Artificial Intelligence Neural Network Approach to Self Tuning of a Discrete-Time PID Control System

2021 ◽  
Author(s):  
Amit Kumar Pal ◽  
Tamara Nestorovic
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Control System ◽  
Discrete Time ◽  
Pid Control ◽  
Network Approach ◽  
Neural Network Approach ◽  
Self Tuning

Research on Self-Tuning PID Control Based on BP Neural Network of Multi-Motor Synchronous Control System

2013 ◽  
Vol 19 (6) ◽  
pp. 1668-1671 ◽  
Author(s):  
Huang Ke ◽  
Yu Zhixiong ◽  
Dong Qiang ◽  
Liu Jishun ◽  
Lu Le ◽  
...  
Keyword(s):  
Neural Network ◽  
Control System ◽  
Bp Neural Network ◽  
Pid Control ◽  
Synchronous Control ◽  
Self Tuning

Control Simulation Study Based on Recurrent Generalized Congruence Neural Network

2011 ◽  
Vol 383-390 ◽  
pp. 5691-5696
Author(s):  
Tian Yun Yan
Keyword(s):  
Neural Network ◽  
Control System ◽  
Simulation Study ◽  
Pid Control ◽  
Network Control ◽  
Neural Network Control ◽  
Nonlinear Dynamical ◽  
Self Tuning ◽  
Simulation Results ◽  
Dynamical Control

In order to meet the real-time demand of neural network control system, the structure and algorithm of self-tuning PID control system based on recurrent generalized congruence neural network(RGCNN) with fast convergence are presented, in which the improved recurrent generalized congruence neural network is adopted for identifier, and the single generalized congruence neuron with three inputs is used as controller. The simulation results of nonlinear dynamical control system show that the proposed RGCNN control system responses quickly and is stable, i.e., the proposed control system based on RGCNN is effective and feasible.

Position control of spherical inverted pendulum via improved discrete-time neural network approach

2020 ◽  
Vol 99 (4) ◽  
pp. 2867-2875
Author(s):  
Chenxi Liu ◽  
Zhaowu Ping ◽  
Yunzhi Huang ◽  
Jun-Guo Lu ◽  
Hai Wang
Keyword(s):  
Neural Network ◽  
Discrete Time ◽  
Inverted Pendulum ◽  
Position Control ◽  
Network Approach ◽  
Neural Network Approach

scholarly journals Control System and Algorithm of Sewage Treatment Plant Based on Artificial Intelligence

2021 ◽  
Vol 2066 (1) ◽  
pp. 012025
Author(s):  
Yunzhu Liu ◽  
Jinbao Cao
Keyword(s):  
Artificial Intelligence ◽  
Water Pollution ◽  
Wastewater Treatment ◽  
Control System ◽  
Pid Control ◽  
Sewage Treatment ◽  
Sewage Treatment Plant ◽  
Treatment Plant ◽  
Treated Wastewater ◽  
Self Tuning

Abstract With the improvement of people’s awareness of environmental protection in recent years, the related problems of water pollution treatment have gradually come into people’s view. As the source of life, water accounts for a huge proportion in our lives, but at the same time, water pollution is quietly spreading in places we don’t know. The continuous discharge of heavy industrial wastewater, agricultural wastewater and domestic sewage leads to increasingly serious water pollution. Sewage treatment(ST) is imperative, and its social benefits are huge, but the corresponding cost is high, and the return on investment is low. Traditional ST methods can not load large-scale ST. How to carry out ST based on artificial intelligence(AI), build ST plant control system, and make ST enter the era of automation is the problem to be solved. The purpose of this paper is to put forward the reform of control system for ST plant based on AI, apply AI into ST, and realize the automation and precision of ST plant. This paper mainly uses the fuzzy self-tuning PID control system algorithm, through the analysis of ST control object, analysis of fuzzy self-tuning PID controller design to complete the ST control system settings. In this paper, the literature review method and data analysis method are used. By collecting relevant data, the control system of ST plant is constructed to simulate ST, and the real-time data of ST is analyzed. The traditional PID control and fuzzy self-tuning PID control are compared. The experimental results show that the wastewater treatment plant system based on AI input, in the aspect of wastewater treatment, the concentration of COD and BOD in the treated wastewater are reduced by a certain proportion, the dissolved oxygen content in the wastewater reaches about 2.0mg/l, which meets the national discharge standard, and its rising time is reduced to 25 seconds, and the adjustment time is saved by 50 seconds.

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