A Composite Model Predictive and Super Twisting Sliding Mode Controller for Stable and Robust Trajectory Tracking of Autonomous Ground Vehicles

Abstract

In this work, we propose a novel composite control system for stable and robust trajectory tracking of autonomous ground vehicles (AGVs) in the presence of bounded disturbances and uncertainties. A nominal model predictive control (MPC) system is combined with a second-order super twisting sliding mode controller (STSMC) to formulate the proposed control system under the umbrella of tube-based MPC, with the aim of tackling the trajectory tracking challenge for AGVs in uncertain environments. The proposed system's stability is analyzed and guaranteed via Input-to-State Stability (ISS) in coordination with Lyapunov stability theory. For the first time, this combined control structure is applied to the nonlinear kinematic model of AGVs, where STSMC plays the role of an auxiliary controller in the feedback loop to handle disturbances and uncertainties that cause deviation from the nominal model. A comparative simulation study is presented to demonstrate the effectiveness and robustness of the proposed composite scheme in the presence of disturbance effects.