The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
In this paper a novel type reducer for interval type-2 fuzzy systems is proposed. Type reduction of interval type-2 fuzzy systems requires the solution of two nonlinear constrained optimization problems. Existing exact solutions to these problems (Karnik-Mendel algorithms and their variants) require sorting which is known to be computationally very expensive. In this research, these optimization problems...
A nonlinear control allocation scheme is developed using hybrid optimization algorithm. To achieve nonlinear control allocation results, a hybrid optimization algorithm is presented in which the ant colony algorithm and differential evolution algorithm are employed. The nonlinear control allocation problem is divided into two optimal problems which are selecting optimal truncation point combination...
The paper investigates generalized H∞ model reduction for two-dimensional (2-D) systems represented by the Roesser model and the Fornasini-Machesini local state-space model, respectively. The generalized H∞ norm of 2-D systems is introduced to evaluate the approximation error over a specific finite frequency (FF) domain. In light of the 2-D generalized Kalman-Yakubovich-Popov lemmas, sufficient conditions...
In this work, we deal with a class of uncertain nonlinear plants along with external high-frequency disturbances. A novel controller structure consisting of two neural networks (NNs) and a low-pass filter is proposed. One NN is utilized to approximate an ideal control law and the other one to approximate the derivative of the output of the former NN. The smoothness of the control signal is guaranteed...
This paper, based on radial basis function (RBF) neural network, presents an novel adaptive robust controller for a class of strict-feedback uncertainty nonlinear systems to address the tracking problem. The proposed approach, takes advantage of RBF neural network approximation property to approximate system uncertainties, and utilizes adaptive backstep-ping techniques for eliminating the effects...
In this paper, a novel robust nonlinear control scheme is proposed for a basic current-controlled active magnetic bearing (AMB) that is non-affine in the control input. First, a dynamic model approximation technique is developed to facilitate the controller design. Then, since the control characteristics of the AMB are highly nonlinear and time-varying with external disturbance, the robust nonlinear...
The aim of this paper is to control the position of two balls in interconnected vertical tubes using two dc fans, ultrasonic sensors and computerized controller. A comparison of different kinds of controllers is performed to find the best adjustment. Indeed, by controlling the speed of a fan that pumps the air toward a ball, the air pressure under the ball will be adjusted such that it suspends the...
A methodology is presented in this paper for stochastic optimal control of unmanned aerial vehicle performing the task of perimeter patrol. The optimal control problem is modeled as a Markov decision processes, and an approximate policy iteration algorithm is used for the cost-to-go function (value function) by introducing Gaussian process regression, resulting in improved quality of the decisions...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.