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On-line system identification of linear time-varying (LTV) systems whose system parameters change in time has been studied lately. One neural network based such on-line identification method was studied by the author with a generalized adaptive linear element (ADALINE). Since the ADALINE is slow in convergence, which is not suitable for identification of LTV system, one technique was proposed to speed...
Deterministic learning control was investigated recently. Due to the existence of time varying disturbances, learning capability may be influenced. In this paper, deterministic learning theory is analyzed in environments with disturbances. With an appropriately designed neural adaptive controller, the disturbances are attenuated and partial persistent excitation (PE) condition for the radial basis...
Deterministic learning theory was presented and investigated recently. Due to the existence of time varying disturbances, learning capability may be influenced. In this paper, deterministic learning theory will be analyzed in environments with disturbances. With appropriately designed adaptive neural controller, the disturbances are attenuated and partial persistent excitation (PE) for radial basis...
This paper presents a novel robust adaptive trajectory linearization control (RATLC) method for a class of uncertain nonlinear systems based on a single hidden layer neural networks disturbance observer (SDO). The term ldquodisturbancerdquo used in this paper refers to the combination of model uncertainties and external disturbances. By utilizing the universal approximation property of neural networks...
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