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In this paper, non-linear systems (hydraulic tank configurations) were analyzed using a technique of fault detection and isolation based on a Takagi-Sugeno fuzzy model. The system state vector was obtained by means of sliding mode observers, and then the signal-residual is generated by comparing the estimated and measured outputs. The isolation problem was solved using Neural Networks. From the resulting...
In this paper, a discrete-time Linear Parameter-Varying (LPV) system identification method using artificial neural network is described. In particular, neural network is transformed to obtain LPV model of the non-linear system. Moreover, a novel robust fault diagnosis scheme is developed, which is based on an observer within H∞ framework for a class of non-linear systems. The effectiveness of the...
A neural network observer is developed for ship dynamic positioning system with uncertain dynamics and disturbances in this paper. For ship dynamic positioning system, in most cases the position measurements are available but the velocity measurements are not. In addition, the measured position and heading signals are often corrupted by noises, which may reduce the performance of the dynamic positioning...
In this paper, the distributed attitude coordinated tracking controller is presented for multi-group spacecrafts based on input normalized adaptive neural network. In contrast to the existing works about spacecraft formation flying(SFF), where all spacecrafts reach the same reference attitude asymptotically, we require that all spacecrafts track several leaders and each spacecraft only synchronize...
This paper concerns the development of a robust asymptotic neuro observer for a class of unknown nonlinear systems. The Luenberger type observer in this system have two important terms, the first term assures the boundness of the weights and in second term has a time delayed term, which approximates the derivatives of the measurable states. The Lyapunov-Krasovskii technique is used to proof the robust...
This paper deals with the problem of designing an observer-based adaptive tracking controller for a class of uncertain nonlinear systems. A neural network-based observer estimates states of the system and a neural network-based controller is designed to approximate input control signal. The estimated states by the observer are inputs of the controller and two neural networks (NNs) interact together...
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