No-Reset Iterative Learning Control
نویسندگان
چکیده
A No-Reset Iterative Learning Control (NRILC) system is an Iterative Learning Control (ILC) system where the plant is not reset at the beginning of each iteration. We compare NRILC with ILC and repetitive control systems in terms of structure. We apply this new scheme to discrete-time, LTI, SISO plants but the approach can be extended to linear time-varying and MIMO plants. We show that an NRILC equilibrium point exists if the desired trajectory lies in a certain well-deened subspace. The convergence of the system depends on the spectral radius of an equivalent system matrix. Using results from output feedback theory, we show that the closed-loop eigenvalues of the system can be placed almost always with the selection of an appropriate nite learning gain.
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