273 research outputs found
K-BMPC: Derivative-based Koopman Bilinear Model Predictive Control For Tractor-trailer Trajectory Tracking With Unknown Parameters
Nonlinear dynamics bring difficulties to controller design for control-affine
systems such as tractor-trailer vehicles, especially when the parameters in
dynamics are unknown. To address this constraint, we propose a derivative-based
lifting function construction method, show that the corresponding infinite
dimensional Koopman bilinear model over the lifting function is equivalent to
the original control-affine system. Further, we analyze the propagation and
bounds of state prediction errors caused by the the truncation in derivative
order. The identified finite dimensional Koopman bilinear model would serve as
predictive model in next step. Koopman Bilinear Model Predictive control
(K-BMPC) is proposed to solve the trajectory tracking problem. We linearize the
bilinear model around the estimation of the lifted state and control input.
Then the bilinear Model Predictive Control problem is approximated by a
quadratic programming problem. Further, the estimation is updated at each
iteration until the convergence is reached. Moreover, we implement our
algorithm on a tractor-trailer dynamic system, taking into account the
longitudinal and side slip effects. The open-loop simulation shows the proposed
Koopman bilinear model captures the dynamics with unknown parameters and has
good prediction performance. Closed loop tracking results show the proposed
K-BMPC exhibits elevated tracking precision along with commendable
computational efficiency. The experimental results demonstrate the feasibility
of the proposed method
PlaneDepth: Plane-Based Self-Supervised Monocular Depth Estimation
Self-supervised monocular depth estimation refers to training a monocular
depth estimation (MDE) network using only RGB images to overcome the difficulty
of collecting dense ground truth depth. Many previous works addressed this
problem using depth classification or depth regression. However, depth
classification tends to fall into local minima due to the bilinear
interpolation search on the target view. Depth classification overcomes this
problem using pre-divided depth bins, but those depth candidates lead to
discontinuities in the final depth result, and using the same probability for
weighted summation of color and depth is ambiguous. To overcome these
limitations, we use some predefined planes that are parallel to the ground,
allowing us to automatically segment the ground and predict continuous depth
for it. We further model depth as a mixture Laplace distribution, which
provides a more certain objective for optimization. Previous works have shown
that MDE networks only use the vertical image position of objects to estimate
the depth and ignore relative sizes. We address this problem for the first time
in both stereo and monocular training using resize cropping data augmentation.
Based on our analysis of resize cropping, we combine it with our plane
definition and improve our training strategy so that the network could learn
the relationship between depth and both the vertical image position and
relative size of objects. We further combine the self-distillation stage with
post-processing to provide more accurate supervision and save extra time in
post-processing. We conduct extensive experiments to demonstrate the
effectiveness of our analysis and improvements.Comment: 12 pages, 7 figure
Diffusion Mechanism in Residual Neural Network: Theory and Applications
Diffusion, a fundamental internal mechanism emerging in many physical
processes, describes the interaction among different objects. In many learning
tasks with limited training samples, the diffusion connects the labeled and
unlabeled data points and is a critical component for achieving high
classification accuracy. Many existing deep learning approaches directly impose
the fusion loss when training neural networks. In this work, inspired by the
convection-diffusion ordinary differential equations (ODEs), we propose a novel
diffusion residual network (Diff-ResNet), internally introduces diffusion into
the architectures of neural networks. Under the structured data assumption, it
is proved that the proposed diffusion block can increase the distance-diameter
ratio that improves the separability of inter-class points and reduces the
distance among local intra-class points. Moreover, this property can be easily
adopted by the residual networks for constructing the separable hyperplanes.
Extensive experiments of synthetic binary classification, semi-supervised graph
node classification and few-shot image classification in various datasets
validate the effectiveness of the proposed method
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