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An artificial neural network (ANN) based current controller for a HVDC transmission link is described in this paper. Different ANN architectures and activation functions (AFs) are investigated for this ANN controller. Small (set current change) and large (dc-line fault) signal perturbations are applied to optimize the learning parameters for the controller. Performance evaluation of the ANN controller under noise conditions is studied. A comparison between a traditional PI and the proposed ANN controller is made for various system contingencies and it is shown that the latter has many attractive features.

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This paper presents a stable neural identifier for multivariable nonlinear systems. A state-space representation is considered based on both parallel and series-parallel models. No a priori knowledge about the nonlinearities of the system is assumed. The proposed learning rule is a novel approach based on the modification of the backpropagation algorithm. The boundedness of the identification error is shown using Lyapunov's direct method. As a case study, identification of the dynamics of a flexible-link manipulator is considered to demonstrate the effectiveness of the proposed algorithm. Simulation results for a two-link planar manipulator and the Space Station Remote Manipulator System (SSRMS) are presented.

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