466 research outputs found
Discrete time neural control of a nonholonomic mobile robot integrating stereo vision feedback
No full textThe tracking control of nonholonomic mobile robots has been an important class of control problems. This paper deals with the design and real-time implementation of a discrete-time super twisting control algorithm for nonholonomic wheeled mobile robots, without the previous knowledged of the plant model or its parameters. In order to show the effectiveness of the proposed controller experimental results are included for a nonholonomic mobile robot Qbot® 3. " 2011 IEEE.",,,,,,"10.1109/ICEEE.2011.6106692",,,"http://hdl.handle.net/20.500.12104/40688","http://www.scopus.com/inward/record.url?eid=2-s2.0-84855791615&partnerID=40&md5=52b614f48306a1e061271dd39c87a6f4",,,,,,,,"CCE 2011 - 2011 8th International Conference on Electrical Engineering, Computing Science and Automatic Control, Program and Abstract Book",,,,,,"Scopus",,,,,,"Discrete control; Super twisting control",,,,,,"Discrete super twisting control algorithm for the nonholonomic mobile robots tracking problem",,"Conference Paper"
"42468","123456789/35008",,"Lopez-Franco, M., CINVESTAV, Unidad Guadalajara, Jalisco 45015, Mexico; Sanchez, E.N., CINVESTAV, Unidad Guadalajara, Jalisco 45015, Mexico; Alanis, A.Y., CUCEI, Universidad de Guadalajara, Apartado Postal 51-71, Col. las aguilas, C.P. 45080, Zapopan, Jalisco, Mexico; Lopez-Franco, C., CUCEI, Universidad de Guadalajara, Apartado Postal 51-71, Col. las aguilas, C.P. 45080, Zapopan, Jalisco, Mexico",,"Lopez-Franco, M
Neural model with particle swarm optimization Kalman learning for forecasting in smart grids
No full textThis paper discusses a novel training algorithm for a neural network architecture applied to time series prediction with smart grids applications. The proposed training algorithm is based on an extended Kalman filter (EKF) improved using particle swarm optimization (PSO) to compute the design parameters. The EKF-PSO-based algorithm is employed to update the synaptic weights of the neural network. The size of the regression vector is determined by means of the Cao methodology. The proposed structure captures more efficiently the complex nature of the wind speed, energy generation, and electrical load demand time series that are constantly monitorated in a smart grid benchmark. The proposed model is trained and tested using real data values in order to show the applicability of the proposed scheme. © 2013 Alma Y. Alanis et al
Special Section on Advances in Intelligent Control: Theory and Applications
No full text[No abstract available
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
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
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
Neural modeling of the blood glucose level for type 1 diabetes mellitus patients
No full textThis paper discusses a novel training algorithm for a neural network architecture applied to time series prediction with smart grids applications. The proposed training algorithm is based on an extended Kalman filter (EKF) improved using particle swarm optimization (PSO) to compute the design parameters. The EKF-PSO-based algorithm is employed to update the synaptic weights of the neural network. The size of the regression vector is determined by means of the Cao methodology. The proposed structure captures more efficiently the complex nature of the wind speed, energy generation, and electrical load demand time series that are constantly monitorated in a smart grid benchmark. The proposed model is trained and tested using real data values in order to show the applicability of the proposed scheme. " 2013 Alma Y. Alanis et al.",,,,,,"10.1155/2013/197690",,,"http://hdl.handle.net/20.500.12104/43068","http://www.scopus.com/inward/record.url?eid=2-s2.0-84879290075&partnerID=40&md5=cc3ccfb7130389dad494823eb1611e09",,,,,,,,"Mathematical Problems in Engineering",,,,"2013",,"Scopu
Inverse optimal control with speed gradient for a power electric system using a neural reduced model
No full textThis paper presented an inverse optimal neural controller with speed gradient (SG) for discrete-time unknown nonlinear systems in the presence of external disturbances and parameter uncertainties, for a power electric system with different types of faults in the transmission lines including load variations. It is based on a discrete-time recurrent high order neural network (RHONN) trained with an extended Kalman filter (EKF) based algorithm. It is well known that electric power grids are considered as complex systems due to their interconections and number of state variables; then, in this paper, a reduced neural model for synchronous machine is proposed for the stabilization of nine bus system in the presence of a fault in three different cases in the lines of transmission. � 2014 Alma Y. Alanis et al
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
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