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.
2006 IEEE Conference on Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006 IEEE International Symposium on Intelligent Control
In (D. Cheng et. al, 2004),(D. Cheng et. al, 2005), it is proved that for a given controllable pair (A, B) with A isin Ropfn times n, B isin Ropfn times m, and any lambda > 0, a gain matrix K can be designed so that pare(A + BK)t par les MlambdaLe-lambdat where M and L are constants independent of A. Here we show that M and L can be chosen much smaller than that proposed in (D. Cheng et. al, 2004),(D...
In this study, we applied biorthogonal wavelets to extract essential features of the ballistocardiogram (BCG) signal and to classify them using a novel neural network so-called supervised fuzzy adaptive resonance theory (SF-ART). SF-ART has two stages. At first stage, pre-classification level, the input data is clustered roughly to arbitrary (M) classes using self-organized fuzzy ART tuned for fast...
A new class of adaptive nonlinear Hinfin control systems for nonlinear and time-varying processes which include nonlinear parametric models approximated by neural networks (NN), is proposed in this manuscript. Those control schemes are derived as solutions of particular nonlinear Hinfin control problems, where unknown system parameters, approximation and algorithmic errors in NN, and estimation errors...
This paper presents the design of an adaptive recurrent neural observer for nonlinear systems, whose mathematical model is assumed to be unknown. The observer is based on a recurrent high order neural network (RHONN), which estimates the state vector of the unknown plant dynamics. The learning algorithm for the RHONN is based on an extended Kalman filter. This paper also includes the respective stability...
This paper presents a neural network based direct adaptive control scheme for a class of affine nonlinear systems which are exactly input-output linearizable by nonlinear state feedback. When the system dynamics are completely unknown, the control input comprises two terms. One is an adaptive feedback linearization term and the other one is a sliding mode term. The neural networks weight update laws...
The neural extended Kalman filter has been shown to be able to work and train on-line in a control loop and as a state estimator for maneuver target tracking. Often, however, an adaptive component in the feedback loop is not considered desirable by the designer of a control system. Instead, the tuning of parameters is considered to be more acceptable. The ability of the NEKF to learn dynamics in an...
A neural network (NN) controller in discrete time is designed to deliver a desired tracking performance for a class of uncertain nonlinear systems with unknown deadzones and magnitude constraints on the input. The NN controller consists of two NNs: the first NN for compensating the unknown deadzones; and the second NN for compensating the uncertain nonlinear system dynamics. The magnitude constraints...
An adaptive neuro-fuzzy controller is proposed in this paper to deal with the problem of tracking nonlinear affine in the control dynamical systems with unknown nonlinearities. The plant is described by means of a Takagi-Sugeno fuzzy model, including dynamic fuzzy rules of generalized form, where the local submodels are realized through nonlinear input-output mappings. Instead of modelling the plant...
Direct search methods are local optimization algorithms maximizing a possibly nonsmooth and nonconvex function using its values only (no first-order gradient or second-order Hessian information). More than one decade ago, N. J. Higham implemented some simple Matlab routines for direct search optimization, investigating questions on stability and accuracy of numerical algorithms in matrix computations...
This paper studies the problem of adaptive robust iterative learning control for trajectory-tracked task of a class of robotic systems with both structured and unstructured uncertainties. A composite control scheme is proposed in which the periodic uncertainties are approached by the learning controller, while the effect of non-periodic uncertainties on system performances is attenuated by the robust...
In this paper, we show an overview of VSF-network, the presumption of parameters for the additive learning, results of the learning applied to obstacle avoidance task using the presumed parameters, and we examined the state of the hidden-layer in VSF-network that the additive learning is applied. The recognition of patterns that are the learned the existing pattern, the incrementally learned pattern,...
In this paper, robust adaptive neural tracking control is developed for a class of uncertain SISO nonlinear systems in a Brunovsky form with unknown nonlinear dead-zone and unknown control gain & its sign. The design is based on the principle of sliding mode control and the use of Nussbaum-type function in solving the problem of the completely unknown function control gain. A novel description...
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.