Multiobjective algebraic synthesis of neural control systems by implicit model following
IEEE Transactions on Neural Networks
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In this paper, a dynamic neurocontroller for positioning of robots based on static and parametric neural networks (NNs) has been developed. This controller is based on Christoffel symbols of first kind in order to carry out coriolis/centripetal matrix. Structural properties of robots and Kronecker product has been taken into account to develop NNs to approximate nonlinearities. The weight updating laws have been obtained from a nonlinear strategy based on Lyapunov energy that guarantees both stability and boundedness of signals and weights. The NN weights are tuned online with no "offline learning phase" and are initialized to zero. The neurocontroller improves the implementation with respect to other dynamic NNs used in the literature