Design of an IMCPID Optimized Neural Network for Stepless Flow Control of Reciprocating Mechinery

It is usually difficult to design a controller for a nonlinear multiple-input and multiple-output (MIMO) system. The methodological approach taken in this study is a mixed methodology based on a PID-type internal model control (IMC) method and neural network (NN) optimization algorithm. The NN contr...

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Bibliographic Details
Main Authors: Huaibin Hong, Zhinong Jiang, Wensheng Ma, Wei Xiong, Jinjie Zhang, Wenhua Liu, Yao Wang
Format: Article
Language:English
Published: MDPI AG 2021-08-01
Series:Applied Sciences
Subjects:
Online Access:https://www.mdpi.com/2076-3417/11/17/7785
Description
Summary:It is usually difficult to design a controller for a nonlinear multiple-input and multiple-output (MIMO) system. The methodological approach taken in this study is a mixed methodology based on a PID-type internal model control (IMC) method and neural network (NN) optimization algorithm. The NN controller is designed for adjusting the sole parameter in IMCPID and compensating the characteristic changes and non-linearity in stepless flow control. In this study, a simulation of a nonlinear MIMO system with strong coupling is carried out. The simulation results indicate that the proposed control method has a better performance in settle time, overshoot, robustness and set-point tracking accuracy compared with other considered methods.
ISSN:2076-3417