Observer-based adaptive iterative learning control for nonstrict-feedback systems with state constraints

This paper investigates the adaptive iterative learning control strategy for nonstrict-feedback nonlinear systems with state constraints, which perform repetitive tasks over the operation time interval. Based on backstepping technique, the adaptive iterative learning controller overcomes the algebraic loop problem caused by the nonstrict-feedback structure. A neural state observer is constructed to estimate the unavailable states. The hybrid time/iteration domain adaptation mechanism is utilised to estimate the unknown nonlinear functions comprising all state variables. Based on Lyapunov-like synthesis, a tan-type barrier Lyapunov function is introduced into the composite energy function, which can guarantee the tracking error convergence along the iteration axis without violating the constraint requirement. Finally, the simulation results illustrate the effectiveness of the proposed control schemes.