scholarly journals A Physiological Neural Controller of a Muscle Fiber Oculomotor Plant in Horizontal Monkey Saccades

2014 ◽  
Vol 2014 ◽  
pp. 1-19 ◽  
Author(s):  
Alireza Ghahari ◽  
John D. Enderle
Keyword(s):  
Neural Network ◽  
Muscle Fiber ◽  
Dominant Factor ◽  
Optimal Control Strategy ◽  
Abducens Nucleus ◽  
Neural Controller ◽  
Antagonist Muscle ◽  
The Neural Network ◽  
Time Optimal ◽  
Agonist And Antagonist

A neural network model of biophysical neurons in the midbrain is presented to drive a muscle fiber oculomotor plant during horizontal monkey saccades. Neural circuitry, including omnipause neuron, premotor excitatory and inhibitory burst neurons, long lead burst neuron, tonic neuron, interneuron, abducens nucleus, and oculomotor nucleus, is developed to examine saccade dynamics. The time-optimal control strategy by realization of agonist and antagonist controller models is investigated. In consequence, each agonist muscle fiber is stimulated by an agonist neuron, while an antagonist muscle fiber is unstimulated by a pause and step from the antagonist neuron. It is concluded that the neural network is constrained by a minimum duration of the agonist pulse and that the most dominant factor in determining the saccade magnitude is the number of active neurons for the small saccades. For the large saccades, however, the duration of agonist burst firing significantly affects the control of saccades. The proposed saccadic circuitry establishes a complete model of saccade generation since it not only includes the neural circuits at both the premotor and motor stages of the saccade generator, but also uses a time-optimal controller to yield the desired saccade magnitude.

scholarly journals Investigation of a Multitasking System for Automatic Ship Berthing in Marine Practice Based on an Integrated Neural Controller

Mathematics ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 1167
Author(s):  
Van Suong Nguyen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Coordinate System ◽  
Numerical Simulations ◽  
Neural Controller ◽  
The Neural Network ◽  
Artificial Neural ◽  
Ship Berthing

In this article, a multitasking system is investigated for automatic ship berthing in marine practices, based on artificial neural networks (ANNs). First, a neural network with separate structures in hidden layers is developed, based on a head-up coordinate system. This network is trained once with the berthing data of a ship in an original port to conduct berthing tasks in different ports. Then, on the basis of the developed network, an integrated mechanism including three negative signs is linked to achieve an integrated neural controller. This controller can bring the ship to a berth on each side of the ship in different ports. The whole system has the ability to berth for different tasks without retraining the neural network. Finally, to validate the effectiveness of the proposed system for automatic ship berthing, numerical simulations were performed for berthing tasks, such as different ports, and berthing each side of the ship. The results indicate that the proposed system shows a good performance in automatic ship berthing.

scholarly journals The control to aggregates of pumping stations using a regulator based on a neural network with fuzzy logic

2019 ◽  
Vol 102 ◽  
pp. 03007
Author(s):  
Vladlen Kuznetsov ◽  
Sergey Dyadun ◽  
Valentin Esilevsky
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Simulation Modeling ◽  
Neural Controller ◽  
The Neural Network ◽  
Pumping Stations ◽  
Fuzzy Neural ◽  
Pumping Units ◽  
Theory Of Fuzzy Sets

A pumping station control system is considered using a controller based on a fuzzy logic neural network. The simulation of the classical and fuzzy regulators. The possibility of the implementation of the controller in the form of an adaptive multilayer neural network is shown. The use of the theory of fuzzy sets in combination with the theory of neural networks to create a fuzzy-neural regulator to control pumping units provides a promising approach. Simulation modeling and real operation have shown that fuzzy-logic regulators have a number of advantages over classical regulators, which allow the use of form and limitations. Using the neural network model allows you to add the properties of adaptability and learning. The fuzzy-neural controller for controlling pumping units is promising in terms of efficiency and safety by controlling pumping stations.

A NEURON-BASED TIME-OPTIMAL CONTROLLER OF HORIZONTAL SACCADIC EYE MOVEMENTS

2014 ◽  
Vol 24 (06) ◽  
pp. 1450017 ◽  
Author(s):  
ALIREZA GHAHARI ◽  
JOHN D. ENDERLE
Keyword(s):  
Neural Network ◽  
Neural Activity ◽  
Neural Circuits ◽  
Saccadic Eye Movements ◽  
Neural Circuitry ◽  
Optimal Controller ◽  
Oculomotor Nucleus ◽  
Abducens Nucleus ◽  
Burst Neuron ◽  
Time Optimal

A neural network model of biophysical neurons in the midbrain for controlling oculomotor muscles during horizontal human saccades is presented. Neural circuitry that includes omnipause neuron, premotor excitatory and inhibitory burst neurons, long lead burst neuron, tonic neuron, interneuron, abducens nucleus and oculomotor nucleus is developed to investigate saccade dynamics. The final motoneuronal signals drive a time-optimal controller that stimulates a linear homeomorphic model of the oculomotor plant. To our knowledge, this is the first report on modeling the neural circuits at both premotor and motor stages of neural activity in saccadic systems.

scholarly journals Staring Imaging Real-Time Optimal Control Based on Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Peiyun Li ◽  
Yunfeng Dong ◽  
Hongjue Li
Keyword(s):  
Neural Network ◽  
Optimal Control ◽  
Real Time ◽  
Optimization Method ◽  
Time Optimal Control ◽  
Control Effect ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Time Optimal ◽  
Attitude Controller

In this paper, a real-time optimal attitude controller is designed for staring imaging, and the output command is based on future prediction. First, the mathematical model of staring imaging is established. Then, the structure of the optimal attitude controller is designed. The controller consists of a preprocessing algorithm and a neural network. Constructing the neural network requires training samples generated by optimization. The objective function in the optimization method takes the future control effect into account. The neural network is trained after sample creation to achieve real-time optimal control. Compared with the PID (proportional-integral-derivative) controller with the best combination of parameters, the neural network controller achieves better attitude pointing accuracy and pointing stability.

scholarly journals On the use of neural regulators

2021 ◽  
pp. 53-63
Author(s):  
Alexsander Voevoda ◽  
◽  
Dmitry Romannikov ◽  
Keyword(s):  
Neural Network ◽  
Reinforcement Learning ◽  
Control Systems ◽  
State Vector ◽  
Neural Controller ◽  
Discrete Form ◽  
Learning Methods ◽  
Data Set ◽  
The Neural Network ◽  
Input Error

The application of neural networks for the synthesis of control systems is considered. Examples of synthesis of control systems using methods of reinforcement learning, in which the state vector is involved, are given. And the synthesis of a neural controller for objects with an inaccessible state vector is discussed: 1) a variant using a neural network with recurrent feedbacks; 2) a variant using the input error vector, where each error (except for the first one) enters the input of the neural network passing through the delay element. The disadvantages of the first method include the fact that for such a structure of a neural network it is not possible to apply existing learning methods with confirmation and for training it is required to use a data set obtained, for example, from a previously calculated linear controller. The structure of the neural network used in the second option allows the application of reinforcement learning methods, but the article provides a statement and its proof that for the synthesis of a control system for objects with three or more integrators, a neural network without recurrent connections cannot be used. The application of the above structures is given on examples of the synthesis of control systems for objects 1/s2 and 1/s3 presented in a discrete form.

scholarly journals Grey Wolf Optimizer in Design Process of the Recurrent Wavelet Neural Controller Applied for Two-Mass System

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 177
Author(s):  
Mateusz Zychlewicz ◽  
Radoslaw Stanislawski ◽  
Marcin Kaminski
Keyword(s):  
Neural Network ◽  
Grey Wolf Optimizer ◽  
State Variables ◽  
Grey Wolf ◽  
Neural Controller ◽  
Mass System ◽  
Numerical Tests ◽  
Speed Controller ◽  
The Neural Network ◽  
Oscillation Damping

In this paper, an adaptive speed controller of the electrical drive is presented. The main part of the control structure is based on the Recurrent Wavelet Neural Network (RWNN). The mechanical part of the plant is considered as an elastic connection of two DC machines. Oscillation damping and robustness against parameter changes are achieved using network parameters updates (online). Moreover, the various combinations of the feedbacks from the state variables are considered. The initial weights of the neural network and the additional gains are tuned using a modified version of the Grey Wolf Optimizer. Convergence of the calculation is forced using a new definition. For theoretical analysis, numerical tests are presented. Then, the RWNN is implemented in a dSPACE card. Finally, the simulation results are verified experimentally.

scholarly journals Real-Time Optimal Approach and Capture of ENVISAT Based on Neural Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Hongjue Li ◽  
Yunfeng Dong ◽  
Peiyun Li
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Optimal Control ◽  
Real Time ◽  
Rbf Neural Networks ◽  
Optimal Control Strategy ◽  
Neural Network Controller ◽  
Training Samples ◽  
Time Optimal ◽  
Optimal Approach

A neural network-based controller is developed to enable a chaser spacecraft to approach and capture a disabled Environmental Satellite (ENVISAT). This task is conventionally tackled by framing it as an optimal control problem. However, the optimization of such a problem is computationally expensive and not suitable for onboard implementation. In this work, a learning-based approach is used to rapidly generate the control outputs of the controller based on a series of training samples. These training samples are generated by solving multiple optimal control problems with successive iterations. Then, Radial Basis Function (RBF) neural networks are designed to mimic this optimal control strategy from the generated data. Compared with a traditional controller, the neural network controller is able to generate real-time high-quality control policies by simply passing the input through the feedforward neural network.

Design of a Multivariable Neural Controller and Its Application to Gas Turbines

1997 ◽  
Vol 119 (3) ◽  
pp. 565-567
Author(s):  
Q. Song ◽  
M. J. Grimble
Keyword(s):  
Neural Network ◽  
Gas Turbine ◽  
Discrete Time ◽  
Gas Turbines ◽  
Neural Controller ◽  
Turbine Plant ◽  
The Neural Network ◽  
Gas Turbine Plant ◽  
Multivariable Controller ◽  
Compensation Signal

The algorithm for a multivariable controller using neural network is based on a discrete-time fixed controller and the neural network provides a compensation signal to suppress the nonlinearity. The multivariable neural controller is easy to train and applied to an aircraft gas turbine plant.

scholarly journals FumeBot: A Deep Convolutional Neural Network Controlled Robot

Robotics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 62 ◽  
Author(s):  
Ajith Thomas ◽  
John Hedley
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Home Monitoring ◽  
Raspberry Pi ◽  
Training Method ◽  
Neural Controller ◽  
Supervised Training ◽  
The Neural Network ◽  
The World ◽  
Past State

This paper describes the development of a convolutional neural network for the control of a home monitoring robot (FumeBot). The robot is fitted with a Raspberry Pi for on board control and a Raspberry Pi camera is used as the data feed for the neural network. A wireless connection between the robot and a graphical user interface running on a laptop allows for the diagnostics and development of the neural network. The neural network, running on the laptop, was trained using a supervised training method. The robot was put through a series of obstacle courses to test its robustness, with the tests demonstrating that the controller has learned to navigate the obstacles to a reasonable level. The main problem identified in this work was that the neural controller did not have memory of past actions it took and a past state of the world resulting in obstacle collisions. Options to rectify this issue are suggested.

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