Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Nonlinear model predictive controller using neural network
Date
1997-06-12
Author
Karahan, O
Ozgen, C
Halici, U
Leblebicioğlu, Mehmet Kemal
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
198
views
0
downloads
Cite This
In this paper, a non-linear Model Predictive Control (MPC) algorithm is proposed which extends the capacities of Linear Model Predictive Controllers to control non-linear systems. A Neural Network (NN) is used to model the deviation of the non-linear system from its linear MPC model. Proposed algorithm is tested in control of an industrial multi-component high purity distillation column by simulation. Results of NN-MPC show high improvement in control of system over linear MPC algorithm.
Subject Keywords
Industrial control
,
Electrical equipment industry
,
Testing
,
Control system synthesis
,
Prediction algorithms
,
Predictive control
,
Nonlinear systems
,
Nonlinear control systems
,
Predictive models
,
Neural networks
URI
https://hdl.handle.net/11511/47163
DOI
https://doi.org/10.1109/icnn.1997.616105
Collections
Department of Electrical and Electronics Engineering, Conference / Seminar
Suggestions
OpenMETU
Core
Adaptive output feedback control with reduced sensitivity to sensor noise
Kutay, Ali Türker; Hovakimyan, N (2003-01-01)
We address adaptive output feedback control of uncertain nonlinear systems with noisy output measurements, in which both the dynamics and the dimension of the regulated system may be unknown, and only the relative degree of the regulated output is assumed to be known. Given a smooth reference trajectory, the problem is to design a controller that forces the system measurement to track it with bounded errors. A recently developed method proposes the use of a linear error observer that estimates the tracking ...
Linear Analysis of Zero-Lift Pitching Moment Coefficient and Gravity Effects on Missile Autopilots
Ovec, Naz T.; Erer, Koray S.; Kutay, Ali Türker (2015-03-14)
The most of the studies on disturbance rejection are covered with robust control methods, where this paper suggests a narrowed but a more simplified linear model based technique to assess the robustness of the controllers. In this paper presented, the problems originated from the disturbances are aimed to be obtained as far in advance as possible. The method used in this work reduces the iterative fine tuning time course between the non-linear simulation and the linear model; hence it enhances the authority...
An artificial neural network estimator design for the inferential model predictive control of an industrial multi-component distillation column
Bahar, Almila; Özgen, Canan; Department of Chemical Engineering (2003)
An inferential control methodology, that utilizes an artificial neural network (ANN) estimator for a model predictive controller, is developed for an industrial multi-component distillation column. In the column, propane and butane is separated from a mixture of propane, n-butane, i-butane, and i-pentane with a top product purity of 96% propane and a bottom product purity of 63% n- butane. Dual composition control of the column must be used in a multivariable model predictive controller for an efficient ope...
A Control System Architecture for Control of Non-Affine in Control, Open-Loop Unstable Underactuated Systems
Marangoz, Alp; Kutay, Ali Türker (2017-07-25)
In this paper, a control system architecture for control of non-affine in control, open-loop unstable underactuated system is discussed. Passivization of the unactuated (internal) system dynamics achieved through perturbation of trajectories of the actuated states, which are calculated through adaptive dynamic inversion technique, based on Tikhonov's theorem. Performance of the controller is shown through simulation of two open-loop unstable and locally uncontrollable example problems.
Temporal logic model predictive control
Aydın Göl, Ebru; Belta, Calin (2015-06-01)
This paper proposes an optimal control strategy for a discrete-time linear system constrained to satisfy a temporal logic specification over a set of linear predicates in its state variables. The cost is a quadratic function that penalizes the distance from desired state and control trajectories. The specification is a formula of syntactically co-safe Linear Temporal Logic (scLTL), which can be satisfied in finite time. To incorporate dynamic environments, it is assumed that the reference trajectories are o...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
O. Karahan, C. Ozgen, U. Halici, and M. K. Leblebicioğlu, “Nonlinear model predictive controller using neural network,” 1997, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/47163.