A New Approach of the Online Tuning Gain Scheduling Nonlinear PID Controller Using Neural Network

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.

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Online Tuning of PID Controller Using a Multilayer Fuzzy Neural Network Design for Quadcopter Attitude Tracking Control

Frontiers in Neurorobotics ◽

10.3389/fnbot.2020.619350 ◽

2021 ◽

Vol 14 ◽

Author(s):

Daewon Park ◽

Tien-Loc Le ◽

Nguyen Vu Quynh ◽

Ngo Kim Long ◽

Sung Kyung Hong

Keyword(s):

Neural Network ◽

Network Design ◽

Attitude Control ◽

Pid Controller ◽

Fuzzy Neural Network ◽

Learning Ability ◽

System Stability ◽

Effective Control ◽

Fuzzy Neural ◽

Online Tuning

This study presents an online tuning proportional-integral-derivative (PID) controller using a multilayer fuzzy neural network design for quadcopter attitude control. PID controllers are simple but effective control methods. However, finding the suitable gain of a model-based controller is relatively complicated and time-consuming because it depends on external disturbances and the dynamic modeling of plants. Therefore, the development of a method for online tuning of quadcopter PID parameters may save time and effort, and better control performance can be achieved. In our controller design, a multilayer structure was provided to improve the learning ability and flexibility of a fuzzy neural network. Adaptation laws to update network parameters online were derived using the gradient descent method. Also, a Lyapunov analysis was provided to guarantee system stability. Finally, simulations concerning quadcopter attitude control were performed using a Gazebo robotics simulator in addition to a robot operating system (ROS), and their results were demonstrated.

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Ship RBF neural network sliding mode PID heading control

MATEC Web of Conferences ◽

10.1051/matecconf/202235503064 ◽

2022 ◽

Vol 355 ◽

pp. 03064

Author(s):

Jiaming Yu ◽

Renxiang Bu ◽

Liangqi Li

Keyword(s):

Neural Network ◽

Pid Control ◽

Pid Controller ◽

Sliding Mode ◽

Rbf Neural Network ◽

Variable Structure ◽

Control Parameters ◽

Mmg Model ◽

Heading Control ◽

Online Tuning

In view of the inherent non-linearity, complexity, susceptibility to external wind, wave, and current interference of under-driven ships, and the difficulty of adjusting and adjusting control parameters, to improve the performance of ship’s autopilot, a kind of RBF neural network sliding mode variable structure PID controller is designed. Traditional PID control is sensitive to parameter changes, online tuning is difficult, and easy to overshoot. In order to solve this problem, combining the variable structure characteristics of PID, a differential compensation term is added to the integral term to convert the PID control parameters into three parameters with more obvious physical meanings, and then combined with the RBF neural network learning and identification function to realize online tuning and adaptive control of ship control parameters. Using MATLAB software to simulate the container ship “MV KOTA SEGAR” MMG model shows that the designed RBF neural network sliding mode PID controller can effectively eliminate the ship’s lateral deviation caused by external interference such as wind, waves, currents, etc., with high control accuracy,robustness and strong adaptability.

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New Approach for 3D Mesh Retrieval Using Artificial Neural Network and Histogram of Features

Scientific Visualization ◽

10.26583/sv.10.2.07 ◽

2018 ◽

Vol 10 (2) ◽

pp. 84-94 ◽

Cited By ~ 1

Author(s):

M. Pershina ◽

V.S. Bouksim ◽

K. Arhid ◽

F.R. Zakani ◽

M. Aboulfatah ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

New Approach ◽

3D Mesh ◽

Artificial Neural

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Design of Memristive Neural Network PID Controller

Chinese Journal of Computers ◽

10.3724/sp.j.1016.2013.02577 ◽

2014 ◽

Vol 36 (12) ◽

pp. 2577-2586 ◽

Cited By ~ 1

Author(s):

Si-Wei XIA ◽

Shu-Kai DUAN ◽

Li-Dan WANG ◽

Xiao-Fang HU

Keyword(s):

Neural Network ◽

Pid Controller ◽

Memristive Neural Network ◽

Neural Network Pid

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An Improved PID Controller for the Compliant Constant-Force Actuator Based on BP Neural Network and Smith Predictor

Applied Sciences ◽

10.3390/app11062685 ◽

2021 ◽

Vol 11 (6) ◽

pp. 2685

Author(s):

Guojin Pei ◽

Ming Yu ◽

Yaohui Xu ◽

Cui Ma ◽

Houhu Lai ◽

...

Keyword(s):

Neural Network ◽

Bp Neural Network ◽

Pid Control ◽

Pid Controller ◽

Experimental Validation ◽

Control Method ◽

Smith Predictor ◽

Constant Force ◽

Matlab Simulation ◽

Adjustment Time

A compliant constant-force actuator based on the cylinder is an important tool for the contact operation of robots. Due to the nonlinearity and time delay of the pneumatic system, the traditional proportional–integral–derivative (PID) method for constant force control does not work so well. In this paper, an improved PID control method combining a backpropagation (BP) neural network and the Smith predictor is proposed. Through MATLAB simulation and experimental validation, the results show that the proposed method can shorten the maximum overshoot and the adjustment time compared with traditional the PID method.

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Disturbance learning controller design for unmanned surface vehicle using LSTM technique of recurrent neural network

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189622 ◽

2021 ◽

pp. 1-11

Author(s):

Sang-Ki Jeong ◽

Dea-Hyeong Ji ◽

Ji-Youn Oh ◽

Jung-Min Seo ◽

Hyeung-Sik Choi

Keyword(s):

Neural Network ◽

Control System ◽

Flow Velocity ◽

Recurrent Neural Network ◽

Pid Controller ◽

Short Term Memory ◽

Ocean Currents ◽

Ocean Current ◽

Movement Speed ◽

Marine Research

In this study, to effectively control small unmanned surface vehicles (USVs) for marine research, characteristics of ocean current were learned using the long short-term memory (LSTM) model algorithm of a recurrent neural network (RNN), and ocean currents were predicted. Using the results, a study on the control of USVs was conducted. A control system model of a small USV equipped with two rear thrusters and a front thruster arranged horizontally was designed. The system was also designed to determine the output of the controller by predicting the speed of the following currents and utilizing this data as a system disturbance by learning data from ocean currents using the LSTM algorithm of a RNN. To measure ocean currents on the sea when a small USV moves, the speed and direction of the ship’s movement were measured using speed, azimuth, and location (latitude and longitude) data from GPS. In addition, the movement speed of the fluid with flow velocity is measured using the installed flow velocity measurement sensor. Additionally, a control system was designed to control the movement of the USV using an artificial neural network-PID (ANN-PID) controller [12]. The ANN-PID controller can manage disturbances by adjusting the control gain. Based on these studies, the control results were analyzed, and the control algorithm was verified through a simulation of the applied control system [8, 9].

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