Data-Driven Model-Free Adaptive Sliding Mode Control for Multi DC Motor Speed Regulation

This paper proposes the distributed data-driven model-free adaptive sliding mode control method to solve the consensus problem of nonlinear multi-agent systems. Firstly, the equivalent data model for each agent is constructed using the compact-form dynamic linearization (CFDL) technique. Secondly, by utilizing process information from neighboring agents, the novel sliding surface is utilized to ensure the boundedness of the distributed measurement error. Subsequently, a distributed model-free adaptive sliding mode controller is developed for accurate consensus tracking. Finally, the effectiveness of the proposed control approach is validated through experiments on multi DC motor system.

Keywords: Data-driven control, Model-free adaptive sliding mode control, Nonlinear multi-agent systems, Multi DC motor speed control.