RBF neural network and modified pid controller based State of Charge determination for lead-acid batteries

2008 ◽  
Author(s):  
Yanqing Shen ◽  
Guangwei Li ◽  
Shanquan Zhou ◽  
Yinquan Hu ◽  
Xiang Yu
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Rbf Neural Network ◽  
State Of Charge ◽  
Lead Acid Batteries ◽  
Charge Determination ◽  
Lead Acid

The Optimization of Adaptive PID Control Algorithm Based on RBF Neural Network

2014 ◽  
Vol 998-999 ◽  
pp. 943-946
Author(s):  
Jing Liu ◽  
Guo Xin Wang
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Rbf Neural Network ◽  
High Reliability ◽  
Simple Algorithm ◽  
Poor Performance ◽  
Learning Ability ◽  
The Neural Network ◽  
Pid Controller Tuning ◽  
Deterministic Control

As the earliest practical controller, PID controller has more than 50 years of history, and it is still the most widely used and most common industrial controllers. PID controller is simple to understand and use, without a prerequisite for an accurate model of the physical system, thus become the most popular, the most common controller. The reason why PID controller is the first developed one is that its simple algorithm, robustness and high reliability. It is widely used in process control and motion control, especially for accurate mathematical model that can be established deterministic control system. But the conventional PID controller tuning parameters are often poor performance, poor adaptability to the operating environment. The neural network has a strong nonlinear mapping ability, competence, self-learning ability of associative memory, and has a viable quantities of information processing methods and good fault tolerance.

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