Self-tuning PID control using recurrent wavelet neural networks

2012 ◽  
Author(s):  
Ching-Chih Tsai ◽  
Ya-Ling Chang
Keyword(s):  
Neural Networks ◽  
Pid Control ◽  
Wavelet Neural Networks ◽  
Self Tuning

Self-Tuning Neuro-PID for Stabilization of Double Inverted Pendulum

1998 ◽  
Vol 10 (5) ◽  
pp. 439-444
Author(s):  
Yoshiyuki Kishida ◽  
◽  
Sigeru Omatu ◽  
Michifumi Yoshioka
Keyword(s):  
Neural Networks ◽  
Pid Control ◽  
Pid Controller ◽  
Inverted Pendulum ◽  
Control Architecture ◽  
Self Tuning ◽  
Single Input ◽  
Simulation Results

This paper covers a new self-tuning neuro-PID control architecture and its application to stabilization of single and double inverted pendulums. Single-Input multioutput controls the inverted pendulum using the PID controller. PID gains are tuned using two types of neural networks. Simulation results demonstrate the effectiveness of the proposed approach.

scholarly journals Self-Tuning Two Degree-of-Freedom Proportional–Integral Control System Based on Reinforcement Learning for a Multiple-Input Multiple-Output Industrial Process That Suffers from Spatial Input Coupling

Processes ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 487
Author(s):  
Fumitake Fujii ◽  
Akinori Kaneishi ◽  
Takafumi Nii ◽  
Ryu’ichiro Maenishi ◽  
Soma Tanaka
Keyword(s):  
Control System ◽  
Reinforcement Learning ◽  
Pid Control ◽  
Multiple Input Multiple Output ◽  
Industrial Process ◽  
Pi Controller ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Self Tuning ◽  
Two Degree Of Freedom

Proportional–integral–derivative (PID) control remains the primary choice for industrial process control problems. However, owing to the increased complexity and precision requirement of current industrial processes, a conventional PID controller may provide only unsatisfactory performance, or the determination of PID gains may become quite difficult. To address these issues, studies have suggested the use of reinforcement learning in combination with PID control laws. The present study aims to extend this idea to the control of a multiple-input multiple-output (MIMO) process that suffers from both physical coupling between inputs and a long input/output lag. We specifically target a thin film production process as an example of such a MIMO process and propose a self-tuning two-degree-of-freedom PI controller for the film thickness control problem. Theoretically, the self-tuning functionality of the proposed control system is based on the actor-critic reinforcement learning algorithm. We also propose a method to compensate for the input coupling. Numerical simulations are conducted under several likely scenarios to demonstrate the enhanced control performance relative to that of a conventional static gain PI controller.

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