Data-Driven Adaptive Sliding Mode Control of Nonlinear Discrete-Time Systems With Prescribed Performance
This paper deals with the issue of data-driven adaptive sliding mode control for a family of discrete-time nonlinear processes with tracking error constraint. More specifically, a novel transformed error algorithm together with a new sliding mode control framework is investigated to ensure the tracking error converges to a predefined zone all the time. Moreover, the proposed controller can guarantee a convergence rate and steady-state behavior for the tracking error depending only on the input/output measurement data, which is more effective in the complex industrial processes. Simulations are given to validate the theoretical results.