Neural Network and Extended State Observer-Based Model Predictive Control for Smooth Braking at Preset Points in Autonomous Vehicles

Abstract

In this paper, we explore the problem of smooth braking at preset points in autonomous vehicles using model predictive control (MPC) with a receding horizon extended state observer (RHESO) and a neural network (NN). An NN-based modeling method is proposed to intuitively describe the relationship between vehicle speed and the vehicle controllers (brake and throttle), and establish a dynamic model of autonomous vehicles. A sufficient condition is put forward to guarantee the convergence of the proposed NN. Furthermore, a composite MPC strategy based on RHESO is designed, which optimizes a given cost function over the receding horizon while mitigating the effects of modeling inaccuracies and disturbances. Additionally, easily verifiable conditions are provided to ensure the autonomous driving vehicles’ uniform boundedness. Numerically illustrative examples are given to demonstrate the effectiveness of the proposed approach.

Keywords

• model predictive control
• extended state observer
• neural network
• autonomous vehicle
• preset point smooth braking