Robust adaptive iterative learning control for discrete-time nonlinear systems with both parametric and nonparametric uncertainties

A new robust adaptive iterative learning control approach is proposed for discrete-time nonlinear systems with both parametric and nonparametric uncertainties. By virtue of a well-designed dead-zone function, the learning of the parametric and nonparametric uncertainties can be performed concurrently. Rigorous Lyapunov function-based analysis ensures that the effect of system uncertainties can be fully compensated, and the tracking error will converge to zero asymptotically in the iteration domain, even under random initial conditions and iteration-varying reference trajectories. The efficacy of the proposed controller is demonstrated by simulating a single-link robot manipulator with unknown frictions. Copyright © 2015 John Wiley & Sons, Ltd.

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