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Review copy

45,80 €
ISBN 978-3-8440-7403-1
Softcover
152 pages
29 figures
224 g
21 x 14,8 cm
English
Thesis

June 2020

Xiaohai Lin

Robust and Stochastic Model Predictive Control of Linear Systems with Predictable Additive Disturbance
with Application to Multi-Objective Adaptive Cruise Control

In this dissertation, novel robust model predictive control (MPC) and stochastic MPC concepts are presented which can efficiently handle a general class of additive disturbances perturbing discrete-time linear time-invariant systems. In particular, methods to use knowledge about the disturbance for improving the performance while ensuring stability and feasibility are proposed. The robust MPC scheme is applied to realize a robust adaptive cruise control (ACC) with which multiple objectives including driving comfort, fuel economy, and recursive satisfaction of constraints can be achieved. Herein the speed of the leading vehicle is considered as an additive disturbance for which a novel prediction procedure is developed. Furthermore, an efficient solver is introduced to ensure the real-time capability of the robust ACC.

Keywords: Model predictive control; robust control; stochastic control; additive disturbance; disturbance prediction; adaptive cruise control

Forschungsberichte aus dem Fachgebiet für Elektromobilität

Edited by apl. Prof. Dr.-Ing. Daniel Görges, Kaiserslautern

Volume 3

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Xiaohai Lin

Robust and Stochastic Model Predictive Control of Linear Systems with Predictable Additive Disturbance with Application to Multi-Objective Adaptive Cruise Control

Robust and Stochastic Model Predictive Control of Linear Systems with Predictable Additive Disturbance
with Application to Multi-Objective Adaptive Cruise Control