Adaptive sliding mode control without knowledge of uncertainty bounds
It addresses the practical challenge of controlling uncertain nonlinear systems without needing to know uncertainty bounds, which is a common limitation in sliding mode control.
This paper introduces an adaptive sliding mode control method for nonlinear systems with time-varying bounded uncertainties that does not require prior knowledge of uncertainty bounds. The approach reduces controller gain to the minimum possible value and eliminates chattering, as demonstrated in simulations.
This paper proposes a new adaptation methodology to find the control inputs for a class of nonlinear systems with time-varying bounded uncertainties. The proposed method does not require any prior knowledge of the uncertainties including their bounds. The main idea is developed under the structure of adaptive sliding mode control; an update law decreases the gain inside and increases the gain outside a vicinity of the sliding surface. The semi-global stability of the closed-loop system and the adaptation error are guaranteed by Lyapunov theory. The simulation results show that the proposed adaptation methodology can reduce the magnitude of the controller gain to the minimum possible value and smooth out the chattering.