A new recurrent neural network approach for on line adaptive control is presented. The resulting control scheme matches both the conventional and neural control methods. Using input-output data, a modified recurrent Elman’s network is trained to model a general non-linear discrete time system. By assuming a linearisation of this neural model a time varying adaptive observer is derived. Therefor...

A robust nonlinear observer, utilizing the sliding mode concept, is developed for the dynamic positioning of ships. The observer provides the estimates of linear velocities of the ship and bias from the slowly varying environmental loads. It also filters out wave frequency motion to avoid wear of actuators and excessive fuel consumption. Especially, the observer structure with a saturation func...

This paper presents a nonlinear adaptive observer based on multilayer perceptron (MLP) neural networks, which is indeed a nonlinear-in-parameters neural network (NLPNN), for a class of nonlinear systems including chaotic systems in which all MLP weights are tuned on-line. These systems can be decomposed into linear and nonlinear parts. It is assumed that the linear part is known while the nonli...

in this paper, an adaptive attitude control algorithm is developed based on neural network for a satellite using four reaction wheels in a tetrahedron configuration. then, an attitude control based on feedback linearization control has been designed and uncertainties in the moment of inertia matrix and disturbances torque have been considered. in order to eliminate the effect of these uncertain...

An adaptive version of growing and pruning RBF neural network has been used to predict the system output and implement Linear Model-Based Predictive Controller (LMPC) and Non-linear Model-based Predictive Controller (NMPC) strategies. A radial-basis neural network with growing and pruning capabilities is introduced to carry out on-line model identification.An Unscented Kal...

Kalman filter is an optimal filtering solution in certain cases, however, it is more often than not, regarded as a non-robust filter. The slight mismatch in noise statistics or process model may lead to large performance deterioration and the loss of optimality. This research paper proposes an alternative method for robust adaptive filtering concerning lack of information of noise statistics. T...

This investigation applies the adaptive fuzzy-neural observer (AFNO) to synchronize a class of unknown chaotic systems via scalar transmitting signal only. The proposed method can be used in synchronization if nonlinear chaotic systems can be transformed into the canonical form of Lur'e system type by the differential geometric method. In this approach, the adaptive fuzzy-neural network (FNN) i...