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Stochastic distribution control (SDC) for non-Gaussian system is a mathematically complicated yet practical problem to solve. The most recent solution involves a radial basis function neural network (RBFNN) framework to approximate non-Gaussian output probability density function (PDF). The dynamic weights of such neural network are controlled within each batch of ILC, using a dedicated adaptive controller...
This paper presents a novel feedback control method for networked control systems by combining the P-type iterative learning control (ILC) idea with minimum tracking error entropy control strategy. In specific, the time-delayed control system of robotic manipulator is considered, where the convergence of the proposed control law is proved. The stochastic characteristics of the tracking error are analyzed...
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