U-Model Based Adaptive Control of Gas Process Plant
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TLDR
In this research work, an adaptive controller, based upon a recently developed U-Model is suggested, a simplified polynomial structure that adaptively adjusts system parameters online.About:
This article is published in Procedia Computer Science.The article was published on 2017-03-01 and is currently open access. It has received 8 citations till now. The article focuses on the topics: Adaptive control & Control theory.read more
Citations
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Journal ArticleDOI
Design and Verification of Aeroengine Rotor Speed Controller Based on U-LADRC
TL;DR: A novel aeroengine transient-speed controller with low algorithm complexity is designed by combining linear parameter varying (LPV) model with U-control theory and has good transition state control performance and good steady-state antidisturbance ability.
Journal ArticleDOI
Research on RBF neural network model reference adaptive control system based on nonlinear U – model
TL;DR: The paper designs the adaptive algorithm of the radial basis function neural network, and trains the network by the error variety, which shows that the model reference adaptive control system based on RBF neural network has better control effect than the nonlinear U-model adaptive controlSystem based on the gradient descent method.
Journal ArticleDOI
Composite control of RBF neural network and PD for nonlinear dynamic plants using U-model
Journal ArticleDOI
Research on parallel control of CMAC and PD based on U model
TL;DR: The article improves the PD algorithm to non linear PD control algorithm to complete the design of the system and shows that the nonlinear PD algorithm is better than thePD algorithm, meanwhile, the tracking speed and control precision of theSystem are improved.
Proceedings ArticleDOI
Online system modeling of chemical process plant using U-model
TL;DR: This reserach work proposes a polynomial adaptive model recently introduced called U-Model to be used for online system identification of Chemical Process Plant, a simple, stable and reliable model which has previously yielded encouraging results when applied to various application in different scenario.
References
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Journal ArticleDOI
A pole placement controller for non-linear dynamic plants
Quanmin Zhu,Lingzhong Guo +1 more
TL;DR: In this paper, a control-oriented model is proposed to represent a wide range of non-linear discrete-time dynamic plants, and a pole placement controller is designed for nonlinear discrete time plants.
Journal ArticleDOI
U-model based learning feedforward control of MIMO nonlinear systems
TL;DR: In this article, a learning feedforward controller (LFFC) using the U-model is proposed for better tracking control of multivariable nonlinear systems over a finite time interval.
Journal ArticleDOI
An Adaptive Learning Rate for RBFNN Using Time-Domain Feedback Analysis
TL;DR: An intelligent adaptation rule is developed for the learning rate of RBFNN which gives faster convergence via an estimate of error energy while giving guarantee to the l 2 stability governed by the upper bounding via small gain theorem.
Journal ArticleDOI
Utilizing higher-order neural networks in U-model based controllers for stable nonlinear plants
Muhammed Shafiq,Naveed Butt +1 more
TL;DR: In this article, a U-model based controller utilizing nonlinear adaptive filters is proposed, which can capture higher-order nonlinear properties of the input pattern space while allowing the synthesis of a simple control law, and the effectiveness of the proposed scheme is demonstrated through application to various nonlinear models and a comparison with the Backstepping controller is presented.
Book ChapterDOI
A Classification and Overview of Sliding Mode Controller Sliding Surface Design Methods
TL;DR: This study reviews and classify the methods available in the literature for sliding surface design focusing on single-input systems to improve controller performance by minimizing or eliminating the time to reach the sliding phase.
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