Bounded Dynamic Stochastic Systems: Modelling and Control
Bounded Dynamic Stochastic Systems: Modelling and Control
Linear and Nonlinear Iterative Learning Control (Lecture Notes in Control and Information Sciences, 291)
PID controller design for output PDFs of stochastic systems using linear matrix inequalities
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Neural Networks
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In this paper, iterative learning control is presented to a dynamic neural network modelling process for nonlinear (stochastic) dynamical systems. When (B-spline) neural networks are used to model a nonlinear dynamical functional such as the output stochastic distributions in the repetitive processes or the batch processes, the parameters in the basis functions will be tuned using the generalized iterative learning control (GILC) scheme. The GILC scheme differs from the classical model-based ILC laws, with no dynamical input-output differential or difference equations given a prior. For the “model free” GILC problem, we propose an optimal design algorithm for the learning operators by introducing an augmented difference model in state space. A sufficient and necessary solvable condition can be given where a robust optimization solution with an LMI-based design algorithm can be provided.