Longitudinal Control for AUV with Self-Adaptive Learning Law

2013 ◽  
Vol 819 ◽  
pp. 259-265
Author(s):  
Xiu Jun Sun ◽  
Yan Yang
Keyword(s):  
Neural Network ◽  
Adaptive Learning ◽  
Attitude Control ◽  
Autonomous Underwater Vehicle ◽  
Linear Equations ◽  
Back Propagation ◽  
Mathematic Model ◽  
Back Propagation Algorithm ◽  
Neural Network Controller ◽  
Self Adaptive

A mini AUV (Autonomous Underwater Vehicle) with cross shaped rudders and one single thruster is presented, which features high maneuverability due to the intelligent control algorithm. A single variable PID neural network controller is also proposed, which is utilized to maintain attitude for the vehicle. In order to testify feasibility of the control methodology, a spatial motion mathematic model is constructed and linear equations that indicate the relation between attitude angles of vehicle and deflection angles of rudders is deduced firstly. Subsequently, the neural network PID controller is developed according to the deduced equations and the attitude control simulation of the vehicle with this controller is conducted. Taking actual and desired attitude angles of the vehicle as input and deflection angles of the rudders as output, this controller performs self-adaptive update for 9 synaptic weights through back-propagation algorithm and employs the converged weights to calculate the appropriate deflection angle of each rudder.

Solving polynomial systems using a fast adaptive back propagation-type neural network algorithm

2017 ◽  
Vol 29 (2) ◽  
pp. 301-337 ◽  
Author(s):  
K. GOULIANAS ◽  
A. MARGARIS ◽  
I. REFANIDIS ◽  
K. DIAMANTARAS
Keyword(s):  
Neural Network ◽  
Adaptive Learning ◽  
Linear Equations ◽  
Back Propagation ◽  
Polynomial Systems ◽  
Optimization Techniques ◽  
Back Propagation Algorithm ◽  
Network Activation ◽  
Non Linear ◽  
Non Linear Equations

This paper proposes a neural network architecture for solving systems of non-linear equations. A back propagation algorithm is applied to solve the problem, using an adaptive learning rate procedure, based on the minimization of the mean squared error function defined by the system, as well as the network activation function, which can be linear or non-linear. The results obtained are compared with some of the standard global optimization techniques that are used for solving non-linear equations systems. The method was tested with some well-known and difficult applications (such as Gauss–Legendre 2-point formula for numerical integration, chemical equilibrium application, kinematic application, neuropsychology application, combustion application and interval arithmetic benchmark) in order to evaluate the performance of the new approach. Empirical results reveal that the proposed method is characterized by fast convergence and is able to deal with high-dimensional equations systems.

A Feed forward Neural Network MPPT Control Strategy Applied to a Modified Cuk Converter

2016 ◽  
Vol 6 (4) ◽  
pp. 1421
Author(s):  
Mohamed Tahar Makhloufi ◽  
Yassine Abdessemed ◽  
Mohamed Salah Khireddine
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Control Strategy ◽  
Back Propagation ◽  
Network Control ◽  
Back Propagation Algorithm ◽  
Feed Forward Neural Network ◽  
Cuk Converter ◽  
Neural Network Controller ◽  
Artificial Neural

<p class="References">This paper presents an intelligent control strategy that uses a feedforward artificial neural network in order to improve the performance of the MPPT (Maximum Power Point Tracker) MPPT photovoltaic (PV) power system based on a modified Cuk converter. The proposed neural network control (NNC) strategy is designed to produce regulated variable DC output voltage. The mathematical model of Cuk converter and artificial neural network algorithm is derived. Cuk converter has some advantages compared to other type of converters. However the nonlinearity characteristic of the Cuk converter due to the switching technique is difficult to be handled by conventional controller. To overcome this problem, a neural network controller with online learning back propagation algorithm is developed. The NNC designed tracked the converter voltage output and improve the dynamic performance regardless load disturbances and supply variations. The proposed controller effectiveness during dynamic transient response is then analyze and verified using MATLAB-Simulink. Simulation results confirm the excellent performance of the proposed NNC.</p>

scholarly journals A Feed forward Neural Network MPPT Control Strategy Applied to a Modified Cuk Converter

2016 ◽  
Vol 6 (4) ◽  
pp. 1421
Author(s):  
Mohamed Tahar Makhloufi ◽  
Yassine Abdessemed ◽  
Mohamed Salah Khireddine
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Control Strategy ◽  
Back Propagation ◽  
Network Control ◽  
Back Propagation Algorithm ◽  
Feed Forward Neural Network ◽  
Cuk Converter ◽  
Neural Network Controller ◽  
Artificial Neural

<p class="References">This paper presents an intelligent control strategy that uses a feedforward artificial neural network in order to improve the performance of the MPPT (Maximum Power Point Tracker) MPPT photovoltaic (PV) power system based on a modified Cuk converter. The proposed neural network control (NNC) strategy is designed to produce regulated variable DC output voltage. The mathematical model of Cuk converter and artificial neural network algorithm is derived. Cuk converter has some advantages compared to other type of converters. However the nonlinearity characteristic of the Cuk converter due to the switching technique is difficult to be handled by conventional controller. To overcome this problem, a neural network controller with online learning back propagation algorithm is developed. The NNC designed tracked the converter voltage output and improve the dynamic performance regardless load disturbances and supply variations. The proposed controller effectiveness during dynamic transient response is then analyze and verified using MATLAB-Simulink. Simulation results confirm the excellent performance of the proposed NNC.</p>

scholarly journals Adaptive Inverse Neural Network Based DC Motor Speed and Position Control Using FPGA

2018 ◽  
Vol 11 (3) ◽  
pp. 71-78
Author(s):  
Aula N. Abd
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Position Control ◽  
Back Propagation ◽  
Dc Motor ◽  
Motor Speed ◽  
Back Propagation Algorithm ◽  
Neural Network Controller ◽  
Network Controller ◽  
Field Programmable

In this research two types of controllers are designed in order to control the speed and position of DC motor. The first one is a conventional PID controller and the other is an intelligent Neural Network (NN) controller that generate a control signal DC motor. Due to nonlinear parameters and movable laborers such saturation and change in load a conventional PID controller is not efficient in such application; therefore neural controller is proposed in order to decreasing the effect of these parameter and improve system performance. The proposed intelligent NN controller is adaptive inverse neural network controller designed and implemented on Field Programmable Gate Array (FPGA) board. This NN is trained by Levenberg-Marquardt back propagation algorithm. After implementation on FPGA, the response appear completely the same as simulation response before implementation that mean the controller based on FPGA is very nigh to software designed controller. The controllers designed by both m-file and Simulink in MATLAB R2012a version 7.14.0.

scholarly journals Neural network controller based sequential switch cascaded H-bridge multilevel inverter

2018 ◽  
Vol 7 (2.8) ◽  
pp. 592
Author(s):  
M Ramya ◽  
P Usha Rani ◽  
G Ganesan @ Subramanian ◽  
K Ramash Kumar
Keyword(s):  
Neural Network ◽  
Power Loss ◽  
Back Propagation ◽  
Multilevel Inverter ◽  
Back Propagation Algorithm ◽  
Neural Network Controller ◽  
Network Controller ◽  
Cascaded Multilevel Inverter ◽  
Hybrid Circuit ◽  
Maximum Voltage

This paper presents a novel cascaded multilevel inverter structure with reduced devices. This structure is termed as sequential switch cascaded multilevel inverter. The basic asymmetrical hybrid circuit is described and is capable of generating 17 voltage levels. The various modes of deriving 17 levels are explained and the proposed topology is compared with existing topologies in various aspects. Neural network controller can be used to generate the gating pulses. The algorithm can be trained online by using back propagation algorithm and also an algorithm to determine the number of levels, maximum voltage ratings and power loss is explained. The simulation can be done by MATLAB Simulink.

scholarly journals Deep neural networks using a single neuron: folded-in-time architecture using feedback-modulated delay loops

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Florian Stelzer ◽  
André Röhm ◽  
Raul Vicente ◽  
Ingo Fischer ◽  
Serhiy Yanchuk
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Neural Network ◽  
Single Neuron ◽  
Deep Neural Networks ◽  
Back Propagation ◽  
Local Network ◽  
Multiple Time ◽  
Learning Tools ◽  
Back Propagation Algorithm

AbstractDeep neural networks are among the most widely applied machine learning tools showing outstanding performance in a broad range of tasks. We present a method for folding a deep neural network of arbitrary size into a single neuron with multiple time-delayed feedback loops. This single-neuron deep neural network comprises only a single nonlinearity and appropriately adjusted modulations of the feedback signals. The network states emerge in time as a temporal unfolding of the neuron’s dynamics. By adjusting the feedback-modulation within the loops, we adapt the network’s connection weights. These connection weights are determined via a back-propagation algorithm, where both the delay-induced and local network connections must be taken into account. Our approach can fully represent standard Deep Neural Networks (DNN), encompasses sparse DNNs, and extends the DNN concept toward dynamical systems implementations. The new method, which we call Folded-in-time DNN (Fit-DNN), exhibits promising performance in a set of benchmark tasks.

scholarly journals Influence of hinge length and distribution number on the camber and cornering properties of a non-pneumatic tire

2020 ◽  
Vol 12 (12) ◽  
pp. 168781402098468
Author(s):  
Xianbin Du ◽  
Youqun Zhao ◽  
Yijiang Ma ◽  
Hongxun Fu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Adaptive Learning ◽  
High Speed ◽  
Back Propagation ◽  
Lateral Force ◽  
Learning Ability ◽  
Vehicle Performance ◽  
Neural Network Theory ◽  
Bp Neural Network Model

The camber and cornering properties of the tire directly affect the handling stability of vehicles, especially in emergencies such as high-speed cornering and obstacle avoidance. The structural and load-bearing mode of non-pneumatic mechanical elastic (ME) wheel determine that the mechanical properties of ME wheel will change when different combinations of hinge length and distribution number are adopted. The camber and cornering properties of ME wheel with different hinge lengths and distributions were studied by combining finite element method (FEM) with neural network theory. A ME wheel back propagation (BP) neural network model was established, and the additional momentum method and adaptive learning rate method were utilized to improve BP algorithm. The learning ability and generalization ability of the network model were verified by comparing the output values with the actual input values. The camber and cornering properties of ME wheel were analyzed when the hinge length and distribution changed. The results showed the variation of lateral force and aligning torque of different wheel structures under the combined conditions, and also provided guidance for the matching of wheel and vehicle performance.

scholarly journals Using Artificial Neural Networks in Predicting the Level of Stress among Military Conscripts

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 626
Author(s):  
Svajone Bekesiene ◽  
Rasa Smaliukiene ◽  
Ramute Vaicaitiene
Keyword(s):  
Neural Network ◽  
Transfer Functions ◽  
Back Propagation ◽  
Armed Forces ◽  
Activation Function ◽  
Optimal Number ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Classification Rate ◽  
Artificial Neural

The present study aims to elucidate the main variables that increase the level of stress at the beginning of military conscription service using an artificial neural network (ANN)-based prediction model. Random sample data were obtained from one battalion of the Lithuanian Armed Forces, and a survey was conducted to generate data for the training and testing of the ANN models. Using nonlinearity in stress research, numerous ANN structures were constructed and verified to limit the optimal number of neurons, hidden layers, and transfer functions. The highest accuracy was obtained by the multilayer perceptron neural network (MLPNN) with a 6-2-2 partition. A standardized rescaling method was used for covariates. For the activation function, the hyperbolic tangent was used with 20 units in one hidden layer as well as the back-propagation algorithm. The best ANN model was determined as the model that showed the smallest cross-entropy error, the correct classification rate, and the area under the ROC curve. These findings show, with high precision, that cohesion in a team and adaptation to military routines are two critical elements that have the greatest impact on the stress level of conscripts.

COMPUTING AN ADAPTIVE MESH IN FLUID PROBLEMS USING NEURAL NETWORK AND GENETIC ALGORITHM WITH ADAPTIVE RELAXATION

2008 ◽  
Vol 17 (06) ◽  
pp. 1089-1108 ◽  
Author(s):  
NAMEER N. EL. EMAM ◽  
RASHEED ABDUL SHAHEED
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Numerical Experiments ◽  
Back Propagation ◽  
Adaptive Mesh ◽  
Learning System ◽  
Back Propagation Algorithm ◽  
Adaptive Smoothing ◽  
Different Types ◽  
Optimal Values

A method based on neural network with Back-Propagation Algorithm (BPA) and Adaptive Smoothing Errors (ASE), and a Genetic Algorithm (GA) employing a new concept named Adaptive Relaxation (GAAR) is presented in this paper to construct learning system that can find an Adaptive Mesh points (AM) in fluid problems. AM based on reallocation scheme is implemented on different types of two steps channels by using a three layer neural network with GA. Results of numerical experiments using Finite Element Method (FEM) are discussed. Such discussion is intended to validate the process and to demonstrate the performance of the proposed learning system on three types of two steps channels. It appears that training is fast enough and accurate due to the optimal values of weights by using a few numbers of patterns. Results confirm that the presented neural network with the proposed GA consistently finds better solutions than the conventional neural network.

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