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This paper presents an inverse optimal neural controller, which is constituted by the combination of two well known techniques: (a) inverse optimal control to avoid solving the Hamilton Jacobi Bellman (HJB) equation associated to nonlinear system optimal control, and (b) an on-line neural identifier, which uses a recurrent neural network, trained with the extended Kalman filter (EKF), in order to...
A 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...
This paper presents a discrete-time decentralized control scheme for identification and trajectory tracking of a two degrees of freedom (DOF) robot manipulator. A recurrent high order neural network (RHONN) structure is used to identify the plant model and based on this model, a discrete-time control law is derived, which combines discrete-time block control and sliding modes techniques. The neural...
This paper presents a discrete-time direct current (DC) motor torque tracking controller, based on a recurrent high order neural network (RHONN) to identify the plant model. Using this model, a control law is derived, which combines block control and sliding modes techniques. The applicability of the scheme is illustrated via real time implementation for a DC motor with separate winding excitation.
This paper presents a hybrid intelligent control based on nonlinear PI controller and a recurrent high order neural network (RHONN) identifier. This control scheme is applied to a wastewater treatment prototype. The hybrid intelligent control and neuronal identification performance is illustrated via simulations.
This paper deals with the discrete-time adaptive output trajectory tracking for induction motors in presence of bounded disturbances. A recurrent high order neural network structure is used to design a nonlinear observer and based on this model, a discrete-time control law is derived, which combines discrete-time block control and sliding modes techniques. The paper also includes the respective stability...
This paper presents a discrete-time decentralized control scheme for identification and trajectory tracking of a five degrees of freedom (DOF) robot manipulator. A recurrent high order neural network (RHONN) structure is used to identify the robot model, and based on this model a discrete-time control law is derived, which combines discrete-time block control and sliding modes techniques. The neural...
An adaptive tracking controller for a discrete-time direct current (DC) motor model in presence of bounded disturbances is presented. A high order neural network is used to identify the plant model; this network is trained with an extended Kalman filter. Then, the discrete-time block control and sliding modes techniques are used to develop the reference tracking control. This paper includes also the...
This paper presents a recurrent neural observer to estimate substrate and biomass concentrations in an activated sludge wastewater treatment. The observer is based on a discrete-time high order neural network (RHONN) trained on-line with an extended Kalman filter (EKF)-based algorithm. This observer is then associated with a hybrid intelligent system to control the substrate/biomass concentration...
This paper deals with the problem of controlling the discrete-time induction motor model based on a sensorless observer with only currents measurements. First a recurrent high order neural observer for the unknown plant is designed, then a high order neural network is used to emulate a control law designed by the backstepping technique. The learning algorithm for both neural networks is based on an...
This paper presents the design of an adaptive controller based on the block control technique, and a new neural observer for a class of MIMO discrete-time nonlinear systems. The observer is based on a recurrent high-order neural network (RHONN), which estimates the state vectors of the unknown plant dynamics. The learning algorithm for the RHONN is based on an extended Kalman filter (EKF). This paper...
This paper presents the design of an adaptive controller based on the block control technique, and a new neural observer for a class of MIMO discrete-time nonlinear systems. The observer is based on a recurrent high-order neural network (RHONN), which estimates the state vectors of the unknown plant dynamics. The learning algorithm for the RHONN is based on an extended Kalman filter (EKF). This paper...
This paper deals with the problem of trajectory tracking for delayed recurrent neural networks. The tracking error is global asymptotic stabilized by a control law derived on the basis of a Lyapunov-Krasovsky functional. Then, it is established that this control law minimizes a meaningful cost functional. Applicability of the approach is illustrated by means of an example
In this paper, the authors discuss a new synthesis approach to train associative memories, based on recurrent neural networks. They propose to determine the weight vector as the optimal solution of a linear combination of support patterns. The proposed training algorithm maximizes the margin between the training patterns and the decision boundary. The design problem considers: (1) obtaining of weights...
This paper deals with the adaptive tracking problem for discrete-time induction motor model in presence of bounded disturbances. In this paper, a high order neural network structure is used to identify the plant model and based on this model, a discrete-time control law is derived, which combines discrete-time block control and sliding modes techniques. The paper also includes the respective stability...
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