Design of Neural Network Controller for Space Robot with Flexible Manipulators

2013 ◽  
Vol 394 ◽  
pp. 393-397
Author(s):  
Jing Ma ◽  
Wen Hui Zhang ◽  
Zhi Hua Zhu
Keyword(s):  
Neural Network ◽  
Pid Control ◽  
Control Method ◽  
System Optimization ◽  
Space Robot ◽  
Learning Ability ◽  
Flexible Manipulators ◽  
Neural Network Controller ◽  
Non Linear System ◽  
Self Learning

Neural network self-learning optimization PID control algorithm is put forward for free-floating space robot with flexible manipulators. Firstly, dynamics model of space flexible robot is established, then, neural network with good learning ability is used to approach non-linear system. Optimization algorithm of network weights is designed to speed up the learning speed and the adjustment velocity. Error function is offered by PID controller. The neural network self-learning PID control method can improve the control precision.

Adaptive Control for NC Position Table Base on Radical Basis Function Neural Network

2013 ◽  
Vol 404 ◽  
pp. 699-703 ◽  
Author(s):  
Jing Ma ◽  
Hong Yu Wu ◽  
Xiao Ming Ji ◽  
Yan Lei
Keyword(s):  
Neural Network ◽  
Control Method ◽  
Rbf Neural Network ◽  
System Optimization ◽  
Learning Ability ◽  
Control Precision ◽  
Learning Speed ◽  
Speed Up ◽  
Good Learning ◽  
Non Linear System

Dynamic optimization control algorithm is put forward for X-Y position table. Firstly, dynamics model of X-Y position table is established, then, RBF neural network with good learning ability is used to approach non-linear system. Optimization algorithm of network weights is designed to speed up the learning speed and the adjustment velocity. The control method is more effective to improve the control precision and has a good application value.

scholarly journals Adaptive robust control for free-floating space robot with unknown uncertainty based on neural network

2018 ◽  
Vol 15 (6) ◽  
pp. 172988141881151
Author(s):  
Zhang Wenhui ◽  
Li Hongsheng ◽  
Ye Xiaoping ◽  
Huang Jiacai ◽  
Huo Mingying
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Robust Control ◽  
Control Method ◽  
External Disturbance ◽  
Adaptive Robust Control ◽  
Lyapunov Theory ◽  
Space Robot ◽  
Neural Network Controller ◽  
The Stability

It is difficult to obtain a precise mathematical model of free-floating space robot for the uncertain factors, such as current measurement technology and external disturbance. Hence, a suitable solution would be an adaptive robust control method based on neural network is proposed for free-floating space robot. The dynamic model of free-floating space robot is established; a computed torque controller based on exact model is designed, and the controller can guarantee the stability of the system. However, in practice, the mathematical model of the system cannot be accurately obtained. Therefore, a neural network controller is proposed to approximate the unknown model in the system, so that the controller avoids dependence on mathematical models. The adaptive learning laws of weights are designed to realize online real-time adjustment. The adaptive robust controller is designed to suppress the external disturbance and compensate the approximation error and improve the robustness and control precision of the system. The stability of closed-loop system is proved based on Lyapunov theory. Simulations tests verify the effectiveness of the proposed control method and are of great significance to free-floating space robot.

Application of PID Neural Network Controller on Engine Throttle System

2012 ◽  
Vol 220-223 ◽  
pp. 939-942
Author(s):  
Huan Xin Cheng ◽  
Peng Li
Keyword(s):  
Neural Network ◽  
Pid Control ◽  
Network Algorithms ◽  
Learning Function ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller ◽  
Pid Neural Network ◽  
Self Learning ◽  
Controlled Object

A mathematical model of engine throttle as the controlled object is established and then the neural network algorithms and PID control are combined. With the self -learning function of the neural network, self -tunings of PID parameters are realized. The method overcomes disadvantages of PID as parameters which are difficult to determine and embodies better intelligence and robustness of the neural network, the simulation is researched by Matlab and the results show that the PID neural network controller is more accurate and adaptive than conventional PID.

Research on Aluminum Electrolysis Equipment Control Strategy

2012 ◽  
Vol 499 ◽  
pp. 474-477
Author(s):  
Jie Jia Li ◽  
Guang Qi Chen ◽  
Hao Wu ◽  
Ying Li
Keyword(s):  
Neural Network ◽  
Control Method ◽  
Industrial Process ◽  
Aluminum Electrolysis ◽  
Learning Ability ◽  
Control Methods ◽  
Time Varying Delay ◽  
Process System ◽  
Self Learning ◽  
Varying Delay

Aluminum electrolytic process is a complex nonlinear, time-varying delay and large industrial process system which conventional control methods are hardly can achieve good results. Because of that, a control method that combines fuzzy and neural network is put forward in the paper. This method has not only the robust fuzzy control and overshoot, but also self-learning ability of neural network. And conduct a system simulation, simulation results show that compared with the previous control method this method has good speed and stability, to achieve the purpose of energy saving for the control of alumina concentration.

scholarly journals An Improved PID Controller for the Compliant Constant-Force Actuator Based on BP Neural Network and Smith Predictor

2021 ◽  
Vol 11 (6) ◽  
pp. 2685
Author(s):  
Guojin Pei ◽  
Ming Yu ◽  
Yaohui Xu ◽  
Cui Ma ◽  
Houhu Lai ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Pid Control ◽  
Pid Controller ◽  
Experimental Validation ◽  
Control Method ◽  
Smith Predictor ◽  
Constant Force ◽  
Matlab Simulation ◽  
Adjustment Time

A compliant constant-force actuator based on the cylinder is an important tool for the contact operation of robots. Due to the nonlinearity and time delay of the pneumatic system, the traditional proportional–integral–derivative (PID) method for constant force control does not work so well. In this paper, an improved PID control method combining a backpropagation (BP) neural network and the Smith predictor is proposed. Through MATLAB simulation and experimental validation, the results show that the proposed method can shorten the maximum overshoot and the adjustment time compared with traditional the PID method.

Study for Workpiece Identity Based on the BP Neural Network and the Zernike Moment

2013 ◽  
Vol 694-697 ◽  
pp. 1958-1963 ◽  
Author(s):  
Xian Wei ◽  
Jing Dong Zhang ◽  
Xue Mei Qi
Keyword(s):  
Neural Network ◽  
Fault Tolerance ◽  
Parallel Processing ◽  
Bp Neural Network ◽  
Complex Shape ◽  
Learning Ability ◽  
Zernike Moment ◽  
Recognition Ability ◽  
Identity Based ◽  
Self Learning

The robots identify, locate and install the workpiece in FMS system by identifying the characteristic information of target workpiece. The paper studied the recognition technology of complex shape workpiece with combination of BP neural network and Zernike moment. The strong recognition ability of Zernike moment can extract the characteristic. The good fault tolerance, classification, parallel processing and self-learning ability of BP neural network can greatly improve the accurate rate of recognition. Experimental results show the effectiveness of the proposed method.

The Application of Neural Network in Computer Control

2013 ◽  
Vol 380-384 ◽  
pp. 421-424
Author(s):  
Jing Liu ◽  
Yu Chi Zhao ◽  
Xiao Hua Shi ◽  
Su Juan Liu
Keyword(s):  
Neural Network ◽  
Distributed Storage ◽  
Computer Control ◽  
System Optimization ◽  
Good Choice ◽  
Information Storage ◽  
Knowledge Process ◽  
Control Computer ◽  
Self Learning ◽  
Logic System

In recent years, it is a very active direction of research to use neural network to control computer. Neural network is a burgeoning crossing subject, and the way it processes information is different from the past symbolic logic system, which has some unique properties: such as the distributed storage and parallel processing of information, the unity of the information storage and information processing, and have the ability of self-organizing and self-learning. And it has been applied widespread in pattern recognition, signal processing, knowledge process, expert system, optimization, intelligent control and so on. Using neural network can deal with some problems such as complicated environment information, fuzzy background knowledge and undefined inference rules, and it allows samples to have relatively large defects and distortion, so it is a very good choice to adopt the recognizing method of neural network. This thesis discusses the application of neural network in computer control.

scholarly journals Single-Phase Photovoltaic Grid-Connected Inverter Based on Fuzzy Neural Network

2021 ◽  
Vol 25 (3) ◽  
pp. 310-316
Author(s):  
Shenping Xiao ◽  
Zhouquan Ou ◽  
Junming Peng ◽  
Yang Zhang ◽  
Xiaohu Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Fuzzy Neural Network ◽  
Single Phase ◽  
Fuzzy Inference ◽  
Learning Ability ◽  
Proportional Integral ◽  
Fuzzy Neural ◽  
Grid Connected Inverter ◽  
Self Learning

Based on a single-phase photovoltaic grid-connected inverter, a control strategy combining traditional proportional–integral–derivative (PID) control and a dynamic optimal control algorithm with a fuzzy neural network was proposed to improve the dynamic characteristics of grid-connected inverter systems effectively. A fuzzy inference rule was established after analyzing the proportional, integral, and differential coefficients of the PID controller. A fuzzy neural network was applied to adjust the parameters of the PID controller automatically. Accordingly, the proposed dynamic optimization algorithm was deduced in theory. The simulation and experimental results showed that the method was effective in making the system more robust to external disruption owing to its excellent steady-state adaptivity and self-learning ability.

Adaptive Neural Network Fuzzy Control Plane Double Inverted Pendulum

2013 ◽  
Vol 765-767 ◽  
pp. 2004-2007
Author(s):  
Su Ying Zhang ◽  
Ying Wang ◽  
Jie Liu ◽  
Xiao Xue Zhao
Keyword(s):  
Neural Network ◽  
Fuzzy Control ◽  
Control Theory ◽  
Inverted Pendulum ◽  
Learning Ability ◽  
Adaptive Neural Network ◽  
Pendulum System ◽  
Inverted Pendulum System ◽  
Self Learning ◽  
Controlled Object

Double inverted pendulum system is nonlinear and unstable. Fuzzy control uses some expert's experience knowledge and learns approximate reasoning algorithm. For it does not depend on the mathematical model of controlled object, it has been widely used for years. In practical engineering applications, most systems are nonlinear time-varying parameter systems. As the fuzzy control theory lacks of on-line self-learning and adaptive ability, it can not control the controlled object effectively. In order to compensate for these defects, it introduced adaptive, self-organizing, self-learning functions of neural network algorithm. We called it adaptive neural network fuzzy inference system (ANFIS). ANFIS not only takes advantage of the fuzzy control theory of abstract ability, the nonlinear processing ability, but also makes use of the autonomous learning ability of neural network, the arbitrary function approximation ability. The controller was applied to double inverted pendulum system and the simulation results showed that this method can effectively control the double inverted pendulum system.

Design of Vehicle’s Controlling System Used a 4WS Control Method Based on BP Neural Network

2011 ◽  
Vol 201-203 ◽  
pp. 276-280
Author(s):  
Ya Peng Liu ◽  
Yan Tang ◽  
Jia Bin Bi
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Control Method ◽  
Theoretical Method ◽  
Learning Ability ◽  
Controlling System ◽  
Nonlinear Dynamic Characteristics ◽  
Tokyo Institute ◽  
Institute Of Technology ◽  
Technology Institute

In this paper, a 4WS control method based on BP neural network was introduced. It used the BP neural network to simulate the map of vehicle and the nonlinear dynamic characteristics of the tire to avoid large errors that relying on mathematical simulation model of the problem. The 4WS measured data of Tokyo institute of Technology institute of Japan was used and used BP neural network method to identify the nonlinear characteristics of vehicle and tires. System controller’s design is not based on any theoretical method, but on the BP neural network’s self-learning ability. Experimental results show that this method has good controlling characteristics, and it can improve the vehicle’s active safety and manipulating stability effectively.

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