Fényes, Dániel and Hegedűs, Tamás and Gáspár, Péter (2025) Neural network-based controller combination for automated vehicles. In: 6th International Conference on Control and Fault-Tolerant Systems, 2025.10.06-08., Ayia Napa, Ciprus. (In Press)
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Abstract
This paper introduces a reinforcement learning (RL)-based framework for adaptively combining four different control strategies in response to varying operational conditions. Starting from a nominal model of the actual system, several feedback controllers are developed, each offering distinct performance benefits under different circumstances. The RL algorithm dynamically determines and mixes the outputs of the controller within specific operating ranges. Four control approaches are considered: Linear Parameter Varying (LPV), Ultra-local Model-based (ULM), Linear Quadratic Regulator (LQR), and a kinematic model-based method. The proposed solution is validated through different test scenarios using the high-fidelity vehicle simulation platform, CarMaker.
| Item Type: | Conference or Workshop Item (Paper) |
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| Subjects: | T Technology / alkalmazott, műszaki tudományok > TK Electrical engineering. Electronics Nuclear engineering / elektrotechnika, elektronika, atomtechnika T Technology / alkalmazott, műszaki tudományok > TL Motor vehicles. Aeronautics. Astronautics / járműtechnika, repülés, űrhajózás |
| Depositing User: | Dr Dániel Fényes |
| Date Deposited: | 23 Sep 2025 12:10 |
| Last Modified: | 23 Sep 2025 12:10 |
| URI: | https://real.mtak.hu/id/eprint/224952 |
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