Lelkó, Attila and Németh, Balázs and Gáspár, Péter (2025) Robust H∞ Control Synthesis with Learning in Additive Form for Autonomous Vehicles. IFAC PAPERSONLINE, 59 (16). pp. 1-6. ISSN 2405-8971 10.1016/j.ifacol.2025.10.070

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Abstract

This paper presents a method for improving the performance level of the robust H<inf>8</inf> controllers. The improvement is achieved in an additive form, which contains a learning-based agent. The contribution of the presented method is that the design methods of the H<inf>8</inf> controller and of the learning-based agent are formed in a joint optimization. This results in the iterative design of the controllers within a reinforcement learning algorithm. The developed design method is applied to an autonomous vehicle control problem for lap time minimization. The presented simulation-based analysis shows that the proposed method can provide improved performance level, compared to the conventional H<inf>8</inf> control without extension or to H<inf>8</inf> control with extension but without joint optimization. © © 2025 The Authors.

Item Type:	Article
Uncontrolled Keywords:	Optimization; reinforcement learning; reinforcement learning; CONTROLLERS; iterative methods; Control system synthesis; Intelligent agents; Two term control systems; Robust control; Learning algorithms; Autonomous agents; Design method; Autonomous Vehicles; Autonomous Vehicles; Autonomous Vehicles; Robust H; Joint optimization; control synthesis; Reinforcement learnings; Performance:level; ITERATIVE DESIGN; Learning-based agent; Robust H8 control; Learning-based agent; Robust H8 control;
Subjects:	Q Science > QA Mathematics and Computer Science > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány
Divisions:	Systems and Control Lab
SWORD Depositor:	MTMT Injector
Depositing User:	MTMT Injector
Date Deposited:	28 Jan 2026 20:59
Last Modified:	28 Jan 2026 21:00
URI:	https://eprints.sztaki.hu/id/eprint/11104

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