scholarly journals Optimization of Neural Network-Based Self-Tuning PID Controllers for Second Order Mechanical Systems

2021 ◽  
Vol 11 (17) ◽  
pp. 8002
Author(s):  
Yong-Seok Lee ◽  
Dong-Won Jang
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Target Position ◽  
Target System ◽  
Pid Controllers ◽  
Response Characteristics ◽  
Response Data ◽  
Observing Responses ◽  
Network Method ◽  
Self Tuning

The feasibility of a neural network method was discussed in terms of a self-tuning proportional–integral–derivative (PID) controller. The proposed method was configured with two neural networks to dramatically reduce the number of tuning attempts with a practically achievable small amount of data acquisition. The first network identified the target system from response data, previous PID parameters, and response characteristics. The second network recommended PID parameters based on the results of the first network. The results showed that it could recommend PID parameters within 2 s of observing responses. When the number of trained data was as low as 1000, the performance efficiency of these methods was 92.9%, and the tuning was completed in an average of 2.94 attempts. Additionally, the robustness of these methods was determined by considering a system with noise or a situation when the target position was modified. These methods are also applicable for traditional PID controllers, thus enabling conservative industries to continue using PID controllers.

Self-tuning of a Neuro-Adaptive PID Controller for a SCARA Robot Based on Neural Network

2018 ◽  
Vol 16 (5) ◽  
pp. 1364-1374 ◽  
Author(s):  
E.O . Freire ◽  
Francisco Guido Rossomando ◽  
Carlos Miguel Soria
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Scara Robot ◽  
Self Tuning

Optimum Gain-Scheduling PID Controllers for Gas Turbine Engines Based on NARMAX and Neural Network Models

2003 ◽  
Author(s):  
Junxia Mu ◽  
David Rees ◽  
Neophytos Chiras
Keyword(s):  
Neural Network ◽  
Gas Turbine ◽  
Pid Controller ◽  
Network Models ◽  
Gain Scheduling ◽  
Pid Controllers ◽  
Neural Network Models ◽  
Turbine Engine ◽  
Dynamic Relationship ◽  
Fuel Flow

This paper presents PID controller designs based on NARMAX and feedforward neural network models of a Spey gas turbine engine. Both models represent the dynamic relationship between the fuel flow and shaft speed. Due to the engine non-linearity, a single set of PID controller parameters is not sufficient to control the gas turbine throughout the operating range. Gain-scheduling PID controllers are therefore used in order to obtain optimum control. A comparison between the controller designs based on the two model representations is also made.

Considerations in the Application of Self-Tuning PID Controllers Using EXACT-Tuning Algorithm

1986 ◽  
Vol 19 (9) ◽  
pp. 260-266 ◽  
Author(s):  
A Carmon
Keyword(s):  
Pid Controller ◽  
System Approach ◽  
New Technology ◽  
Pid Controllers ◽  
Indirect Benefits ◽  
Industry Standard ◽  
Plant Maintenance ◽  
Self Tuning ◽  
On Line ◽  
Insight Into

The paper reviews the operation of an on-line continuously adaptive PID controller which uses an expert system approach to self-tune. The benefits of using continuously adaptive control have been demonstrated by trial installations and have produced financial returns running into six figure values per annum per loop. Indirect benefits such as better insight into plant controllability (used to indicate a need for plant maintenance) and faster plant startups have been identified by users. The paper stresses the need for experience feedback in developing the understanding of the industry in the correct and best use of this new technology, a technology which promises to become the industry standard. An application guideline is provided and some limitations are pointed out. Some features that help make it a robust and secure product in a process control environment are also indicated.

BP Neural Network for Mobile Robot Self-Tuning PID Controller Design

2014 ◽  
Vol 898 ◽  
pp. 755-758 ◽  
Author(s):  
Wei Li ◽  
Jian Fang
Keyword(s):  
Neural Network ◽  
Mobile Robot ◽  
Bp Neural Network ◽  
Pid Controller ◽  
Controller Design ◽  
System Stability ◽  
Stabilization Control ◽  
Pid Controller Design ◽  
Self Tuning ◽  
Attitude Model

Establish the attitude model for self-designed mobile robot, According to the characteristics of nonlinear, unstable, using BP neural network method to achieve self-tuning PID parameters to make optimal parameters of the PID controller. Stabilization control of two-wheeled self-balanced robots at the same time, decrease the overshoot of the system and the number of shocks. Simulation experiments show that: Using BP neural network self-tuning PID controller improves system stability, effectiveness has been well controlled, with high practical value

scholarly journals Design of Neutrosophic Self-Tuning PID Controller for AC Permanent Magnet Synchronous Motor Based on Neutrosophic Theory

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Zhongliang Fu ◽  
Chunping Liu ◽  
Shengyi Ruan ◽  
Kun Chen
Keyword(s):  
Genetic Algorithm ◽  
Permanent Magnet ◽  
Pid Controller ◽  
Permanent Magnet Synchronous Motor ◽  
System Response ◽  
Permanent Magnet Synchronous Motors ◽  
Response Characteristics ◽  
Cosine Measure ◽  
Self Tuning ◽  
Different Response

In practical control applications, AC permanent magnet synchronous motors need to work in different response characteristics. In order to meet this demand, a controller which can independently realize the different response characteristics of the motor is designed based on neutrosophic theory and genetic algorithm. According to different response characteristics, neutrosophic membership functions are constructed. Then, combined with the cosine measure theorem and genetic algorithm, the neutrosophic self-tuning PID controller is designed. It can adjust the parameters of the controller according to response requirements. Finally, three kinds of controllers with typical system response characteristics are designed by using Simulink. The effectiveness of the designed controller is verified by simulation results.

DESIGN OF ADAPTIVE FUZZY FRACTIONAL ORDER PID CONTROLLER FOR AUTONOMOUS UNDERWATER VEHICLE (AUV) IN HEADING AND DEPTH ATTITUDES

2021 ◽  
Vol 158 (A1) ◽  
Author(s):  
M H Khodayari ◽  
S Balochian
Keyword(s):  
Fractional Order ◽  
Pid Controller ◽  
Autonomous Underwater Vehicle ◽  
Path Following ◽  
Transient State ◽  
Pid Controllers ◽  
Adaptive Fuzzy ◽  
Highly Nonlinear ◽  
Self Tuning ◽  
Fractional Order Pid

This paper deals with the design of new self-tuning Fuzzy Fractional Order PID (AFFOPID) controller based on nonlinear MIMO structure for an AUV in order to enhance the performance in both transient state and steady state of traditional PID controller. It is particularly advantageous when the effects of highly nonlinear processes, like high maneuver, parameters variation, have to be controlled in presence of sensor noises and wave disturbances. Aspects of AUV controlling are crucial because of Complexity and highly coupled dynamics, time variety and difficulty in hydrodynamic modeling. In this try, the comprehensive nonlinear model of AUV is derived through kinematics and dynamic equations. The scaling factor of the proposed AFFOPID Controller is adjusted online at different underwater conditions. Combination of adaptive fuzzy methods and PID controllers can enhance solving the uncertainty challenge in the PID parameters and AUV parameter uncertainty. The simulation results show that developed control system is stable, competent and efficient enough to control the AUV in path following with stabilized and controlled speed. Obtained results demonstrate that the proposed controller has good performance and significant robust stability in comparison to traditional tuned PID controllers.

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