Intelligent Self-Adaptive Control Method Based on RBFNN and its Application in Hydraulic Control

2014 ◽  
Vol 1030-1032 ◽  
pp. 1488-1492
Author(s):  
Hao Zhang ◽  
Ya Jie Zhang ◽  
Yan Gu Zhang
Keyword(s):  
Adaptive Control ◽  
Control System ◽  
Nonlinear Control ◽  
Intelligent Control ◽  
Control Method ◽  
Internal Model Control ◽  
Nonlinear Control System ◽  
Hydraulic Control ◽  
Model Control ◽  
Self Adaptive

We proposed an intelligent self-adaptive control method based on RBFNN in this paper, dynamic identification model of nonlinear control system is built based on radial basis neural network, mixed intelligent method of dynamic self-adaptive internal model control is developed by adjusting online for nonlinear control system. We applied the intelligent self-adaptive control method to nonlinear hydraulic control, simulation shows the dynamic characteristic is greatly improved by the intelligent control strategy for nonlinear control system, good tracking and control effect is reached in condition of high frequency response, and the intelligent control method has higher precision, smaller overshoot and stronger robustness compared with common PID control, BPNN control and fuzzy control. It provides a new control method for nonlinear control system.

The Research on Intelligent Control Method for EHP System

2012 ◽  
Vol 580 ◽  
pp. 12-15 ◽  
Author(s):  
Yi Wan ◽  
Qi Bo Cai ◽  
Huan Wang
Keyword(s):  
Control System ◽  
Predictive Control ◽  
Intelligent Control ◽  
Internal Model ◽  
High Speed ◽  
Control Method ◽  
Internal Model Control ◽  
Support Vector ◽  
Proportional Control ◽  
Self Adaptive

Optimized machine learning algorithm is applied to control modeling of high-speed electric-hydraulic proportional system of high nonlinear in this paper, a identification model of high-speed electric-hydraulic proportional system is built based on support vector machines, fusion intelligent method of dynamic self-adaptive internal model control and predictive control is realized for high-speed electric-hydraulic proportional control system. Internal model and inverse controller model are online adjusted together. Simulation shows the satisfactory tracking effect by intelligent technology of dynamic self-adaptive internal control and predictive control based on the support vector machine, the dynamic characteristic is greatly improved by the intelligent control strategy for high-speed electric-hydraulic proportional control system, good tracking and control effect is reached in condition of high frequency response. It provides a new intelligent control method for high-speed electric-hydraulic proportional system.

The Internal Model Control of Greenhouse System Based on LS_SVM

2014 ◽  
Vol 532 ◽  
pp. 200-203 ◽  
Author(s):  
Qing Ling Dai
Keyword(s):  
Control System ◽  
Internal Model ◽  
Control Method ◽  
Good Control ◽  
Internal Model Control ◽  
System Method ◽  
Model Control ◽  
Environment Control ◽  
Very High ◽  
Controlled Object

The greenhouse environment has the characteristics of nonlinear, time-varying, large time delay, so it is difficult to obtain satisfactory result using conventional control method. The internal model control system method and the algorithm based on LS_SVM is proposed for the problem. The simulation experiment was performed on the greenhouse environment control system. Results shows that the controlled object internal mode and inverse model have very high precision and generalization ability and the control system has not only good control performance but also strong anti-interference ability and robust performance which has the great value to application and extension

scholarly journals A Review of the Self-Adaptive Traffic Signal Control System Based on Future Traffic Environment

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Yizhe Wang ◽  
Xiaoguang Yang ◽  
Hailun Liang ◽  
Yangdong Liu
Keyword(s):  
Adaptive Control ◽  
Control System ◽  
Control Method ◽  
Autonomous Driving ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Control Methods ◽  
Adaptive Traffic Signal Control ◽  
Self Learning ◽  
Self Adaptive

The self-adaptive traffic signal control system serves as an effective measure for relieving urban traffic congestion. The system is capable of adjusting the signal timing parameters in real time according to the seasonal changes and short-term fluctuation of traffic demand, resulting in improvement of the efficiency of traffic operation on urban road networks. The development of information technologies on computing science, autonomous driving, vehicle-to-vehicle, and mobile Internet has created a sufficient abundance of acquisition means for traffic data. Great improvements for data acquisition include the increase of available amount of holographic data, available data types, and accuracy. The article investigates the development of commonly used self-adaptive signal control systems in the world, their technical characteristics, the current research status of self-adaptive control methods, and the signal control methods for heterogeneous traffic flow composed of connected vehicles and autonomous vehicles. Finally, the article concluded that signal control based on multiagent reinforcement learning is a kind of closed-loop feedback adaptive control method, which outperforms many counterparts in terms of real-time characteristic, accuracy, and self-learning and therefore will be an important research focus of control method in future due to the property of “model-free” and “self-learning” that well accommodates the abundance of traffic information data. Besides, it will also provide an entry point and technical support for the development of Vehicle-to-X systems, Internet of vehicles, and autonomous driving industries. Therefore, the related achievements of the adaptive control system for the future traffic environment have extremely broad application prospects.

The Linkage Mechanism with Clearance Modeling Based on Neural Network and its Intelligent Control

2013 ◽  
Vol 401-403 ◽  
pp. 1644-1648
Author(s):  
Ting Gui Li ◽  
Shao Wen Xue
Keyword(s):  
Neural Network ◽  
Intelligent Control ◽  
Control Method ◽  
Internal Model Control ◽  
System Stability ◽  
Experimental Sample ◽  
Linkage Mechanism ◽  
The Neural Network ◽  
Model Control ◽  
Strongly Nonlinear System

Using the traditional control method is difficult to obtain the ideal control result, because the linkage mechanism with clearance is a strongly nonlinear system, and it is difficult to establish the accurate mathematical model. In response to these problems, take the linkage mechanism with clearance for example and establish BP neural network offline modeling, on the basis of the experimental sample data. We separately applied the neural network internal model control and the parameters self-adjusting fuzzy control to reduce the nonlinear error caused by the clearance. The experimental results show that using intelligent control technology, the system stability has been significantly improved, and the system error has been reduced effectively.

scholarly journals Research of Smart Car’s Speed Control Based on the Internal Model Control

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Han Yu ◽  
Hamid Reza Karimi ◽  
Xuemei Zhu
Keyword(s):  
Control System ◽  
Internal Control ◽  
Internal Model ◽  
Control Method ◽  
Good Control ◽  
Speed Control ◽  
Internal Model Control ◽  
Determine Parameter ◽  
Model Control ◽  
Speed Control System

Based on analyzing principle about the smart car’s speed control system, the system mathematical model is built. Considering the control optimization, a novel control scheme is proposed based on internal model control, and the internal model controller of speed control system is established. Regarding this subject, the internal control theory is introduced to verify the control performance; the traditional PID control method is employed in the experiment. The experiment indicates that the proposed method based on internal model control is easier to determine parameter and has a well robust and good control result of smart car’s speed.

The Design of Combustion Control System Based on Fuzzy Immune Internal Model Controller

2011 ◽  
Vol 311-313 ◽  
pp. 2230-2234
Author(s):  
Gui Li Yuan
Keyword(s):  
Control System ◽  
Internal Model ◽  
Control Method ◽  
Fast Response ◽  
Internal Model Control ◽  
Combustion Control ◽  
Control Effect ◽  
Large Delay ◽  
Model Control ◽  
Internal Model Controller

The controlled object of boiler combustion system in power plant is a complex system with nonlinear, timing change, large lagging and multi-variable coupling, and does not have precise mathematical model, so it is difficult to obtain the satisfactory control effect adopting the traditional PID control. Advanced control strategies are adopted to improve the performance of the boiler combustion control system, and it has been more and more the concern of the majority of electricity production enterprises. Internal model control is a very practical control method, and its main characteristic is simple structure, intuitive design and few online adjustment parameter, and easy adjustment policy. And it is especially particularly significant to improve the control effect of large delay system. The internal model control system is used in power station boiler combustion system, it can effectively solve the large delay, large inertia and other shortcomings, but there is the contradiction between the fast response and robustness in internal model control system. The fuzzy immune control has advantages, such as, fast response, fast stable and good robust, etc. The fuzzy immune control is introduced into internal model control system, this paper designs fuzzy immune internal model controller, which integrates speed and robustness of the internal model control. The fuzzy immune internal model control is applied to combustion control system, and we compare it with ordinary internal model control method. The simulation result shows that fuzzy immune internal model control can greatly improve the characteristics of the control system with time delay. And this effectiveness of the fuzzy immune internal model controller has been verified.

Multimode Adaptive Control for Tank Gun Control System Based on Tracking Differentiator

2011 ◽  
Vol 383-390 ◽  
pp. 79-85
Author(s):  
Dong Yuan ◽  
Xiao Jun Ma ◽  
Wei Wei
Keyword(s):  
Adaptive Control ◽  
Control System ◽  
High Speed ◽  
Control Method ◽  
Reference Model ◽  
Gun Control ◽  
Math Model ◽  
Tracking Differentiator ◽  
Switch Control ◽  
Model Reference Adaptive

Aiming at the problems such as switch impulsion, insurmountability for influence caused by nonlinearity in one tank gun control system which adopts double PID controller to realize the multimode switch control between high speed and low speed movement, the system math model is built up; And then, Model Reference Adaptive Control (MRAC) method based on nonroutine reference model is brought in and the adaptive gun controller is designed. Consequently, the compensation of nonlinearity and multimode control are implemented. Furthermore, the Tracking Differentiator (TD) is affiliated to the front of controller in order to restrain the impulsion caused by mode switch. Finally, the validity of control method in this paper is verified by simulation.

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