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linear-quadratic-regulator--lqr
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Designing a LQR controller for an electro-hydraulic-actuated-clutch model
, Article Proceedings of 2016 2nd International Conference on Control Science and Systems Engineering, ICCSSE 2016, 27 July 2016 through 29 July 2016 ; 2016 , Pages 82-87 ; 9781467398725 (ISBN) ; Selk Ghafari, A ; Pourebrahim, M ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2016
Abstract
During the past decade, Electro-Hydraulic system has performed a significant role in industrial engineering as an actuator for high performance and precision positioning applications. In this case, many control methods have been developed for an electro-hydraulic actuated clutch. In this paper a Linear Quadratic Regulators (LQR) is proposed to trajectory control of a wet clutch actuated by a hydraulic servo valve mechanism. Simulation study was performed using linearized mathematical model of the system implemented in MATLAB software. Based on the simulation results performance of the proposed controller was evaluated and discussed
Design a LQG power system stabilizer for bistun power plant
, Article AUPEC'09 - 19th Australasian Universities Power Engineering Conference: Sustainable Energy Technologies and Systems, 27 September 2009 through 30 September 2009, Adelaide ; 2009 ; 9780863967184 (ISBN) ; Hossein Vahabie, A ; Nademi, H ; Ranjbar, A ; Sharif University of Technology
Abstract
Design of a classical controller is based on output feedback and structure of the controller. From modern control point of view, the information of input signals, output signals and internal behavior of the system are presented in state space model. In this paper, modern control is used to increase small-disturbance stability of power systems. Although many of existing power plants prefer to use classical Power System Stabilizers (PSS), these controllers have their own problems. Digital implementation of modern controllers is considered as a good advantage of modern PSS. In this paper, a modern control based PSS is designed for Bistun power plant and its output results are compared to the...
Model-free LQR design by Q-function learning
, Article Automatica ; Volume 137 , 2022 ; 00051098 (ISSN) ; Babazadeh, M ; Sharif University of Technology
Elsevier Ltd
2022
Abstract
Reinforcement learning methods such as Q-learning have shown promising results in the model-free design of linear quadratic regulator (LQR) controllers for linear time-invariant (LTI) systems. However, challenges such as sample-efficiency, sensitivity to hyper-parameters, and compatibility with classical control paradigms limit the integration of such algorithms in critical control applications. This paper aims to take some steps towards bridging the well-known classical control requirements and learning algorithms by using optimization frameworks and properties of conic constraints. Accordingly, a new off-policy model-free approach is proposed for learning the Q-function and designing the...