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In this paper, a discrete-time neural control scheme to regulate carbon monoxide (CO) and nitrogen oxides (NOx) emissions for a fluidized bed sludge incinerator is proposed. Carbon monoxide emissions are reduce by oxygen regulation in the incinerator; nevertheless nitrogen oxides emissions are difficult to control because the sludge composition varies continuously. This scheme ensures carbon monoxide...
In this paper, the authors propose a discrete-time neural control scheme to regulate nitrogen oxides (NOx) emissions for a fluidized bed sludge combustor. This scheme ensures carbon monoxide (CO) regulation without decreasing combustion efficiency. In order to obtain the sludge combustion model, it is proposed to use a recurrent high order neural network (RHONN) which is trained with an extended Kalman...
This paper describes the development of an inverse model for a direct current (DC) motor. The model consist of an Adaptive Network Fuzzy Inference System (ANFIS). The identification procedure includes: the experiment to collect data, ANFIS training and model validation in real-time. The obtained model is used to design a neuro-fuzzy inverse control strategy for trajectory tracking. The obtained real-time...
In this paper the torque and the square of the rotor flux magnitude control using second order sliding mode controller for an induction motor, is proposed. Designed super-twisting controller permits to reduce chattering that is inherent in standard sliding mode control and to improve accuracy. To estimate the rotor flux and speed, an adaptive observer is proposed. The effectiveness of the designed...
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...
This paper presents a discrete-time adaptive neural backstepping control for a double fed induction generator connected to an infinity bus, based on a discrete-time high order neural network (HONN), which is trained with an extended Kalman filter (EFK) algorithm. The discrete-time adaptive neural backstepping control performance is illustrated via simulations.
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 deals with adaptive trajectory tracking for discrete-time MIMO nonlinear systems. A high order neural network (HONN) is used to approximate a decentralized control law designed by the backstepping technique as applied to a block strict feedback form (BSFF). The HONN learning is performed online by an Extended Kalman Filter (EKF) algorithm. The proposed scheme is implemented in real-time...
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.
In this paper, the authors propose a block control scheme using sliding modes, for a double fed induction generator connected to an infinity bus. To estimate the mechanical torque, a nonlinear observer is utilized; additionally, as a viable alternative, to eliminate the requirement on this estimation, an integrator is included. To reduce the effect of parameter variations, the one step delayed disturbance...
This paper presents neuronal network identification of a wastewater treatment prototype. This identification is based on a discrete-time recurrent high order neuronal network (RHONN). The neuronal network is trained with an extended Kalman filter (EFK) algorithm. The neuronal identification performance is illustrated via simulations.
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...
In this paper, we propose a high order neural network (HONN) trained with an extended Kalman filter based algorithm to predict wind speed. Due to the chaotic behavior of the wind time series, it is not possible satisfactorily to apply the traditional forecasting techniques for time series; however, the results presented in this paper confirm that HONNs can very well capture the complexity underlying...
This paper presents neuronal network identification of a double fed induction generator, based on a discrete-time high order neuronal network (RHONN), which is trained with an extended Kalman filter (EFK) algorithm. The neuronal identification performance is illustrated via simulations.
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...
This paper deals with adaptive trajectory tracking for a five DOF robot manipulator, A high order neural network (HONN) is used to approximate a decentralized control law designed by the backstepping technique as applied to a block strict feedback form (BSFF). The HONN learning is performed online by an Extended Kalman Filter (EKF) algorithm. The applicability of the proposed scheme is illustrated...
This paper presents a neural network identification scheme to estimate substrate, biomass and dissolved oxygen concentrations in an activated sludge wastewater treatment. This scheme is based on a discrete-time high order neural network (RHONN) trained on-line with an extended Kalman filter (EKF)-based algorithm. Then, the identification scheme is associated with a fuzzy control to regulate the ratio...
In this paper, a recurrent neural networks observer for anaerobic processes is proposed; the main objective is to estimate biomass, in a completely stirred tank reactor. The neural network is trained with an extended Kalman filter algorithm. The applicability of the proposed observer is verified via simulations.
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