scholarly journals Optimal Control of a Surface Vehicle to Improve Underwater Vehicle Network Connectivity

2012 ◽  
Vol 9 (1) ◽  
pp. 1-13 ◽  
Author(s):  
Camila C. Françolin ◽  
Anil V. Rao ◽  
Christiane Duarte ◽  
Gerald Martel
Keyword(s):  
Optimal Control ◽  
Network Connectivity ◽  
Underwater Vehicle ◽  
Vehicle Network

Feedforward and Feedback Optimal Control of Autonomous Profiling Monitoring Underwater Vehicle with Disturbance

2014 ◽  
Vol 602-605 ◽  
pp. 970-973 ◽  
Author(s):  
Hua Mu ◽  
Jian Yuan
Keyword(s):  
Optimal Control ◽  
Linear System ◽  
Feedback Linearization ◽  
Vertical Plane ◽  
Underwater Vehicle ◽  
Linearization Method ◽  
Control Law ◽  
Control Scheme ◽  
System States ◽  
Feedback Optimal Control

The optimal control of autonomous profiling monitoring underwater vehicle (APMUV) is investigated. Firstly, dynamics equations in vertical plane with disturbances are constructed, and the equations are converted into a linear system by feedback linearization method and then feedforward and feedback optimal control (FFOC) law is designed for the linear system. To solve the unpractical problem of the control law, we construct a disturbance observer to observe the system states to make a quick convergance of the observed system states. Numerical simulations show the effectiveness of the control scheme

Research in OFDM-Based High-Speed In-Vehicle Network Connectivity for Cameras and Displays

2021 ◽  
Author(s):  
Taiji Kondo ◽  
Kayo Ueda ◽  
Naoshi Serizawa ◽  
Tomoyuki Koike ◽  
Hisashi Kondo
Keyword(s):  
High Speed ◽  
Network Connectivity ◽  
Vehicle Network

scholarly journals Optimal control of complex networks with conformity behavior

2022 ◽  
Vol 2022 (1) ◽  
pp. 013401
Author(s):  
Zu-Yu Qian ◽  
Cheng Yuan ◽  
Jie Zhou ◽  
Shi-Ming Chen ◽  
Sen Nie
Keyword(s):  
Optimal Control ◽  
Complex Networks ◽  
Critical Point ◽  
Control Strategy ◽  
Network Connectivity ◽  
Network Control ◽  
Networked Systems ◽  
Optimal Control Strategy ◽  
Practical Applications ◽  
Conformity Behavior

Abstract Despite the significant advances in identifying the driver nodes and energy requiring in network control, a framework that incorporates more complicated dynamics remains challenging. Here, we consider the conformity behavior into network control, showing that the control of undirected networked systems with conformity will become easier as long as the number of external inputs beyond a critical point. We find that this critical point is fundamentally determined by the network connectivity. In particular, we investigate the nodal structural characteristic in network control and propose optimal control strategy to reduce the energy requiring in controlling networked systems with conformity behavior. We examine those findings in various synthetic and real networks, confirming that they are prevailing in describing the control energy of networked systems. Our results advance the understanding of network control in practical applications.

scholarly journals The brain uses invariant dynamics to generalize outputs across movements

2021 ◽  
Author(s):  
Vivek R. Athalye ◽  
Preeya Khanna ◽  
Suraj Gowda ◽  
Amy L. Orsborn ◽  
Rui M. Costa ◽  
...  
Keyword(s):  
Optimal Control ◽  
Nervous System ◽  
Neural Activity ◽  
Optimal Control Theory ◽  
Activity Patterns ◽  
Network Connectivity ◽  
Brain Machine Interface ◽  
Recent Proposal ◽  
Machine Interface ◽  
The Brain

AbstractThe nervous system uses a repertoire of outputs to produce diverse movements. Thus, the brain must solve how to issue and transition the same outputs in different movements. A recent proposal states that network connectivity constrains the transitions of neural activity to follow invariant rules across different movements, which we term ‘invariant dynamics’. However, it is unknown whether invariant dynamics are actually used to drive and generalize outputs across movements, and what advantage they provide for controlling movement. Using a brain-machine interface that transformed motor cortex activity into outputs for a neuroprosthetic cursor, we discovered that the same output is issued by different activity patterns in different movements. These distinct patterns then transition according to a model of invariant dynamics, leading to patterns that drive distinct future outputs. Optimal control theory revealed this use of invariant dynamics reduces the feedback input needed to control movement. Our results demonstrate that the brain uses invariant dynamics to generalize outputs across movements.

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