Data-driven sliding mode control for partially unknown nonlinear systems

TL;DR

This paper introduces a novel data-driven sliding mode control approach for complex nonlinear systems with unknown dynamics, enhancing robustness and stability through a combination of data-driven controllers and semidefinite programming.

Contribution

It develops a new data-driven control scheme that combines sliding mode control with SDP-based nominal control for partially unknown nonlinear systems.

Findings

Outperforms existing data-driven methods in simulations

Ensures system stability and robustness

Effectively handles unknown nonlinearities and disturbances

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.