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To deal with constraints, updating law is designed by using model predictive control scheme.
The learning-based updating law is utilized to compensate for periodic time-varying parametric uncertainties.
First, a high-order ILC updating law is proposed to include tracking errors in more than one previous iteration.
For the batchwise direction, a robust ILC updating law is developed based on the two-dimensional (2D) control system theory.
A parameter updating law is obtained simultaneously, and the reactance modulated input is derived via the coordinated passivation approach.
First, an excitation control input and a parameter updating law are obtained simultaneously via adaptive back-stepping and Lyapunov methods to achieve stability of the zero dynamics subsystem.
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The updating laws are derived using both continuous and discrete time Lyapunov approaches.
Both open-loop and closed-loop PDα-type iterative learning updating laws are considered.
These conditions are represented in terms of linear matrix inequalities and adaptive updating laws.
The unknown kinematic couplings, parametric uncertainties, and bounded external disturbances are handled with adaptive updating laws.
Further, we extend the deterministic results to random case by designing ILC updating laws with randomly varying trial length.
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