Research on Robust Adaptive and Efficient Control System: A Theoretical Approach

Abstract

In this paper, a robust adaptive repetitive control algorithm is presented for periodically time-varying systems. Periodic time-varying parameter estimation through periodic learning algorithm, and the uncertainty of aperiodic was robust adaptive method. Different from the existing repetitive control, this paper introduces the design of a new variable cycle number control. Convergence error when the number increase will gradually decrease due to the cyclical repetition character system, in order to ensure the global asymptotic stability. Further, this method is applied to a class of nonlinearly parameterized systems with non-parametric disturbances, and the tracking error converges asymptotically. The results verify the simulation model of the inverse pendulum. In addition, it is proved that the proposed design method is applied to eliminate the influence of approximation error of neural network. Theoretical analysis shows that the system output is convergent to the desired one and all signals in the network based robust adaptive repetitive control system are bounded. The experimental result illustrates the effectiveness of our proposed methodology.