7,876 research outputs found
Asymmetrically interacting spreading dynamics on complex layered networks
The spread of disease through a physical-contact network and the spread of
information about the disease on a communication network are two intimately
related dynamical processes. We investigate the asymmetrical interplay between
the two types of spreading dynamics, each occurring on its own layer, by
focusing on the two fundamental quantities underlying any spreading process:
epidemic threshold and the final infection ratio. We find that an epidemic
outbreak on the contact layer can induce an outbreak on the communication
layer, and information spreading can effectively raise the epidemic threshold.
When structural correlation exists between the two layers, the information
threshold remains unchanged but the epidemic threshold can be enhanced, making
the contact layer more resilient to epidemic outbreak. We develop a physical
theory to understand the intricate interplay between the two types of spreading
dynamics.Comment: 29 pages, 14 figure
Integrating SPC and EPC for Multivariate Autocorrelated Process
Statistical process control (SPC) is a widely employed quality control method in industry. SPC is mainly designed for monitoring single quality characteristic. However, as the design of a product/process becomes complex, a process usually has multiple quality characteristics related to it. These characteristics must be monitored by multivariate SPC. When the autocorrelation is present in the process data, the traditional SPC may mislead the results. Hence, the autocorrelated data must be treated to eliminate the autocorrelation effect before employing SPC to detect the assignable causes. Besides, chance causes also have impact on the processes. When the process is out of control but no assignable cause is found, it can be adjusted by employing engineering process control (EPC). However, only using EPC to adjust the process may make inappropriate adjustments due to external disturbances or assignable causes. This study presents an integrated SPC and EPC procedure for multivariate autocorrelated process. The SPC procedure constructs a predicting model using group method of data handling (GMDH), which can transfer the autocorrelated data into uncorrelated data. Then, the Hotellingās T2 and multivariate cumulative sum control charts are constructed to monitor the process. The EPC procedure constructs a controller utilizing data mining technique to adjust the multiple quality characteristics to their target values. Industry can employ this procedure to monitor and adjust the multivariate autocorrelated process
Learning-based Predictive Path Following Control for Nonlinear Systems Under Uncertain Disturbances
Accurate path following is challenging for autonomous robots operating in
uncertain environments. Adaptive and predictive control strategies are crucial
for a nonlinear robotic system to achieve high-performance path following
control. In this paper, we propose a novel learning-based predictive control
scheme that couples a high-level model predictive path following controller
(MPFC) with a low-level learning-based feedback linearization controller
(LB-FBLC) for nonlinear systems under uncertain disturbances. The low-level
LB-FBLC utilizes Gaussian Processes to learn the uncertain environmental
disturbances online and tracks the reference state accurately with a
probabilistic stability guarantee. Meanwhile, the high-level MPFC exploits the
linearized system model augmented with a virtual linear path dynamics model to
optimize the evolution of path reference targets, and provides the reference
states and controls for the low-level LB-FBLC. Simulation results illustrate
the effectiveness of the proposed control strategy on a quadrotor path
following task under unknown wind disturbances.Comment: 8 pages, 7 figures, accepted for publication in IEEE Robotics and
Automation Letters ( Volume: 6, Issue: 2, April 2021
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