Adaptive Iterative Learning Control for Full States Constrained non-Strict Feedback Nonlinear Systems

This paper presents an adaptive iterative learning control scheme for state-constrained non-strict feedback nonlinear systems performing the same operation during a finite time interval under the alignment condition. A systematic design procedure is constructed via the barrier Lyapunov function combined with a command filter adaptive backstepping. The barrier Lyapunov function keeps all states within a predefined region. Command filter solves the problems of restrictions on high order differentiability of virtual learning controller and avoids the complexity that arises due to the explosion of terms in adaptive backstepping design. A new barrier composite energy function is used to prove the boundedness of all signals of the resulting closed-loop system and the convergence of the tracking errors to zero over iterations. Simulation results are provided to show the effectiveness of the proposed control scheme.