained and the communication energy can be *** this paper,communication management means a strategy about how the sensing ranges of agents are adjusted in the process of reaching *** proposed communication management in this paper is not coupled with controller but only imposes a constraint for controller,so there is more freedom to develop an appropriate control strategy for achieving *** the multi-agent systems with this novel communication management,a predictive control based strategy is developed for achieving *** results indicate the effectiveness and advantages of our scheme.
This paper studies a robust fault compensation and vibration suppression problem of flexible hypersonic *** controlled plant is represented by a cascade system composed of a nonlinear Ordinary Differential Equation(OD...
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This paper studies a robust fault compensation and vibration suppression problem of flexible hypersonic *** controlled plant is represented by a cascade system composed of a nonlinear Ordinary Differential Equation(ODE)and an Euler-Bernoulli Beam Equation(EBBE),in which the vibration dynamics is coupled with the rigid dynamics and suffers from distributed faults.A state differential transformation is introduced to transfer distributed faults to an EBBE boundary and a longitudinal dynamics is refined by utilizing T-S fuzzy IF-THEN rules.A novel T-S fuzzy based fault-tolerant control algorithm is developed and related stability conditions are *** robust exponential stability and well-posedness are proved by using the modified l_(0)-semigroup based Lyapunov direct approach.A simulation study on the longitudinal dynamics of flexible hypersonic vehicles effectively verifies the validity of the developed theoretical results.
To address the periodic disturbances introduced by the manipulator mounted on the drone, as well as the overall system parameter variations caused by the robotic arm carried by the Unmanned Aerial Vehicle (UAV), the f...
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In the domain of systems control, the robust control of fractional-order systems (FOSs) with ellipsoidal parameter uncertainty has received scant attention, even though ellipsoidal uncertainty is significant for quant...
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Real-time six degrees-of-freedom(6D)object pose estimation is essential for many real-world applications, such as robotic grasping and augmented reality. To achieve an accurate object pose estimation from RGB images i...
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Real-time six degrees-of-freedom(6D)object pose estimation is essential for many real-world applications, such as robotic grasping and augmented reality. To achieve an accurate object pose estimation from RGB images in real-time, we propose an effective and lightweight model, namely high-resolution 6D pose estimation network(HRPose). We adopt the efficient and small HRNetV2-W18 as a feature extractor to reduce computational burdens while generating accurate 6D poses. With only 33% of the model size and lower computational costs, our HRPose achieves comparable performance compared with state-of-the-art models. Moreover, by transferring knowledge from a large model to our proposed HRPose through output and feature-similarity distillations, the performance of our HRPose is improved in effectiveness and efficiency. Numerical experiments on the widely-used benchmark LINEMOD demonstrate the superiority of our proposed HRPose against state-of-the-art methods.
High-level task planning under adversarial environments is one of the central problems in the development of autonomous systems such as unmanned ground vehicles (UGV). Existing works commonly assume that the decision-...
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High-level task planning under adversarial environments is one of the central problems in the development of autonomous systems such as unmanned ground vehicles (UGV). Existing works commonly assume that the decision-maker such as UAV shares the same information with the environment. However, in many scenarios, the UGV, as an integral part of the system, generally has more information than the external adversary. For such a scenario, the decision-maker with more information may achieve better performance by using deceptive strategies. In this paper, we investigate the problem of optimal deceptive strategy synthesis for autonomous systems under asymmetric information between the internal decision-maker and the external adversary. Specifically, we model the dynamic system as a weighted two-player graph game and the objective is to optimize the mean payoff value per task. To capture the asymmetric information between two parties, we assume that the UGV has complete knowledge of the system, whereas the adversary may have misconceptions regarding the task as well as the cost. To synthesize an optimal deceptive strategy, we propose a synthesis algorithm based on hyper-games. The correctness as well as the complexity of the algorithm are analyzed. We illustrate the proposed algorithm by running examples as well as a simulation case study. Finally, we conduct an empirical experiment using real-world scenarios to verify the practical applicability of our algorithm. IEEE
Aiming at the problems of low accuracy and poor robustness that existed in the current hot rolling strip width spread model,an improved strip spread prediction model based on a material forming mechanism and Bayesian ...
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Aiming at the problems of low accuracy and poor robustness that existed in the current hot rolling strip width spread model,an improved strip spread prediction model based on a material forming mechanism and Bayesian optimized adaptive differential evolution algorithm(BADE)was *** first,we improved the original spread mechanism model by adding the weight and bias term to enhance the model robustness based on rolling ***,the BADE algorithm was proposed to optimize the improved spread mechanism *** optimization algorithm is based on a novel adaptive differential evolution algorithm,which can effectively achieve the global optimal ***,the prediction performances of five machine learning algorithms were compared in *** results show that the prediction accuracy of the improved spread model is obviously better than that of the machine learning algorithms,which proves the effectiveness of the proposed method.
Preserving the topology from being inferred by external adversaries has become a paramount security issue for network systems (NSs), and adding random noises to the nodal states provides a promising way. Nevertheless,...
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The H-type two-axis linear plane motion platform with dual gantry drive is one of the most crucial components in contour processing equipment. The dual gantry structure employs independently controlled dual-side motor...
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We develop a safe, efficient, and flexible motion control framework for collision-free navigation of robots in uncertain and dynamic environments. With the gathered real-time data at each control time, the motion dist...
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