Design of an LMI-based Polytopic LQR Cruise Controller for an Autonomous Vehicle towards Riding Comfort

Abstract

In this paper, we present an LMI-based approach for comfort-oriented cruise control of an autonomous vehicle. First, vehicle longitudinal dynamics and a corresponding parameter-dependent state-space representation are explained and discussed. An LMI-based polytopic LQR controller is then designed for the vehicle speed to track the reference value in the presence of noise and disturbances, where the scheduling parameters are functions of the vehicle mass and the speed itself. An appropriate disturbance force compensation term is also included in the designed controller to provide a smoother response. Then we detail how the reference speed is calculated online, using polynomial functions of the given desired comfort level (quantified by the vertical acceleration absorbed by the human body) and of the road type characterized by road roughness. Finally, time-domain simulations illustrate the method’s effectiveness.

Learning-Based Control of Autonomous Vehicles Using an Adaptive Neuro-Fuzzy Inference System and the Linear Matrix Inequality Approach

“…LPV-MPC [28,29] uses LPV to obtain the vehicle model, and despite its merits, the control still needs online optimization for MPC during iteration, but with less computational load than NL-MPC. Another versatile and effective tool in automatic control is the Linear Matrix Inequalities (LMIs) approach [30], which allows for systematic design of closed-loop systems with guarantees of stability and performance without requiring online optimization [31][32][33].…”