On initial conditions in iterative learning control
Initial conditions, or initial resetting conditions, play a fundamental role in all kinds of iterative
learning control methods. In this work we study five different initial conditions, disclose the
inherent relationship between each initial condition and corresponding learning
convergence (or boundedness) property. The iterative learning control method under
consideration is based on Lyapunov theory, which is suitable for plants with time varying
parametric uncertainties and local Lipschitz nonlinearities.
learning control methods. In this work we study five different initial conditions, disclose the
inherent relationship between each initial condition and corresponding learning
convergence (or boundedness) property. The iterative learning control method under
consideration is based on Lyapunov theory, which is suitable for plants with time varying
parametric uncertainties and local Lipschitz nonlinearities.
Initial conditions, or initial resetting conditions, play a fundamental role in all kinds of iterative learning control methods. In this work we study five different initial conditions, disclose the inherent relationship between each initial condition and corresponding learning convergence (or boundedness) property. The iterative learning control method under consideration is based on Lyapunov theory, which is suitable for plants with time varying parametric uncertainties and local Lipschitz nonlinearities.
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