We propose to estimate the region of attraction (ROA) for the stability of nonlinear systems from only system measurement data and without knowledge of the system model. The key to our result is the use of Koopman ope...
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We propose to estimate the region of attraction (ROA) for the stability of nonlinear systems from only system measurement data and without knowledge of the system model. The key to our result is the use of Koopman operator theory to approximate the nonlinear dynamics in linear coordinates. This approximation is typically more accurate than the traditional Jacobian-based linearization method. We then employ the Extended Dynamic Mode Decomposition (EDMD) method to estimate the linear approximation of the system through data. This is then used to construct a Lyapunov function that helps estimate the ROA. However, this estimate is typically very conservative. The trajectory reversing method is then used on the set of points that form this conservative estimate, to enlarge the ROA approximation. The output of EDMD is also utilized in the trajectory reversing method, keeping the entire analysis data-driven. Finally, an example is used to show the accuracy of this data-driven method, despite not knowing the system.
modeling human trajectories in crowded environments is challenging due to the complex nature of pedestrian behavior and interactions. This paper proposes a geometric graph neural network (GNN) architecture that integr...
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modeling human trajectories in crowded environments is challenging due to the complex nature of pedestrian behavior and interactions. This paper proposes a geometric graph neural network (GNN) architecture that integrates domain knowledge from psychological studies to model pedestrian interactions and predict future trajectories. Unlike prior studies using complete graphs, we defne interaction neighborhoods using pedestrians’ field of view, motion direction, and distance-based kernel functions to construct graph representations of crowds. Evaluations across multiple datasets demonstrate improved prediction accuracy through reduced average and final displacement error metrics. Our findings underscore the importance of integrating domain knowledge with data-driven approaches for effective modeling of human interactions in crowds.
The presence of adverse road conditions like water, snow, and ice is known to largely increase the chances of road vehicle crashes due to reduced vehicle performance. Providing useful information about future tire-roa...
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The presence of adverse road conditions like water, snow, and ice is known to largely increase the chances of road vehicle crashes due to reduced vehicle performance. Providing useful information about future tire-road friction characteristics that will influence vehicle traction and braking capability are essential for safe operation under such weather conditions. This paper describes an approach for using a low-cost camera, vehicle GPS and other basic vehicle sensors on a scaled autonomous vehicle to build a Neural Network for slip-slope prediction. A Recursive Least Squares Filter is used to estimate the longitudinal friction slip-slope. As an alternative to surface classification which requires a labelled data set, this method uses an easily obtained slip-slope to infer tire-road friction levels. A preliminary experiment is conducted which indicates the neural networks effectiveness to distinguish slip-slope values based on previewed road images captured by the vehicle camera.
The SeptaPose Assistive and Rehabilitative (SPAR) Glove has been developed to assist individuals with upper extremity impairment arising from neuromuscular injury. The glove detects user intent via the MYO wearable el...
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The SeptaPose Assistive and Rehabilitative (SPAR) Glove has been developed to assist individuals with upper extremity impairment arising from neuromuscular injury. The glove detects user intent via the MYO wearable electromyography (EMG) device. In this manuscript, pattern recognition tools infer the desired hand pose from EMG activity. The ability of the measurement and classification methods to distinguish between hand poses was evaluated with nine able-bodied participants and three participants with spinal cord injury (SCI) in an offline experiment. The strong performance of the proposed intent detection method is shown in the steady-state classification accuracy, presented as confusion matrices, as well as the average confidence for each classification. Building upon the strong performance in detecting pose, a pilot study with two participants with SCI presents the initial results of the real-time implementation of the system, which suggests directions for future work in improving the steady-state classification accuracy through expanded measurement and a refined taxonomy to enable intuitive control. Copyright (C) 2021 The Authors.
This paper presents a novel modeling method for the control design of autonomous vehicle systems. The goal of the method is to provide a control-oriented model in a predefined Linear Parameter Varying (LPV) structure....
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This paper presents a novel modeling method for the control design of autonomous vehicle systems. The goal of the method is to provide a control-oriented model in a predefined Linear Parameter Varying (LPV) structure. The scheduling variables of the LPV model through machine-learning-based methods using a big dataset are selected. Moreover, the LPV model parameters through an optimization algorithm are computed, with which accurate fitting on the dataset is achieved. The proposed method is illustrated on the nonlinear modeling of the lateral vehicle dynamics. The resulting LPV-based vehicle model is used for the control design of path following functionality of autonomous vehicles. The effectiveness of the modeling and control design methods through comprehensive simulation examples based on a high-fidelity simulation software are illustrated.
The SeptaPose Assistive and Rehabilitative (SPAR) Glove has been developed to assist individuals with upper extremity impairment arising from neuromuscular injury. The glove detects user intent via the MYO wearable el...
详细信息
The SeptaPose Assistive and Rehabilitative (SPAR) Glove has been developed to assist individuals with upper extremity impairment arising from neuromuscular injury. The glove detects user intent via the MYO wearable electromyography (EMG) device. In this manuscript, pattern recognition tools infer the desired hand pose from EMG activity. The ability of the measurement and classification methods to distinguish between hand poses was evaluated with nine able-bodied participants and three participants with spinal cord injury (SCI) in an offline experiment. The strong performance of the proposed intent detection method is shown in the steady-state classification accuracy, presented as confusion matrices, as well as the average confidence for each classification. Building upon the strong performance in detecting pose, a pilot study with two participants with SCI presents the initial results of the real-time implementation of the system, which suggests directions for future work in improving the steady-state classification accuracy through expanded measurement and a refined taxonomy to enable intuitive control.
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