Adaptive Neural Network Compensating Control for Coordinated Motion of Space-Based Robot System With Uncertain Parameters and External Disturbances

2009 ◽  
Author(s):  
Chen Zhiyong ◽  
Chen Li
Keyword(s):  
Neural Network ◽  
System Stability ◽  
Uncertain Parameters ◽  
Coordinated Motion ◽  
Adaptive Neural Network ◽  
Robot System ◽  
External Disturbances ◽  
Neural Network Controller ◽  
The Neural Network ◽  
On Line

The control problem of space-based robot system with uncertain parameters and external disturbances is considered. With Lagrangian formulation and augmentation approach, the dynamic equations of space-based robot system in workspace are derived. Based on the results, an adaptive neural network compensating control scheme for coordinated motion between the base’s attitude and end-effector of space-based robot system is developed. It is based on the inertia-related method, and incorporates a neural network controller to compensate the uncertainties. The closed-loop system stability with the neural network adapted on-line is discussed in detail through the Lyapunov stability approach. Comparing with many adaptive and robust control schemes, the controller proposed does not require one to determine the regression matrix for space robot system and then avoids tedious computations. Numerical simulations are provided to show the effectiveness of the approach.

Control of Pneumatic Servo System Based on Neural Network PID Algorithm

2013 ◽  
Vol 457-458 ◽  
pp. 1344-1347 ◽  
Author(s):  
Qin Hui Gong
Keyword(s):  
Neural Network ◽  
Pid Control ◽  
Learning Rule ◽  
Servo System ◽  
External Disturbances ◽  
Pid Algorithm ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Pneumatic Servo System ◽  
Adaptive Ability

The pneumatic servo system has characteristics of nonlinear, time-variant, large parameter variations and external disturbances, which is difficult to control. The conventional PID control is not suitable for the variable parameters of the controlled object, external disturbances. In this paper, the neural network controller combined with PID control is used to control the pneumatic servo system, and the structure diagram, algorithm and learning rule of the single neuron adaptive PID controller are put forward. The results show that,compared with the traditional PID control, the controller has significantly improved the control performance of system, Namely, the system has faster computational speed (real-time), stronger robustness and better adaptive ability.

Research on Intelligent Variable Frequency of Air-Conditioner Control System Based on Fuzzy Control

2011 ◽  
Vol 110-116 ◽  
pp. 4076-4084
Author(s):  
Hai Cun Du
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fuzzy Control ◽  
Fuzzy Neural Network ◽  
Air Conditioner ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller ◽  
Fuzzy Neural ◽  
Fuzzy Control Strategy

In this paper, we determine the fuzzy control strategy of inverter air conditioner, the fuzzy control model structure, the neural network and fuzzy control technology, structural design of the fuzzy neural network controller as well as the neural network predictor FNNC NNP. Simulation results show that the fuzzy neural network controller can control the accuracy greatly improved the compressor, and the control system has strong adaptability to achieve a truly intelligent; model of the controller design and implementation of technology are mainly from the practical point of view, which is practical and feasible.

scholarly journals An Intelligent Adaptive Neural Network Controller for a Direct Torque Controlled eCAR Propulsion System

2021 ◽  
Vol 12 (1) ◽  
pp. 44
Author(s):  
Gururaj Banda ◽  
Sri Gowri Kolli
Keyword(s):  
Neural Network ◽  
Pulse Width Modulation ◽  
Simulation Software ◽  
Propulsion System ◽  
Torque Control ◽  
Matlab Simulation ◽  
Time Data ◽  
Adaptive Neural Network ◽  
Adaptive Controllers ◽  
Neural Network Controller

This article deals with an intelligent adaptive neural network (ANN) controller for a direct torque controlled (DTC) electric vehicle (EV) propulsion system. With the realization of artificial intelligence (AI) conferred adaptive controllers, the torque control of an electric car (eCAR) propulsion motor can be achieved by estimating the stator reference flux voltage used to synthesize the space vector pulse width modulation (SVPWM) for a DTC scheme. The proposed ANN tool optimizes the parameters of a proportional integral (PI) controller with real-time data and offers splendid dynamic stability. The response of an ANN controller is examined over standard drive cycles to validate the performance of an eCAR in terms of drive range and energy efficiency using MATLAB simulation software.

scholarly journals Neural feedback linearization adaptive control for affine nonlinear systems based on neural network estimator

2011 ◽  
Vol 8 (3) ◽  
pp. 307-323 ◽  
Author(s):  
Mohamed Bahita ◽  
Khaled Belarbi
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Feedback Linearization ◽  
Pendulum System ◽  
Rbf Network ◽  
Inverted Pendulum System ◽  
Neural Network Controller ◽  
Control Gain ◽  
On Line ◽  
Neural Feedback

In this work, we introduce an adaptive neural network controller for a class of nonlinear systems. The approach uses two Radial Basis Functions, RBF networks. The first RBF network is used to approximate the ideal control law which cannot be implemented since the dynamics of the system are unknown. The second RBF network is used for on-line estimating the control gain which is a nonlinear and unknown function of the states. The updating laws for the combined estimator and controller are derived through Lyapunov analysis. Asymptotic stability is established with the tracking errors converging to a neighborhood of the origin. Finally, the proposed method is applied to control and stabilize the inverted pendulum system.

scholarly journals Computer image processing and neural network technology for boiler thermal energy diagnosis

2020 ◽  
Vol 24 (5 Part B) ◽  
pp. 3059-3068
Author(s):  
Qinghong Wu
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Thermal Energy ◽  
Processing System ◽  
Heat Energy ◽  
Image Feature ◽  
The Neural Network ◽  
Computer Image Processing ◽  
On Line ◽  
Parameter Values

The paper uses the flame image processing technology to diagnose the furnace flame combustion achieve the measurement of boiler heat energy. The paper obtains the combustion image of the flame image processing system, and extracts the flame image characteristics of the boiler thermal energy diagnosis, constructs the neural network model of the boiler thermal energy diagnosis, and trains and tests the extracted flame image feature parameter values as the input of the neural network. A rough diagnosis of the boiler?s thermal energy is obtained while predicting the state of combustion. According to the research results, a boiler thermal energy diagnosis system was designed and tested on the boiler of 200 MW unit. The experimental results confirmed the applicability of the system, which can realize on-line monitoring of boiler heat energy and evaluate the combustion situation.

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