86,546 research outputs found

    Adaptive control method and system in a terrestrial vehicle for tracking a route, particularly in an autonomous driving scenario

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    A method is described for the control of the lateral movement of a terrestrial vehicle arranged to track a predetermined trajectory, particularly in an assisted driving or autonomous driving scenario, comprising: determining a lateral offset of the vehicle center of mass from the predetermined trajectory; determining a look-ahead error defined as a distance of a virtual look-ahead position of the vehicle center of mass from the predetermined trajectory; and controlling the steering angle of the vehicle so as to also minimize the lateral offset and the first derivative of said look-ahead error over time

    Hybrid parameter identification of a multi-modal underwater soft robot

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    We introduce an octopus-inspired, underwater, soft-bodied robot capable of performing waterborne pulsed-jet propulsion and benthic legged-locomotion. This vehicle consists for as much as 80% of its volume of rubber-like materials so that structural flexibility is exploited as a key element during both modes of locomotion. The high bodily softness, the unconventional morphology and the non-stationary nature of its propulsion mechanisms require dynamic characterization of this robot to be dealt with by ad hoc techniques. We perform parameter identification by resorting to a hybrid optimization approach where the characterization of the dual ambulatory strategies of the robot is performed in a segregated fashion. A least squares-based method coupled with a genetic algorithm-based method is employed for the swimming and the crawling phases, respectively. The outcomes bring evidence that compartmentalized parameter identification represents a viable protocol for multi-modal vehicles characterization. However, the use of static thrust recordings as the input signal in the dynamic determination of shape-changing self-propelled vehicles is responsible for the critical underestimation of the quadratic drag coefficient

    Interferon β-1a downregulates TNFα-induced intercellular adhesion molecule 1 expression on brain microvascular endothelial cells through a tyrosine kinase-dependent pathway

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    TNFα (100 U/ml, 24 h) upregulated intercellular adhesion molecule 1 (ICAM1) expression on brain microvascular endothelial cell (BMEC) culture. The tyrosine kinase (TK) inhibitor genestein (100 μg/ml), the protein kinase C (PKC) inhibitor staurosporin (1 nM), and interferon (IF) β-1a (1000 U/ml) antagonized TNFα effect. When an ineffective dose of IFβ-1a (100 U/ml) was challenged with ineffective doses of either genestein (10 μg/ml) or staurosporin (0.1 nM), the combination IFβ-1a-genestein significantly reduced TNFα-induced ICAM1 expression whereas IFβ-1a-staurosporin did not. These findings indicate that a TK- rather than a PKC-dependent mechanism is involved in the modulation of TNFα response by IFβ-1a on BMECs. Copyright (C) 2000 Elsevier Science B.V

    An LPV Approach to Autonomous Vehicle Path Tracking in the Presence of Steering Actuation Nonlinearities

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    This article deals with trajectory tracking for autonomous cars during evasive maneuvers and in the presence of steering actuator nonlinearities. This article develops an LPV MISO H-infinity controller based on the feedback of the lateral error at the center of gravity and the look-ahead distance. The controller architecture offers a way to cope with the effect of the steering nonlinearities, by scheduling one of the control weighting functions. A detailed experimental validation on three different maneuvers (straight driving, wide bend, and a double-lane change) shows the effectiveness of the proposed LPV solution

    Hybrid parameter identification of a multi-modal underwater soft robot

    No full text
    We introduce an octopus-inspired, underwater, soft-bodied robot capable of performing waterborne pulsed-jet propulsion and benthic legged-locomotion. This vehicle consists for as much as 80% of its volume of rubber-like materials so that structural flexibility is exploited as a key element during both modes of locomotion. The high bodily softness, the unconventional morphology and the non-stationary nature of its propulsion mechanisms require dynamic characterization of this robot to be dealt with by ad hoc techniques. We perform parameter identification by resorting to a hybrid optimization approach where the characterization of the dual ambulatory strategies of the robot is performed in a segregated fashion. A least squares-based method coupled with a genetic algorithm-based method is employed for the swimming and the crawling phases, respectively. The outcomes bring evidence that compartmentalized parameter identification represents a viable protocol for multi-modal vehicles characterization. However, the use of static thrust recordings as the input signal in the dynamic determination of shape-changing self-propelled vehicles is responsible for the critical underestimation of the quadratic drag coefficient
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