Uncertainty in robot dynamic systems is caused by model errors in the dynamic parameters, and accurate identification of the dynamic parameters is essential to improve the control accuracy of the robot. In this paper,...
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This paper presents a new negative imaginary(NI) synthesis method for a linear-time-invariant(LTI) system with up to two poles at the origin. A dynamic parallel feedforward compensator(DPFC) is added to the controlled...
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Rehabilitation robots based on variable stiffness elastic actuators exhibit strong human-robot interaction char-acteristics due to the specific features of the actuators, including flexible drive and variable stiffnes...
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Recent years have seen a rising interest in distributed optimization problems because of their widespread applications in power grids, multi-robot control, and regression *** the last few decades, many distributed alg...
Recent years have seen a rising interest in distributed optimization problems because of their widespread applications in power grids, multi-robot control, and regression *** the last few decades, many distributed algorithms have been developed for tackling distributed optimization problems. In these algorithms, agents over the network only have access to their own local functions and exchange information with their neighbors.
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 the coal-to-ethylene glycol(CTEG)process,precisely estimating quality variables is crucial for process monitoring,optimization,and control.A significant challenge in this regard is relying on offline laboratory ana...
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In the coal-to-ethylene glycol(CTEG)process,precisely estimating quality variables is crucial for process monitoring,optimization,and control.A significant challenge in this regard is relying on offline laboratory analysis to obtain these variables,which often incurs substantial monetary costs and significant time *** resulting few-shot learning scenarios present a hurdle to the efficient development of predictive *** address this issue,our study introduces the transferable adversarial slow feature extraction network(TASF-Net),an innovative approach designed specifically for few-shot quality prediction in the CTEG ***-Net uniquely integrates the slowness principle with a deep Bayesian framework,effectively capturing the nonlinear and inertial characteristics of the CTEG ***,the model employs a variable attention mechanism to identify quality-related input variables adaptively at each time step.A key strength of TASF-Net lies in its ability to navigate the complex measurement noise,outliers,and system interference typical in CTEG *** learning strategy using a min-max game is adopted to improve its robustness and ability to model irregular industrial data accurately and ***,an incremental refining transfer learning framework is designed to further improve few-shot prediction performance achieved by transferring knowledge from the pretrained model on the source domain to the target *** effectiveness and superiority of TASF-Net have been empirically validated using a real-world CTEG *** with some state-of-the-art methods,TASF-Net demonstrates exceptional capability in addressing the intricate challenges for few-shot quality prediction in the CTEG process.
Collaborative Simultaneous Localization and Mapping (CSLAM) is a technology that enables multiple robots to estimate their own trajectories and collaboratively construct a global map in a large-scale unknown environme...
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This article proposes a new time-series signal processing model for crack detection and localization in spiral welded pipelines. Using the time series signal of pipeline cracks as the original dataset, local features ...
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The hydrogen management subsystem has a significant impact on the output performance of proton exchange membrane fuel cells (PEMFC) and plays a crucial role in the control of fuel cell systems. In order to improve the...
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Offline multi-agent reinforcement learning (MARL) has attracted considerable attention for its capability to learn distributed policies from offline datasets, eliminating the need for costly exploratory interactions o...
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