Barrier Robust Iterative Learning Control for Nonlinear Systems With Both Nonparametric Uncertainties and Time-Iteration-Varying Parametric Uncertainties Under Alignment Condition

Abstract

In this work, a barrier robust iterative learning control approach for a class of nonlinear systems with both nonparametric uncertainties and time-iteration-varying parametric uncertainties is studied. The nonparametric uncertainties meet Lipschitz-like continuous condition, and the time-iteration-varying parametric uncertainties are generated by a high-order internal model(HOIM). A barrier Lyapunov function is adopted for controller design to achieve system constraints. In light of the fact that the reference trajectory is smoothly closed, alignment condition is used to overcome the initial position problem of ILC. Robust learning method is used to compensate for the nonparametric uncertainties. According to the characteristic of HOIM, the time-iteration-varying parametric uncertainties is estimated by using difference learning method. Excellent tracking performance may be obtained as the iteration number increases, with the error quadratic form constrained during each iteration. Numerical Simulation results show the effectiveness of the propose barrier robust iterative learning control scheme.

Keywords

• Nonlinear systems
• iterative learning control
• high-order internal model
• barrier Lyapunov function
• alignment condition