Modeling and simulation of the air fuel ratio controlling the biogas engine based on the fuzzy PID algorithm: Modeling and simulation of the air fuel ratio controlling the biogas engine based on the fuzzy PID algorithm

Author(s):  
Zuhua Fang ◽  
Ying Sun ◽  
Kang Wang ◽  
Gengjuan Guo ◽  
Jiale Wang
Author(s):  
James Waldie ◽  
Brian Surgenor ◽  
Behrad Dehghan

In previous work, the performance of PID plus an adaptive neural network compensator (ANNC) was compared with the performance of a novel fuzzy adaptive PID algorithm, as applied to position control of one axis of a pneumatic gantry robot. The fuzzy PID controller was found to be superior. In this paper, a simplified non-adaptive fuzzy algorithm was applied to the control of both axes of the robot. Individual step results are first shown to confirm the validity of the simplified fuzzy PID controller. The fuzzy controller is then applied to a sinuosoidal tracking problem with and without a fuzzy PD tracking algorithm. Initial results are considered to be very promising. Future work requires developing an adaptive version of the controller in order to demonstrate robustness relative to changing tracking frequencies and changing supply pressures.


2012 ◽  
Vol 542-543 ◽  
pp. 217-222
Author(s):  
Ren Jiang Li ◽  
Tie Wang ◽  
Cheng Xun Sun

The Sampling flow will be decreased because the permeability weakness of filterable membrane after the dust sampler working for a period of time. It seriously affects the accuracy of measurement, so we should keep the dust sampler working under the constant gas flow. The stable, rapidness and accuracy constant sampling can be achieved based on the application of fuzzy-PID algorithm and the adoption of PWM technique. It has carried on design to the dust sampler control system from selecting the hardware and designing the software to data processing and errors analysis, and discussing the preferable performance of the dust sample control system.


2012 ◽  
Vol 263-266 ◽  
pp. 713-717
Author(s):  
Li Li ◽  
Ling Zhu ◽  
Qin Jiang ◽  
Gang Liu

Microfluidic PCR implements the PCR as a continuous process for nucleic acid analytics. Main fields of application are the monitoring of continuous processes for rapid identification of contaminants and quality control as well as high throughput screening of cells or microorganisms. The special heater arrangement allows the implementation of up to 40 cycles on the footprint of a sample. Precise temperature control is the key factor of PCR instrument, and optimal algorithm occupies an important position in this instrument. In order to optimizing the microfluidic PCR system thermal cycler perfomants, fuzzy-PID algorithm was designed, which would replace the lead correction algorithm. The simulation model was constructed and experimented with the use of the Simulink of Matlab software.The simulation results show that overshoot of lead correction algorithm is 8.5%, adjustment time is 3.4s. Overshoot of fuzzy-PID algorithm is 1.45%, adjustment time is only 1.94 s. Obviously, fuzzy-PID algorithm would overcome the defect of large overshoot of traditional PID, and the adjustment time becomes shorter also.


2011 ◽  
Vol 221 ◽  
pp. 571-576
Author(s):  
Chun Tang Zhang ◽  
Zhen Zhu Yu

Aiming at rubber sulfuration of nonlinear, delay and complexity, a Fuzzy/PID compound control algorithm is proposed. The algorithm combined fuzzy inference system and PID algorithm, it has solved well the problem which is difficult to establish a precise mathematical model because of the uncertainties and complexities of rubber sulfuration. The simulation results indicate that the control algorithm is viable and effective.


Sign in / Sign up

Export Citation Format

Share Document