Browsing by Author "Şahin, Savaş"

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Citation - WoS: 12
Citation - Scopus: 15
Online Learning ARMA Controllers With Guaranteed Closed-Loop Stability
(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2016) Savas Sahin; Cuneyt Guzelis; Guzelis, Cuneyt; Şahin, Savaş
This paper presents a novel online block adaptive learning algorithm for autoregressive moving average (ARMA) controller design based on the real data measured from the plant. The method employs ARMA input-output models both for the plant and the resulting closed-loop system. In a sliding window the plant model parameters are identified first offline using a supervised learning algorithm minimizing an epsilon-insensitive and regularized identification error which is the window average of the distances between the measured plant output and the model output for the input provided by the controller. The optimal controller parameters are then determined again offline for another sliding window as the solution to a constrained optimization problem where the cost is the epsilon-insensitive and regularized output tracking error and the constraints that are linear inequalities of the controller parameters are imposed for ensuring the closed-loop system to be Schur stable. Not only the identification phase but also the controller design phase uses the input-output samples measured from the plant during online learning. In the developed online controller design method the controller parameters can always be kept in a parameter region providing Schur stability for the closed-loop system. The e-insensitiveness provides robustness against disturbances so does the regularization better generalization performance in the identification and the control. The method is tested on benchmark plants including the inverted pendulum and dc motor models. The method is also tested on an emulated and also a real dc motor by online block adaptive learning ARMA controllers in particular Proportional-Integral-Derivative controllers.
Citation - Scopus: 3
Online learning of stable robust adaptive controllers design based on data-dependent feedback linearization with application to rotary inverted pendulum
(Springer Science and Business Media Deutschland GmbH, 2024) Mehmet Uğur Soydemir; Savaş Şahin; Aykut Kocaoǧlu; Parvin Bulucu; Cüneyt Güzeliş; Kocaoğlu, Aykut; Bulucu, Parvin; Soydemir, Mehmet Uğur; Güzeliş, Cüneyt; Şahin, Savaş
This study introduces an online (supervised) learning method to design nonlinear auto-regressive moving average (NARMA) controllers for feedback-linearized nonlinear single-input single-output (SISO) systems. The algorithm ensures Schur stability of the overall closed-loop system and provides adaptiveness and robustness for the NARMA controllers. The first stage of the method derives in a data-dependent way a feedback-linearized model of the nonlinear plant by using its input and output sample pairs. The method’s second stage which constitutes the novel part of the presented study builds up an online learning scheme for the linear auto-regressive moving average (ARMA) controller based on an already learned feedback-linearized model of the nonlinear plant. During online supervised learning ARMA parameters of the feedback-linearized SISO plant model and the closed-loop ARMA model are computed by minimizing the plant identification and the closed-loop system tracking errors. Both errors are defined as ℓ1ε namely ε-insensitive loss functions that provide NARMA controller the robustness against noise and outliers. The proposed online learning control algorithm is applied to a rotary inverted pendulum model and to a real rotary inverted pendulum setup. The tracking performance of the developed controller is compared with those of the linear quadratic regulator and coupled sliding mode controller in terms of mean square error. © 2024 Elsevier B.V. All rights reserved.