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The high-precision trajectory tracking and chatter-free performance of the manipulators are of great significance to the improvement of the automation level. Therefore, in view of the problems of chattering and tracking performance degradation of multi-joint manipulators caused by uncertain factors such as external disturbances and modeling errors, an adaptive fuzzy sliding mode control of the manipulator based on an improved super-twisting algorithm is proposed. First, fuzzy control is chosen to approximate the uncertainty of the manipulator model, which reduces the influence of unknown disturbances on the system, and then the adaptive law is utilized to automatically modify and improve the fuzzy rules to enhance the approximation capability; Second, an improved super-twisting algorithm is used on this basis to put the discontinuous term into the integral term to avoid chattering and eliminate the approximation error; Finally, a comparative simulation experiment is carried out with the 3-DOF manipulator as the research object. The experimental results show that the method in this paper improves the convergence speed by up to 69.74% and the tracking error accuracy by up to 85.11% over the fuzzy control based on adaptive reaching law with no chattering and little difference in control inputs, thus verifying the feasibility and superiority of the method. [ABSTRACT FROM AUTHOR]

Traumatic rupture of the aorta (TRA) is highly lethal in motor vehicle collisions (MVCs). Human body biomechanical models (HUBMs) show promise as useful tools for better comprehension of the mechanical response and injury mechanism of TRA. However, the prediction accuracy largely depends on its material modeling. This study investigated the performance of the isotropic elastic material constitutive model (Mat_001) and the orthotropic elastic material constitutive model (Mat_002) for predicting aortic dynamic response and injury in blunt side impacts, via simulations against vertical and oblique side impact cadaver tests with previously developed HUBM. The aortic strain response, peak intraluminal pressure, thoracic deflections and injury distributions were compared. It was found that both Mat_001 and Mat_002 could be used to model aortic tissue, but Mat_002 presented more sensitivity to blunt side impacts in terms of strain responses. The thoracic rib cage and internal organs sustained more severe injuries under the oblique left side impact (AIS 5+) than those under the vertical left side impact (AIS 4+), whereas the aortic peri-isthmus exhibited a higher TRA risk in the vertical side impact than that in the oblique side impact, with regard to the peak longitudinal strain. The TRA could result from the aorta's sudden stretch, and the peak longitudinal strain could be regarded as a main contributing factor to it. This study enhances our understanding of the aortic material modeling and injury mechanisms. [ABSTRACT FROM AUTHOR]

This paper presents a sliding mode control based on particle swarm optimization neural network and adaptive reaching law, and the proposed control method solves the problem of chattering and tracking performance degradation of a multi-joint manipulator caused by uncertainties such as external disturbances and modeling error. First, to address the problem that the precise dynamic system of the manipulator is difficult to establish, the radial basis function neural network (RBFNN) is used to approximate the uncertainty of the manipulator model, and the parameters of the neural network are optimized through the adaptive natural selection particle swarm optimization algorithm (ASelPSO) to improve the approximation ability and reduce the approximation error. Second, to eliminate chattering, adaptive reaching law is selected to improve the dynamic quality of approaching motion. Finally, a comparative simulation experiment is carried out with a 3-DOF manipulator as the research object. The results show that the control method has obvious improvements in eliminating chattering, improving tracking accuracy, and increasing convergence speed, which verifies the feasibility and superiority of the control scheme. [ABSTRACT FROM AUTHOR]

High-tilt reclined seats are one of the most popular configurations in highly automated vehicles; however, current restraint systems cannot protect out-of-position occupants in this type of seat. To reduce the risk of injury to reclined occupants, this study proposes a swiveling seat driven by occupant inertia and rotated in the sagittal plane during impact. The effectiveness of the swiveling seat was evaluated based on kinematics and injury to a human biomechanical model in a frontal sled test. A simulation matrix was constructed to design and optimize various safety devices, including the belt, pre-tensioner, knee constraint, and rotation stiffness for the swiveling seat. The results showed that (1) submarining occurred when the reclined occupant was on a fixed seat with a normal three-point belt during impact; (2) a fixed seat with a dynamic locking tongue and passenger lap pretension prevented the submarining, but produced a high lumbar force of 5359 N, which was higher than the spine injury criterion; and (3) the proposed swiveling seat with a matched restraint system could prevent submarining and produce lumbar force of 1787 N. The results demonstrated that the swiveling seat has high potential for occupant protection in intelligent driving scenarios. [ABSTRACT FROM AUTHOR]

This article is concerned with the trajectory tracking control problem for the nonlinear systems in the sense of the predefined settling time and accuracy. In contrast with the existing works, we focus on the cases where the system dynamics, its bounding functions, the unmatched disturbances, and the time‐varying parameters are totally unknown; the derivatives of the desired trajectory are not required to be available. They significantly challenge the identification and/or approximation‐based control solutions. To overcome this obstacle, a novel robust prescribed performance control approach via state feedback is put forward in this article. It not only ensures the natural satisfaction of the specific initial condition but also realizes a full‐time performance specification for trajectory tracking. Furthermore, for the case of unmeasured state variables, an output‐feedback control approach is further derived by adopting an input‐driven filter and conducting trivial changes on the design procedure. Moreover, both approaches exhibit significant simplicity, without the needs for parameter identification, function approximation, disturbance estimation, derivative calculation, or command filtering. Three simulation studies are conducted to clarify and verify the above theoretical findings. [ABSTRACT FROM AUTHOR]