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Based on the concept of persistent excitation (PE), a deterministic learning algorithm is proposed for neural network (NN)-based identification of nonlinear systems recently. This paper investigates the quantitative relationship between the PE levels (including the level of excitation), the architectures of NNs and the convergence properties of deterministic learning, which is motivated by a practical...
This paper focuses on the tracking control of nonholonomic mobile robots. A kinematic control law and a dynamic control law are presented using backstepping theory. Furthermore, neural network control law is used to compensate for modelling error and approximate external disturbances in order to achieve the desired tracking performance. The global uniformly asymptotic stability of the system is guaranteed...
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