Adaptive sliding mode control for a class of nonlinear systems using an uncertainty identification observer

This study proposes a control method for a class of uncertain nonlinear systems by combining an observer with an adaptive sliding-mode controller (SMC). With a particular focus on two main contributions: First, an observer based on a radial basis function (RBF) neural network is proposed to estimate the uncertain nonlinear dynamics of the system. The proposed observer has a simple structure without increasing the system order, offering a significant advantage over conventional observers that often require an increase in system order. Next, an adaptive SMC is proposed for reference tracking. Importantly, the reaching control component incorporates an adaptive mechanism to drive the system to the sliding surface within a finite time while reducing chattering. The proposed observer and adaptive SMC are combined to enable accurate and robust reference tracking. The obtained simulation results verify the validity and effectiveness of the proposed approach.