scholarly journals Ship Steering Control Based on Quantum Neural Network

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Wei Guan ◽  
Haotian Zhou ◽  
Zuojing Su ◽  
Xianku Zhang ◽  
Chao Zhao
Keyword(s):  
Neural Network ◽  
Good Control ◽  
Learning Rate ◽  
Steering Control ◽  
Quantum Neural Network ◽  
Planning And Control ◽  
Fast Learning ◽  
Ship Steering ◽  
Learning Capabilities ◽  
And Control

During the mission at sea, the ship steering control to yaw motions of the intelligent autonomous surface vessel (IASV) is a very challenging task. In this paper, a quantum neural network (QNN) which takes the advantages of learning capabilities and fast learning rate is proposed to act as the foundation feedback control hierarchy module of the IASV planning and control strategy. The numeric simulations had shown that the QNN steering controller could improve the learning rate performance significantly comparing with the conventional neural networks. Furthermore, the numeric and practical steering control experiment of the IASV BAICHUAN has shown a good control performance similar to the conventional PID steering controller and it confirms the feasibility of the QNN steering controller of IASV planning and control engineering applications in the future.

Anomalous Behavior of Merchant Ship Steering Systems

1970 ◽  
Vol 7 (02) ◽  
pp. 205-215 ◽  
Author(s):  
Robert Taggart
Keyword(s):  
High Speed ◽  
Mechanical Characteristics ◽  
Anomalous Behavior ◽  
Similar Response ◽  
Steering Control ◽  
Unstable Condition ◽  
Ship Steering ◽  
Merchant Ship ◽  
Automatic Steering ◽  
And Control

An unusual combination of circumstances occurring during an Atlantic crossing of a highspeed containership created a situation where the rudder, acting in response to automatic steering control demands, caused excessive ship rolling. Further investigation revealed the existence of an unstable condition due to a combination of asymmetrical hydrodynamic and mechanical characteristics and the interrelationship of ship motion and control actuation. Similar response has been noted on other high-speed vessels and is a cause for major concern in future containership operations. The elements involved in creating these conditions have been examined in detail and a plausible explanation has been evolved as to how they can combine to produce the observed results. With an understanding of the causes of this anomalous behavior it is possible to devise means for preventing its occurrence in future designs.

Intelligent Motion Planning and Control for Robotic Joints Using Bio-Inspired Spiking Neural Networks

2019 ◽  
Vol 16 (04) ◽  
pp. 1950012 ◽  
Author(s):  
Mircea Hulea ◽  
Adrian Burlacu ◽  
Constantin-Florin Caruntu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Motion Planning ◽  
Control Method ◽  
Inhibitory Neurons ◽  
Robotic Arm ◽  
Control Approach ◽  
Planning And Control ◽  
Robotic Joints ◽  
And Control

This paper details an intelligent motion planning and control approach for a one-degree of freedom joint of a robotic arm that can be used to implement anthropomorphic robotic hands. This intelligent control method is based on bio-inspired electronic neural networks and contractile artificial muscles implemented with shape memory alloy (SMA) actuators. The spiking neural network (SNN) includes several excitatory neurons that naturally determine the contraction force of the actuators, and unevenly distributed inhibitory neurons that regulate the excitatory activity. To validate the proposed concept, the experiments highlight the motion planning and control of a single-joint robotic arm. The results show that the electronic neural network is able to intelligently activate motion and hold with high precision the mobile link to the target positions even if the arm is slightly loaded. These results are encouraging for the development of improved biologically plausible neural structures that are able to control simultaneously multiple muscles.

scholarly journals Probabilistic prediction for the ampacity of overhead lines using Quantile Regression Neural Network

2020 ◽  
Vol 185 ◽  
pp. 02022
Author(s):  
Xu Jin ◽  
Fudong Cai ◽  
Mengxia Wang ◽  
Yang Sun ◽  
Shengyuan Zhou
Keyword(s):  
Neural Network ◽  
Quantile Regression ◽  
Transmission Lines ◽  
Meteorological Data ◽  
Overhead Line ◽  
Probabilistic Prediction ◽  
Overhead Lines ◽  
Overhead Transmission Lines ◽  
Planning And Control ◽  
And Control

The ampacity of overhead transmission lines play a key role in power system planning and control. Due to the volatility of the meteorological elements, the ampacity of an overhead line is timevarying. In order to fully utilize the transfer capability of overhead transmission lines, it is necessary to provide system operators with accurate probabilistic prediction results of the ampacity. In this paper, a method based on the Quantile Regression Neural Network (QRNN) is proposed to improve the performance of the probabilistic prediction of the ampacity. The QRNN-based method uses a nonlinear model to comprehensively model the impacts of historical meteorological data and historical ampacity data on the ampacity at predictive time period. Numerical simulations based on the actual meteorological data around an overhead line verify the effectiveness of the proposed method.

scholarly journals Savings in human force field learning supported by feedback adaptation

2021 ◽  
Author(s):  
James Mathew ◽  
Philippe Lefevre ◽  
Frederic Crevecoeur
Keyword(s):  
Force Field ◽  
External Disturbance ◽  
Learning Rate ◽  
Human Adaptation ◽  
Planning And Control ◽  
Healthy Humans ◽  
Distinct Process ◽  
And Control ◽  
First Time ◽  
The Brain

Savings have been described as the ability of healthy humans to relearn a previously acquired motor skill faster than the first time, which in the context of motor adaptation suggests that the learning rate in the brain could be adjusted when a perturbation is recognized. Alternatively, it has been argued that apparent savings were the consequence of a distinct process that instead of reflecting a change in the learning rate, revealed an explicit re-aiming strategy. Based on recent evidence that feedback adaptation may be central to both planning and control, we hypothesized that this component could genuinely accelerate relearning in human adaptation to force fields during reaching. Consistent with our hypothesis, we observed that upon re-exposure to a previously learned force field, the very first movement performed by healthy volunteers in the relearning context was better adapted to the external disturbance, and this occurred without any anticipation or cognitive strategy because the relearning session was started unexpectedly. We conclude that feedback adaptation is a medium by which the nervous system can genuinely accelerate learning across movements.

scholarly journals Intelligent Consumer Flexibility Management With Neural Network-Based Planning and Control

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 40755-40767
Author(s):  
Jussi Kiljander ◽  
Roope Sarala ◽  
Jari Rehu ◽  
Daniel Pakkala ◽  
Pekka Paakkonen ◽  
...  
Keyword(s):  
Neural Network ◽  
Planning And Control ◽  
And Control
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