scholarly journals NN-Based Approximate Model Control for the EAF Electrode Regulator System

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Hongge Zhao
Keyword(s):  
Approximate Model ◽  
Equivalent Model ◽  
Neural Network Controller ◽  
Network Controller ◽  
Control Scheme ◽  
Model Control ◽  
System Requirements ◽  
Robust Adaptive ◽  
The Stability ◽  
Nonlinear Control Law

This paper proposes a robust adaptive neural network controller (RANNC) for electrode regulator system. According to the characteristics of electrode regulator system, an affine-like equivalent model is first derived. Then, the nonlinear control law is derived directly based on the affine-like equivalent model identified with neural networks, which avoids complex control development and intensive computation. The control scheme is simple enough that it can be implemented on an automotive microcontroller system, and the performance meets the system requirements. The stability of the system is established by the Lyapunov method. Several simulations illustrate the effectiveness of the controller.

An Adaptive Approximate Model Neural Network Controller for EAF Electrode Regulator System

2013 ◽  
Vol 373-375 ◽  
pp. 1432-1436 ◽  
Author(s):  
Hong Ge Zhao
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Approximate Model ◽  
Equivalent Model ◽  
Nonlinear Controller ◽  
Output Measurement ◽  
Neural Network Controller ◽  
Network Controller ◽  
The Neural Networks ◽  
Robust Adaptive

This paper proposes a robust adaptive neural network controller (RANNC) for electrode regulator system. An equivalent model in affine-like is derived for electrode regulator system. Then, the nonlinear control law is derived directly based on the affine-like equivalent model identified with neural networks, which avoids complex control development and intensive computation. Pretraining is not required and the weights of the neural networks used in adaptive control are directly updated online based on the input-output measurement. The proposed nonlinear controller is verified by computer simulations.

AN ULTRASONIC MOTOR SPEED CONTROL SCHEME BASED ON NEURAL NETWORKS AND ITERATIVE CONTROLLER

2004 ◽  
Vol 01 (03) ◽  
pp. 457-470
Author(s):  
X. H. SHI ◽  
Y. C. LIANG ◽  
X. XU
Keyword(s):  
Neural Networks ◽  
Adaptive Learning ◽  
Speed Control ◽  
Ultrasonic Motor ◽  
Random Disturbance ◽  
Motor Speed ◽  
Neural Network Controller ◽  
Learning Rates ◽  
Network Controller ◽  
Control Scheme

An ultrasonic motor speed control scheme is presented in this paper based on neural networks and iterative controller. Suitable ranges of the adaptive learning rates of neural network controller are presented through the theoretical analysis on the proposed model, which could guarantee its stability. The convergence of iterative controller is also discussed. Numerical results show that the control scheme is effective for various kinds of reference speeds of ultrasonic motors. Comparisons with the existing method show that the precision of control could be increased using the proposed method. Simulations also show that the proposed scheme is fairly robust against random disturbance to the control variables.

scholarly journals A Robust Control Scheme for Renewable-Based Distributed Generators Using Artificial Hydrocarbon Networks

Energies ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 1896 ◽  
Author(s):  
Antonio Rosales ◽  
Pedro Ponce ◽  
Hiram Ponce ◽  
Arturo Molina
Keyword(s):  
Robust Control ◽  
Control Algorithm ◽  
Solar Panels ◽  
Lyapunov Techniques ◽  
Distributed Generators ◽  
Stability Robustness ◽  
Network Controller ◽  
Control Scheme ◽  
Power Regulation ◽  
The Stability

Distributed generators (DGs) based on renewable energy systems such as wind turbines, solar panels, and storage systems, are key in transforming the current electric grid into a green and sustainable network. These DGs are called inverter-interfaced systems because they are integrated into the grid through power converters. However, inverter-interfaced systems lack inertia, deteriorating the stability of the grid as frequency and voltage oscillations emerge. Additionally, when DGs are connected to the grid, its robustness against unbalanced conditions must to be ensured. This paper presents a robust control scheme for power regulation in DGs, which includes inertia and operates under unbalanced conditions. The proposed scheme integrates a robust control algorithm to ensured power regulation, despite unbalanced voltages. The control algorithm is an artificial hydrocarbon network controller, which is a chemically-inspired technique, based on carbon networks, that provides stability, robustness, and accuracy. The robustness and stability of the proposed control scheme are tested using Lyapunov techniques. Simulation, considering one- and three-phase voltage sags, is executed to validate the performance of the control scheme.

Singular perturbation based neuro-H∞ control scheme for a manipulator with flexible links and joints

Robotica ◽  
2005 ◽  
Vol 24 (2) ◽  
pp. 151-161 ◽  
Author(s):  
B. Subudhi ◽  
A. S. Morris
Keyword(s):  
Singular Perturbation ◽  
Inverse Dynamics ◽  
Perturbation Technique ◽  
Singular Perturbation Technique ◽  
Flexible Links ◽  
Neural Network Controller ◽  
Network Controller ◽  
Control Scheme ◽  
Improved Performance ◽  
Slow Subsystem

A novel composite control scheme for a manipulator with flexible links and joints is presented that uses the singular perturbation technique (SPT) to divide the manipulator dynamics into reduced order slow and fast subsystems. A neural network controller is then applied for the slow subsystem and a state-feedback H∞ controller for the fast subsystem. Results are presented that demonstrate improved performance over an alternative SPT-based controller that uses inverse dynamics and LQR controllers.

Design of an Adaptive Neural Network Controller for Robot Manipulators

2013 ◽  
Vol 787 ◽  
pp. 876-880 ◽  
Author(s):  
Jing Ma ◽  
Xiao Ming Ji ◽  
Hong Yu Wu
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Variable Structure ◽  
Tracking Errors ◽  
Robot Trajectory ◽  
Neural Network Controller ◽  
Network Controller ◽  
Model Control ◽  
Lyapunov Function Method ◽  
Adaptive Laws

This paper brings forward a kind of adaptive neural-sliding model control schemes for uncertain robot trajectory tracking. The first scheme consists of a PD feedback and a dynamic compensator which is composed of RBF neural network and variable structure. The adaptive laws of Network weights are based on Lyapunov function method. This controller can guarantee stability of closed-loop system and asymptotic convergence of tracking errors.

scholarly journals Adaptive Neural Gradient Descent Control for a Class of Nonlinear Dynamic Systems with Chaotic Phenomenon

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Xiujuan Liu ◽  
Tian Lan
Keyword(s):  
Gradient Descent ◽  
Chaotic Systems ◽  
Controller Design ◽  
Closed Loop System ◽  
Neural Network Controller ◽  
Network Controller ◽  
Gradient Descent Algorithm ◽  
The Neural Networks ◽  
The Stability ◽  
Adaptation Law

A neural network controller design is studied for a class of nonlinear chaotic systems with uncertain parameters. Because the chaos phenomena are often in this class of systems, it is indispensable to control this class of systems. At the same time, due to the presence of uncertainties in the chaotic systems, it results in the difficulties of the controller design. The neural networks are employed to estimate the uncertainties of the systems and a controller is designed to overcome the chaos phenomena. The main contribution of this paper is that the adaptation law can be determined via the gradient descent algorithm to minimize a cost function of error. It can prove the stability of the closed-loop system. The numerical simulation is specified to pinpoint the validation of the approach.

Dual Adaptive Neural Network Controller for Underactuated Systems

Robotica ◽  
2021 ◽  
pp. 1-18
Author(s):  
Seyed Hassan Zabihifar ◽  
Hamed Navvabi ◽  
Arkady Semenovich Yushchenko
Keyword(s):  
Neural Network ◽  
Adaptive Controller ◽  
Underactuated Systems ◽  
Adaptive Neural Network ◽  
Neural Network Controller ◽  
Network Controller ◽  
Control Scheme ◽  
Equivalent Control ◽  
Inertia Wheel Pendulum ◽  
The Given

SUMMARY A new stable adaptive controller based on a neural network for underactuated systems is proposed in this paper. The control scheme has been developed for two underactuated systems as examples. The Furuta pendulum and the Inertia Wheel Pendulum (IWP) have been examined in this paper. The presented approach aims to address the control problem of the given system in swing up, stabilization, and disturbance rejection. To avoid oscillations, two adaptive neural networks (ANNs) are implemented. The first one is used to approximate the equivalent control online and the second one to minimize the oscillations.

Engineering Calculation And Algorithm Of Adaptation Of Parameters Of A Neuro-Fuzzy Controller

2021 ◽  
Vol 03 (09) ◽  
pp. 41-49
Author(s):  
I.H. Siddikov ◽  
◽  
P.I. Kalandarov ◽  
D.B., Yadgarova ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Fuzzy Controller ◽  
Rule Base ◽  
Transient Time ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller ◽  
Neuro Fuzzy ◽  
Control Scheme

As part of the study, a control scheme with the adaptation of the coefficients of the neuron-fuzzy regulator implemented. The area difference method used as a training method for the network. It improved by adding a rule base, which allows choosing the optimal learning rate for individual neurons of the neural network. The neural network controller applied as a superstructure of the PID controller in the process control scheme. The dynamic object can function in different modes. This technological process operates in different modes in terms of loading and temperature setpoints. Because of experiments, the power consumption and the amount of time required maintaining the same absorption process, using a conventional PID controller and a neural-network controller evaluated. It concluded that the neuro-fuzzy controller with a superstructure reduced the transient time by 19%.

scholarly journals Robust adaptive controller for wheel mobile robot with disturbances and wheel slips

2021 ◽  
Vol 11 (1) ◽  
pp. 336
Author(s):  
Nga Thi-Thuy Vu ◽  
Loc Xuan Ong ◽  
Nam Hai Trinh ◽  
Sen Thi Huong Pham
Keyword(s):  
Mobile Robot ◽  
Adaptive Controller ◽  
Lyapunov Theory ◽  
Control Loops ◽  
Proposed Model ◽  
Control Scheme ◽  
System Uncertainties ◽  
Robust Adaptive ◽  
The Stability ◽  
Adaptive Control Algorithm

In this paper an observer based adaptive control algorithm is built for wheel mobile robot (WMR) with considering the system uncertainties, input disturbances, and wheel slips. Firstly, the model of the kinematic and dynamic loops is shown with presence of the disturbances and system uncertainties. Next, the adaptive controller for nonlinear mismatched disturbance systems based on the disturbances observer is presented in detail. The controller includes two parts, the first one is for the stability purpose and the later is for the disturbances compensation. After that this control scheme is applied for both two loops of the system. In this paper, the stability of the closed system which consists of two control loops and the convergence of the observers is mathematically analysed based on the Lyapunov theory. Moreover, the proposed model does not require the complex calculation so it is easy for the implementation. Finally, the simulation model is built for presented method and the existed one to verify the correctness and the effectiveness of the proposed scheme. The simulation results show that the introduced controller gives the good performances even that the desired trajectory is complicated and the working condition is hard.

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