Data-driven sliding mode control for partially unknown nonlinear systems

2024/03/24 by Jianglin Lan, Lan, Jianglin, Xianxian Zhao +3 · 1 citation
Engineering · #Advanced Control Systems Optimization #Artificial intelligence #Computer science #Control (management) #Control theory (sociology) #FOS: Electrical engineering #Fault Detection and Control Systems #Iterative Learning Control Systems #Mode (computer interface) #Nonlinear system #Physics #Sliding mode control #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2403.16136

openalex publication_date 2024/03/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper presents a new data-driven control for multi-input, multi-output nonlinear systems with partially unknown dynamics and bounded disturbances. Since exact nonlinearity cancellation is not feasible with unknown disturbances, we adapt sliding mode control (SMC) for system stability and robustness. The SMC features a data-driven robust controller to reach the sliding surface and a data-driven nominal controller from a semidefinite program (SDP) to ensure stability. Simulations show the proposed method outperforms existing data-driven approaches with approximate nonlinearity cancellation.

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• Data-Driven Adaptive Second-Order Sliding Mode Control with Noisy Data

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