Although unmanned underwater vehicles (UUVs) exhibit significant advantages in coastal water applications (e.g. underwater grasping), dynamic and disturbed environments, along with the uncertainties in underwater posi...
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ISBN:
(纸本)9798350364200;9798350364194
Although unmanned underwater vehicles (UUVs) exhibit significant advantages in coastal water applications (e.g. underwater grasping), dynamic and disturbed environments, along with the uncertainties in underwater positioning and navigation pose challenges to dynamic interaction with underwater environments. To deal with these challenges, this paper explores the underwater robot-environment-interaction (REI) task by introducing a lightweight UUV named Sea-U-Dragon. Sea-U-Dragon employs an active-passive compliant control strategy, featuring a flexible end effector designed to passively adapt to underwater dynamic changes, along with an uncertainty disturbance estimator (UDE)-based dynamic motion/force controller to compensate for underwater uncertainties. Finally, the trajectory following experiments and dynamic force tracking demonstrations justify the maneuverability of Sea-U-Dragon and its ability to perform dynamic interaction tasks.
In real world, most of the phenomenons occur based on some physical, chemical and biological laws. The study of phenomena becomes much easier through the use of mathematical models with various approaches. In many con...
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Accurate channel estimation is crucial for the proper operation of reconfigurable intelligent surfaces (RIS). This paper introduces a convolutional neural network (CNN) approach for multi-user RIS channel estimation t...
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ISBN:
(纸本)9798350387452;9798350387445
Accurate channel estimation is crucial for the proper operation of reconfigurable intelligent surfaces (RIS). This paper introduces a convolutional neural network (CNN) approach for multi-user RIS channel estimation that incorporates the zero-shot noise-to-noise (N2N) methodology within its architecture. In contrast to techniques that rely on clean training data, the proposed method learns from the noisy data itself to figure out how to remove the noise. The proposed zero-shot N2N self-learning demonstrates improved performance and a fast convergence rate in the RIS channel estimation.
This paper addresses the problem of composite synchronization and learning control in a network of multi-agent robotic manipulator systems with heterogeneous nonlinear uncertainties under a leader-follower framework. ...
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ISBN:
(纸本)9798350355376;9798350355369
This paper addresses the problem of composite synchronization and learning control in a network of multi-agent robotic manipulator systems with heterogeneous nonlinear uncertainties under a leader-follower framework. A novel two-layer distributed adaptive learning control strategy is introduced, comprising a first-layer distributed cooperative estimator and a second-layer decentralized deterministic learning controller. The first layer is to facilitate each robotic agent's estimation of the leader's information. The second layer is responsible for both controlling individual robot agents to track desired reference trajectories and accurately identifying/learning their nonlinear uncertain dynamics. The proposed distributed learning control scheme represents an advancement in the existing literature due to its ability to manage robotic agents with completely uncertain dynamics including uncertain mass matrices. This allows the robotic control to be environment-independent which can be used in various settings, from underwater to space where identifying system dynamics parameters is challenging. The stability and parameter convergence of the closed-loop system are rigorously analyzed using the Lyapunov method. Numerical simulations validate the effectiveness of the proposed scheme.
Hybrid precoding plays an important role in massive MIMO systems for reducing the hardware cost caused by radio frequency (RF) chains. In this paper, an efficient joint hybrid precoding and analog combining (EJHPAC) s...
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ISBN:
(纸本)9798350361261;9798350361278
Hybrid precoding plays an important role in massive MIMO systems for reducing the hardware cost caused by radio frequency (RF) chains. In this paper, an efficient joint hybrid precoding and analog combining (EJHPAC) scheme is proposed for massive MIMO with multiple-antenna user equipment (UE), which applies the phase elimination method to harvest the power gain. Specifically, the problem of analog combining is transformed to a least square problem with constant modulus constraint. Based on it, we adopt the gradient descent projection (GDP) method to the analog combiner and jointly design the related hybrid precoding algorithm, which leads to the proposed EJHPAC algorithm. According to complexity analysis and simulation results, we show that the EJHPAC algorithm has advantages in both spectral efficiency and computational complexity for massive MIMO systems.
Raman spectroscopy offers invaluable insights into the chemical composition and structural characteristics of various materials, making it a powerful tool for structural analysis. However, accurate quantum mechanical ...
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ISBN:
(数字)9798350352917
ISBN:
(纸本)9798350352924;9798350352917
Raman spectroscopy offers invaluable insights into the chemical composition and structural characteristics of various materials, making it a powerful tool for structural analysis. However, accurate quantum mechanical simulations of Raman spectra for large systems, such as biological materials, have been limited due to immense computational costs and technical challenges. In this study, we developed efficient algorithms and optimized implementations on heterogeneous computing architectures to enable fast and highly scalable ab initio simulations of Raman spectra for large-scale biological systems with up to 100 million atoms. Our simulations have achieved nearly linear strong and weak scaling on two cutting-edge high-performance computingsystems, with peak FP64 performances reaching 400 PFLOPS on 96,000 nodes of new Sunway supercomputer and 85 PFLOPS on 6,000 node of ORISE supercomputer. These advances provide promising prospects for extending quantum mechanical simulations to biological systems.
Arm robotics is becoming more and more popular due to the need to help humans with difficult and repetitive tasks and the desire to increase productivity. Optimal control techniques are required for better control of ...
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This study extends the whole-body control (WBC) formulation for bipedal humanoid robots that include closed (parallel) kinematic chains in their structure. Along with general formulation, we also stress the implementa...
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ISBN:
(纸本)9781665491907
This study extends the whole-body control (WBC) formulation for bipedal humanoid robots that include closed (parallel) kinematic chains in their structure. Along with general formulation, we also stress the implementation of this formulation on Kangaroo, which is a highly dynamic humanoid robot developed by PAL Robotics. This 76-DOF robot includes 24 independent closed-kinematic chains in its structure and constitutes a good case study for our approach. We discuss the WBC formulation for various control structures, including inverse dynamics control (IDC) and Modular Passive Tracking control (MPTC). As a test scenario, we employ a 3D spring-loaded inverted pendulum (SLIP) jumping trajectory with disturbance rejection as the desired CoM trajectory.
This paper proposes a reachability-aware model predictive control with a discrete control barrier function for backward obstacle avoidance for a tractor-trailer system. The framework incorporates the state-variant rea...
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ISBN:
(纸本)9781665491907
This paper proposes a reachability-aware model predictive control with a discrete control barrier function for backward obstacle avoidance for a tractor-trailer system. The framework incorporates the state-variant reachable set obtained through sampling-based reachability analysis and symbolic regression into the objective function of model predictive control. By optimizing the intersection of the reachable set and iterative non-safe region generated by the control barrier function, the system demonstrates better performance in terms of safety with a constant decay rate, while enhancing the feasibility of the optimization problem. The proposed algorithm improves real-time performance due to a shorter horizon and outperforms the state-of-the-art algorithms in the simulation environment and on a real robot.
Together, the Internet of Things (IoT) and cognitive computing have revolutionized the agricultural industry by enabling the implementation of intelligent husbandry systems. Investigating the creation and use of an In...
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Together, the Internet of Things (IoT) and cognitive computing have revolutionized the agricultural industry by enabling the implementation of intelligent husbandry systems. Investigating the creation and use of an Internet of Things-enabled smart husbandry system that leverages cognitive computing techniques is the aim of this study. The system includes detectors to gather data on environmental factors like crop health, soil temperature, and humidity in real-time. After that, a centralized platform receives these data. This data is processed by algorithms used in cognitive computing to give growers useful insights like optimal irrigation timing and early detection of crop issues. The application of this method raises agricultural operations' productivity and enhances decision-making processes' effectiveness. This study covers the following topics: the system's armature;the choice and incorporation of Internet of Things bias;the use of cognitive computing models;and the assessment of system performance in actual agricultural scripts. The results show how cognitive computing and the Internet of Things (IoT) may revolutionize traditional husbandry methods into data-driven, intelligent husbandry systems.
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