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In this paper, a novel radial basis function (RBF) neural network is proposed and applied successively for online stable identification and control of nonlinear discrete-time systems. The proposed RBF network has one hidden layer neural network (NN) with its all parameters being adaptable. The RBF network parameters are optimized by gradient descent method with stable learning rate whose stable convergence...
Using the repeatability of controlled system, a new feedback-feedforward configuration for the iterative learning control (ILC) based on model free adaptive control (MFAC), which consists of a feedback and a feedforward component, is presented for a general discrete-time nonlinear system and rigorous convergence is proved. The effectiveness and advantage of the proposed learning control method is...
A nonaffine discrete-time system represented by the nonlinear autoregressive moving average with eXogenous input (NARMAX) representation with unknown nonlinear system dynamics is considered. An equivalent affinelike representation in terms of the tracking error dynamics is first obtained from the original nonaffine nonlinear discrete-time system so that reinforcement-learning-based near-optimal neural...
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