This paper presents the mean-square optimal data-based quadratic-Gaussian controller for stochastic nonlinear polynomial systems with a polynomial multiplicative noise, a linear control input, and a quadratic criterio...
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This paper presents the mean-square optimal data-based quadratic-Gaussian controller for stochastic nonlinear polynomial systems with a polynomial multiplicative noise, a linear control input, and a quadratic criterion over linear observations. The mean-square optimal closed-form controller equations are obtained using the separation principle, whose applicability to the considered problem is substantiated. As an intermediate result, the paper gives a closed-form solution of the optimal regulator (control) problem for stochastic nonlinear polynomial systems with a polynomial multiplicative noise, a linear control input, and a quadratic criterion. Performance of the obtained mean-square optimal data-based controller is verified in the illustrative example against the conventional LQG controller that is optimal for linearized systems. Simulation graphs demonstrating overall performance and computational accuracy of the designed optimal controller are included. (C) 2012 Elsevier Inc. All rights reserved.
To meet the demands of the modern power system for satisfactory operation and control, here, a novel data-driven control strategy is proposed to solve the load frequency control (LFC) problems of power systems, with c...
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To meet the demands of the modern power system for satisfactory operation and control, here, a novel data-driven control strategy is proposed to solve the load frequency control (LFC) problems of power systems, with complete convergence analysis. This data-based LFC approach is designed based on the simultaneous perturbation stochastic approximation (SPSA) method and neural network ensemble. The data-based controller is constructed using a function approximator, which is fixed as a neural network. Being the control parameters, the connection weights of the neural network controller are updated at each iteration step. In order to improve the overall control accuracy and get more stable control performance, the idea of neural network ensemble is introduced for the data-based controller structure design. The proposed data-based controller takes past and current system information as input and generates a control signal that can affect future system performance as output, and during the whole process, it is not necessary to build mathematical model for the controlled plant. A one-area LFC problem with system parametric uncertainties as well as a typical two-area LFC problem have been introduced for simulation tests, and the feasibility and effectiveness of this newly proposed data-based LFC strategy is well revealed through simulation results.
Hepatitis C is a liver disease caused by hepatitis C virus. Prevalence of the disease among people and spread of it in the society is becoming an important concern in epidemiology. In this study an ANFIS-based optimal...
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Hepatitis C is a liver disease caused by hepatitis C virus. Prevalence of the disease among people and spread of it in the society is becoming an important concern in epidemiology. In this study an ANFIS-based optimal control is developed to control hepatitis C virus epidemic. Genetic Algorithm has employed to select optimal control inputs based on the initial population of the different compartments and this process is repeated for several different initial conditions in order to create a set of data. Prepared data were used as input for ANFIS to train coefficients of Takagi-Sugeno fuzzy structure and finally this structure is used as the controller. Taking advantages of the proposed method, decreased population of the infected classes significantly in comparison with no control case. Moreover, employing our strategy decreased value of objective function about 16% with respect to past strategies which introduced in the literature. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
This paper presents an approach for designing gain-scheduled controllers for unknown systems using a set of measured data. In most control approaches, controllers are designed using a mathematical model, which is ofte...
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ISBN:
(纸本)9781479928903
This paper presents an approach for designing gain-scheduled controllers for unknown systems using a set of measured data. In most control approaches, controllers are designed using a mathematical model, which is often obtained on the basis of some simplifying assumptions. Thus, controllers designed through model-based methods may result in degradation of the desired closed-loop performance due to complex dynamics. Hence, the proposed approach is motivated by the fact that: 1) errors associated with the modeling process are avoided since no mathematical model is required for the controller design, 2) the designed adaptive controllers are able to ensure desired performance specifications for the plant operated not only at a given operating point but over a range of operating conditions, and 3) the controller structure can be selected a priori. A simulation example to control a water heating system is presented to validate the proposed method.
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