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In this paper, an approximation-based robust adaptive tracking control is developed for a class of uncertain multiple-input and multiple-output (MIMO) nonlinear systems with unknown disturbances and input saturation. Radial basis function neural networks (RBFNNs) are used to approximate the function uncertainties of MIMO nonlinear systems. An auxiliary design system is introduced to analyze the constraint...
The paper deals with the problem of model reference adaptive control of a class of uncertain nonlinear systems by output feedback based on neural networks. The uncertainty of the system can not be parameterized and its upper bound is unknown. In order to approximate the uncertainty via neural networks, a technique of global approximation of continuous functions is introduced. Based on the technique,...
An adaptive neural network control scheme is developed for a class of nonlinear systems in the strict-feedback form. Compared with the existing approaches, the main advantage is that the developed scheme can be implemented by utilizing only one neural network approximator. Thus, the designed controller structure is simplified. In addition, less neural network can reduce the running cost in practical...
The paper considers the problem of global adaptive tracking for a class of uncertain nonlinear systems in which the uncertainty is impossible to be parameterized. With the help of the technique of unit partition in differential topology, a result on global approximation of function using neural networks is proved. Based the result, a method of global adaptive neural network control for the uncertain...
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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