1,292 research outputs found
Second order sliding mode for MIMO nonlinear uncertain systems based on a neural identifier
No full textThis paper deals with adaptive tracking for discrete-time nonlinear systems in presence of disturbances. A high order neural network structure is used to identify the plant model and based on this model, a discrete-time high order sliding mode, control law is derived. The paper also includes the respective stability analysis, for the whole system with a strategy. In order to show the applicability of the proposed scheme, simulation results are included for a Van der Pol oscillator
Discrete-time reduced order neural observers
No full textA nonlinear discrete-time reduced order neural observer for the state estimation of a discrete-time unknown nonlinear system, in presence of external and internal uncertainties is presented. The observer is based on a discrete-time recurrent high order neural network (RHONN) trained with an extended Kalman filter (EKF)-based algorithm. This observer estimates the state of the unknown discrete-time nonlinear system, using a parallel configuration. To illustrate the applicability simulation results are included. � 2009 Springer-Verlag Berlin Heidelberg
Second order sliding mode for MIMO nonlinear uncertain systems based on a neural identifier
No full textThis paper deals with adaptive tracking for discrete-time nonlinear systems in presence of disturbances. A high order neural network structure is used to identify the plant model and based on this model, a discrete-time high order sliding mode, control law is derived. The paper also includes the respective stability analysis, for the whole system with a strategy. In order to show the applicability of the proposed scheme, simulation results are included for a Van der Pol oscillator
Discrete-time reduced order neural observers
No full textA nonlinear discrete-time reduced order neural observer for the state estimation of a discrete-time unknown nonlinear system, in presence of external and internal uncertainties is presented. The observer is based on a discrete-time recurrent high order neural network (RHONN) trained with an extended Kalman filter (EKF)-based algorithm. This observer estimates the state of the unknown discrete-time nonlinear system, using a parallel configuration. To illustrate the applicability simulation results are included. © 2009 Springer-Verlag Berlin Heidelberg
Reflections on the impact of the new economic, sociological and historical institutionalism in institutional social policy
No full textThis Chapter presents the design of an adaptive recurrent neural observer-controller scheme for nonlinear systems whose model is assumed to be unknown and with constrained inputs. The control scheme is composed of a neural observer based on Recurrent High Order Neural Networks which builds the state vector of the unknown plant dynamics and a learning adaptation law for the neural network weights for both the observer and identifier. These laws are obtained via control Lyapunov functions. Then, a control law, which stabilizes the tracking error dynamics is developed using the Lyapunov and the inverse optimal control methodologies. Tracking error boundedness is established as a function of design parameters. " 2010, IGI Global.",,,,,,"10.4018/978-1-61520-711-4.ch013",,,"http://hdl.handle.net/20.500.12104/44119","http://www.scopus.com/inward/record.url?eid=2-s2.0-84898557307&partnerID=40&md5=2bb5e8db27d2fa7dbcdd5ba82d0a02b4",,,,,,,,"Artificial Higher Order Neural Networks for Computer Science and Engineering: Trends for Emerging Applications",,"28
Recurrent higher order neural network control for output trajectory tracking with neural observers and constrained inputs
No full textThis Chapter presents the design of an adaptive recurrent neural observer-controller scheme for nonlinear systems whose model is assumed to be unknown and with constrained inputs. The control scheme is composed of a neural observer based on Recurrent High Order Neural Networks which builds the state vector of the unknown plant dynamics and a learning adaptation law for the neural network weights for both the observer and identifier. These laws are obtained via control Lyapunov functions. Then, a control law, which stabilizes the tracking error dynamics is developed using the Lyapunov and the inverse optimal control methodologies. Tracking error boundedness is established as a function of design parameters. © 2010, IGI Global
Conclusions and future work
No full textThe first designed robust direct neural control scheme is based on the backstepping technique, approximated by a high order neural network. On the basis of the Lyapunov approach, the respective stability analysis, for the whole closed-loop system, including the extended Kalman filter (EKF)-based NN learning algorithm, is also performed. The second robust indirect control is designed with a recurrent high order neural network, which enables to identify the plant model. A strategy to avoid specific adaptive weights zero-crossing and conserve the identifier controllability property is proposed. Based on this neural identifier and applying the discrete-time block control approach, a nonlinear sliding manifold with a desired asymptotically stable motions was formulated. Using a Lyapunov functions approach, a discrete-time sliding mode control that makes the designed sliding manifold to be attractive was introduced. � 2008 Springer-Verlag Berlin Heidelberg
Real time implementation
No full textIn this chapter real time implementation is presented in order to validate the theoretical results discussed in previous chapters. The results presented in this chapter include the Neural Network Identification scheme presented in Chap. 4, the RHONO presented in Chap. 5, the Neural Backstepping Approach analyzed in Chap. 3, the Neural Bock Control Technique discussed in Chap. 4 and the modifications of the last two controllers treated in Chap. 6 to include the RHONO. All these applications was performed using a three phase induction motor. � 2008 Springer-Verlag Berlin Heidelberg
Discrete-time neural observers
No full textThis chapter 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 and it has a Luenberger structure. The learning algorithm for the RHONN is implemented using an extended Kaiman filter (EKF). The respective stability analysis, on the basis of the Lyapunov approach, is included for the observer trained with an EKF and simulation results are included to illustrate the applicability of the proposed scheme. � 2008 Springer-Verlag Berlin Heidelberg
Freshwater ostracods as environmental tracers
No full textIn this paper, the implementation of a discrete-time neural model in an field programmable gate array (FPGA) is proposed to model insulin-glucose dynamics of type 1 diabetes mellitus (T1DM) patients. The neural model is obtained from an on-line neural identifier, which uses a recurrent high-order neural network (RHONN) trained with an extended Kalman filter (EKF), which captures the nonlinear behavior of this dynamics. Experimental data given by continuous glucose monitoring (CGM) device are utilized for identification. " 2014 TSI Press.",,,,,,"10.1109/WAC.2014.6936098",,,"http://hdl.handle.net/20.500.12104/41591","http://www.scopus.com/inward/record.url?eid=2-s2.0-84908868920&partnerID=40&md5=8edc1d4008685b130facfb9bde24dcee",,,,,,,,"World Automation Congress Proceedings",,"67
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