Fuzzy System-Based Position Tracking Iterative Learning Control for Tank Gun Control Systems with Error Constraints

Abstract

In order to get accurate position tracking and effective system constraint together for tank gun control systems, a fuzzy system-based barrier adaptive iterative learning control scheme is proposed. Firstly, the error tracking strategy is applied to solve the initial position problem of tank gun iterative learning control systems. Then, a barrier Lyapunov function is adopted to controller design for the system constraint. In addition, a fuzzy system is used as an approximator to compensate for the nonparametric uncertainties, and difference learning learning approach is used to estimate the optimal parameters of fuzzy systems. It is shown that the system constraints are guaranteed and position tracking error converges to a tunable residual set as the iteration number increases.