scholarly journals Performance Optimization of Ship Course Via Artificial Neural Network and Command Filtered Cdm-Backstepping Controller

2019 ◽  
Vol 16 (1) ◽  
pp. 5-10
Author(s):  
Fouad Haouari ◽  
Rabah Gouri ◽  
Nourdine Bali ◽  
Mohamed Tadjine ◽  
Mohamed Seghir Boucherit
Keyword(s):  
Neural Network ◽  
Performance Optimization ◽  
System Stability ◽  
Stable Convergence ◽  
Diagram Method ◽  
First Order ◽  
Coefficient Diagram Method ◽  
Backstepping Controller ◽  
High Control ◽  
Analytic Derivation

Abstract This paper proposes a robust nonlinear ship course controller, under the control of which the system is globally asymptotically stabilized with high control quality. The proposed controller is synthesized by combining coefficient diagram method and command filtered backstepping based on first order filter to avoid the complex analytic derivation of the virtual control, the controller parameter are tuned using radial basis function neural network, It can not only obtain a higher accuracy in ship course controlling, but also infinitely approach the nonlinear system with quicker and more stable convergence. The simulation results illustrate that the projected controller shortens the settling time evidently with good system stability. It has a better performance than the traditional controllers.

scholarly journals A Coefficient Diagram Method Controller with Backstepping Methodology for Robotic Manipulators

2015 ◽  
Vol 66 (5) ◽  
pp. 270-276 ◽  
Author(s):  
Fouad Haouari ◽  
Bali Nourdine ◽  
Mohamed Segir Boucherit ◽  
Mohamed Tadjine
Keyword(s):  
Robotic Manipulators ◽  
Control Law ◽  
Control Procedure ◽  
Parametric Uncertainties ◽  
The Novel ◽  
External Disturbances ◽  
Diagram Method ◽  
Coefficient Diagram Method ◽  
Backstepping Controller ◽  
Puma Robot

AbstractA new robust control procedure for robot manipulators is proposed in this paper. Coefficients diagram method controllers CDM and Backstepping methodology are combined to create the novel control law. Two steps of backstepping on the resulting system are used to design a nonlinear CDM-Backstepping controller. Simulations on a PUMA robot including external disturbances, parametric uncertainties and noises are performed to show the effectiveness and feasibility of the proposed method.

scholarly journals Optimization of Optical Machine Structure by Backpropagation Neural Network Based on Particle Swarm Optimization and Bayesian Regularization Algorithms

Materials ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 2998
Author(s):  
Xinyong Zhang ◽  
Liwei Sun
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Prediction Model ◽  
Bayesian Regularization ◽  
Backpropagation Neural Network ◽  
Swarm Optimization ◽  
Modal Frequency ◽  
Supporting Structure ◽  
First Order ◽  
Machine Structure

Fit of the highly nonlinear functional relationship between input variables and output response is important and challenging for the optical machine structure optimization design process. The backpropagation neural network method based on particle swarm optimization and Bayesian regularization algorithms (called BMPB) is proposed to solve this problem. A prediction model of the mass and first-order modal frequency of the supporting structure is developed using the supporting structure as an example. The first-order modal frequency is used as the constraint condition to optimize the lightweight design of the supporting structure’s mass. Results show that the prediction model has more than 99% accuracy in predicting the mass and the first-order modal frequency of the supporting structure, and converges quickly in the supporting structure’s mass-optimization process. The supporting structure results demonstrate the advantages of the method proposed in the article in terms of high accuracy and efficiency. The study in this paper provides an effective method for the optimized design of optical machine structures.

System of Annealing Furnace Temperature Control Based on Neural Network

2014 ◽  
Vol 1044-1045 ◽  
pp. 881-884
Author(s):  
Xin Wang ◽  
He Pan
Keyword(s):  
Neural Network ◽  
Temperature Control ◽  
Bp Neural Network ◽  
Pid Controller ◽  
Nonlinear Approximation ◽  
Annealing Furnace ◽  
Furnace Temperature ◽  
Control Precision ◽  
High Control ◽  
Adaptive Ability

In the thesis the adaptive ability of neural network strong and good nonlinear approximation ability, A controller is designed based on BP neural network by the adaptive ability of neural network strong and good nonlinear approximation ability in this paper, this method changed defect of the usual PID controller that parameters of annealing furnace condition are not easy set and the ability to adapt is poor. The new method is not only has good stability, but also has high control precision and strong adaptability.

scholarly journals Neural network models of the tactile system develop first-order units with spatially complex receptive fields

2017 ◽  
Author(s):  
Charlie W. Zhao ◽  
Mark J. Daley ◽  
J. Andrew Pruszynski
Keyword(s):  
Neural Network ◽  
Network Performance ◽  
Receptive Fields ◽  
Network Models ◽  
Network Architectures ◽  
Learning Tools ◽  
Neural Network Models ◽  
First Order ◽  
Wide Range ◽  
Tactile System

AbstractFirst-order tactile neurons have spatially complex receptive fields. Here we use machine learning tools to show that such complexity arises for a wide range of training sets and network architectures, and benefits network performance, especially on more difficult tasks and in the presence of noise. Our work suggests that spatially complex receptive fields are normatively good given the biological constraints of the tactile periphery.

scholarly journals Design and Experimental Investigations on NOx Emission Control Using FOCDM (Fractional-Order-Based Coefficient Diagram Method)-PIλDµ Controller

2020 ◽  
Vol 53 (5) ◽  
pp. 695-703
Author(s):  
Maheswari Chennippan ◽  
Priyanka E. Bhaskaran ◽  
Thangavel Subramaniam ◽  
Balasubramaniam Meenakshipriya ◽  
Kasilingam Krishnamurthy ◽  
...  
Keyword(s):  
Fractional Order ◽  
Packed Column ◽  
Experimental Studies ◽  
Pso Algorithm ◽  
Experimental Investigations ◽  
Nox Removal ◽  
Diagram Method ◽  
Coefficient Diagram Method ◽  
Fractional Order Controller ◽  
G Ratio

This paper aims to explore experimental studies on the NOx removal process by using pilot plant packed column experimental hardware. Physical modeling based on chemical absorption equations is used to estimate the diameter concerning the height and L/G ratio. Hydrogen peroxide is used as the additive for achieving high NOx removal efficiency. The absorbent entering into the packed column has been controlled by varying its flow rate through the fractional order controller. The FOCDM-PIλDµ controller tuning parameters such as KP, τI, τD are determined using CDM (Coefficient Diagram Method) PID control strategy and the additional parameters of FOCDM-PIλDµ controller such as λ and µ are determined based on the PSO algorithm. The comparative analysis is performed with classical controllers like ZN-PID along with the CDM-PID controllers.

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