Error-Driven Iterative Learning Sliding Mode Control for Robust Tracking under Uncertainty
DOI:
https://doi.org/10.30537/4cr1at02Keywords:
Error-Driven Control, Iterative Learning Control, Sliding Mode Control, Analysis of Convergence, Analysis of LyapunovAbstract
One of the most important problems in control engineering is to achieve high -precision tracking of nonlinear systems with uncertain dynamics. Although model-based iterative learning sliding mode control (ILSMC) has good performance, it is restricted to systems with time-varying or unknown dynamics because the method requires that the system can be identified accurately. The paper proposes a strategy of Error-Driven ILSMC, which constructs the control input directly from past tracking error information, thereby reducing its reliance on an accurate system model. It is an iterative learning update law that is combined with a high-gain sliding term and guaranteed to converge using Lyapunov analysis. The controller is evaluated on the Quanser SRV02 motor-driven system under parametric uncertainty, load disturbance, and measurement noise. Monte Carlo simulation over more than iterations and randomized experiments indicates that position RMSE and velocity RMSE are improved by and respectively, compared to the ML-ILSMC. The proposed strategy provides both theoretical assurances and practical implementation advantages, making it a potential solution for robust control in systems where an accurate dynamic model is difficult to obtain.
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