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A state-space technique for control of nonlinear multi-input multi-output (MIMO) systems identified by an Additive Nonlinear Autoregressive eXogenous (ANARX) model is presented. Controlled system is identified by Neural Network based Simplified Additive NARX (NN-SANARX) model linearized by dynamic feedback. The neural network based model is represented in the discrete-time state-space form. The problem...
In this paper, a novel adaptive neural network control scheme is put forward for a class of SISO nonlinear systems with backlash-like hysteresis, where the hysteresis is modeled by a differential equation in the presence of bounded external disturbances. By using the merit of tangent function and the method of minimal parameterization, the proposed designs need no requirements for the knowledges of...
In this paper a self-tuning wavelet PID controller using wavelet networks is presented. The wavelet-based multiresolution PID controller was purpose by the ability of this controller is to provide good rejection to disturbances and smooth control signal. One of its disadvantages is that the tuning gains are in trial or error mode. A wavelet network to identify the system and to tune the gains of the...
The purpose of this comment is to point out some mistakes in the above paper. It is shown that the main results of the paper cannot stand in general. Also, it is pointed out that after some corrections, the proposed control algorithm is still applicable to a more simple system. For simplicity, all the symbols in this comment are the same as those in the above paper.
In this paper, an application of Neural Networks based Additive Nonlinear AutoRegressive eXogenous (NN-ANARX) structure is investigated for modeling and control of nonlinear multi-input-multi-output (MIMO) systems. A novel analytical technique for calculation of control signal is proposed. After that the ANARX-based dynamic output feedback linearization control algorithm is applied for control of...
The neural extended Kalman filter (NEKF) is an adaptive state estimation technique. The neural network training occurs while the system is in operation then the NEKF is able to learn on-line. The NEKF identifies mismodeled dynamics of the system to improve state estimation by learning the differences between the previous model and the measurements that it observes. The prediction from the NEKF can...
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