62 research outputs found
Distributed Model Predictive Control for Heterogeneous Vehicle Platoon with Inter-Vehicular Spacing Constraints
This paper proposes a distributed control scheme
for a platoon of heterogeneous vehicles based on the mechanism
of model predictive control (MPC). The platoon composes of a
group of vehicles interacting with each other via inter-vehicular
spacing constraints, to avoid collision and reduce communication
latency, and aims to make multiple vehicles driving on the same
lane safely with a close range and the same velocity. Each
vehicle is subject to both state constraints and input constraints,
communicates only with neighboring vehicles, and may not know
a priori desired setpoint. We divide the computation of control
inputs into several local optimization problems based on each
vehicle’s local information. To compute the control input of
each vehicle based on local information, a distributed computing
method must be adopted and thus the coupled constraint is
required to be decoupled. This is achieved by introducing the
reference state trajectories from neighboring vehicles for each
vehicle and by employing the interactive structure of computing
local problems of vehicles with odd indices and even indices. It
is shown that the feasibility of MPC optimization problems is
achieved at all time steps based on tailored terminal inequality
constraints, and the asymptotic stability of each vehicle to the
desired trajectory is guaranteed even under a single iteration
between vehicles at each time. Finally, a comparison simulation
is conducted to demonstrate the effectiveness of the proposed
distributed MPC method for heterogeneous vehicle control with
respect to normal and extreme scenarios
Workflow-based Fast Data-driven Predictive Control with Disturbance Observer in Cloud-edge Collaborative Architecture
Data-driven predictive control (DPC) has been studied and used in various
scenarios, since it could generate the predicted control sequence only relying
on the historical input and output data. Recently, based on cloud computing,
data-driven predictive cloud control system (DPCCS) has been proposed with the
advantage of sufficient computational resources. However, the existing
computation mode of DPCCS is centralized. This computation mode could not
utilize fully the computing power of cloud computing, of which the structure is
distributed. Thus, the computation delay could not been reduced and still
affects the control quality. In this paper, a novel cloud-edge collaborative
containerised workflow-based DPC system with disturbance observer (DOB) is
proposed, to improve the computation efficiency and guarantee the control
accuracy. First, a construction method for the DPC workflow is designed, to
match the distributed processing environment of cloud computing. But the
non-computation overheads of the workflow tasks are relatively high. Therefore,
a cloud-edge collaborative control scheme with DOB is designed. The low-weight
data could be truncated to reduce the non-computation overheads. Meanwhile, we
design an edge DOB to estimate and compensate the uncertainty in cloud workflow
processing, and obtain the composite control variable. The UUB stability of the
DOB is also proved. Third, to execute the workflow-based DPC controller and
evaluate the proposed cloud-edge collaborative control scheme with DOB in the
real cloud environment, we design and implement a practical workflow-based
cloud control experimental system based on container technology. Finally, a
series of evaluations show that, the computation times are decreased by 45.19%
and 74.35% for two real-time control examples, respectively, and by at most
85.10% for a high-dimension control example.Comment: 58 pages and 23 figure
Acute rejection is associated with antibodies to non-Gal antigens in baboons using Gal-knockout pig kidneys
We transplanted kidneys from α1,3-galactosyltransferase knockout (GalT-KO) pigs into six baboons using two different immunosuppressive regimens, but most of the baboons died from severe acute humoral xenograft rejection. Circulating induced antibodies to non-Gal antigens were markedly elevated at rejection, which mediated strong complement-dependent cytotoxicity against GalT-KO porcine target cells. These data suggest that antibodies to non-Gal antigens will present an additional barrier to transplantation of organs from GalT-KO pigs to humans. © 2005 Nature Publishing Group
Large-scale association analysis identifies new lung cancer susceptibility loci and heterogeneity in genetic susceptibility across histological subtypes.
Although several lung cancer susceptibility loci have been identified, much of the heritability for lung cancer remains unexplained. Here 14,803 cases and 12,262 controls of European descent were genotyped on the OncoArray and combined with existing data for an aggregated genome-wide association study (GWAS) analysis of lung cancer in 29,266 cases and 56,450 controls. We identified 18 susceptibility loci achieving genome-wide significance, including 10 new loci. The new loci highlight the striking heterogeneity in genetic susceptibility across the histological subtypes of lung cancer, with four loci associated with lung cancer overall and six loci associated with lung adenocarcinoma. Gene expression quantitative trait locus (eQTL) analysis in 1,425 normal lung tissue samples highlights RNASET2, SECISBP2L and NRG1 as candidate genes. Other loci include genes such as a cholinergic nicotinic receptor, CHRNA2, and the telomere-related genes OFBC1 and RTEL1. Further exploration of the target genes will continue to provide new insights into the etiology of lung cancer
EFFECTS OF TEMPERATURE ON THE SPECTRAL EMISSIVITY OF C/SiC COMPOSITES
The effect of temperature on the infrared spectral emissivity of C/SiC composites as a thermal protection material has been studied, using a measurement system based on a FT-IR spectrometer. The spectral emissivity of C/SiC composites in the wavelength range 3-20 μm and in the temperature range from 1000 K to over 2000 K was measured. Based on the analysis of the measured spectral emissivity, variations of the spectral emissivity with temperature were studied. The relationship between emissivity and temperature in some certain wavelength was found
Tracking of Unicycle Robots Using Event-Based MPC With Adaptive Prediction Horizon
In this article, we propose two event-based model predictive control (MPC) schemes with adaptive prediction horizon for tracking of unicycle robots with additive disturbances. The schemes are able to reduce the computational burden from two aspects: reducing the frequency of solving the optimization control problem (OCP) to relieve the computational load and decreasing the prediction horizon to decline the computational complexity. Event-triggering and self-triggering mechanisms are developed to activate the OCP solver aperiodically, and a prediction horizon update strategy is presented to decrease the dimension of the OCP in each step. The proposed schemes are tested on a networked platform to show their efficiency
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