K-SMPC: Koopman Operator-Based Stochastic Model Predictive Control for Enhanced Lateral Control of Autonomous Vehicles
This paper proposes Koopman operator-based Stochastic Model Predictive Control (K-SMPC) for enhanced lateral control of autonomous vehicles. The Koopman operator is a linear map representing the nonlinear dynamics in an infinite-dimensional space. Thus, we use the Koopman operator to represent the n...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10844279/ |
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| author | Jin Sung Kim Ying Shuai Quan Chung Choo Chung Woo Young Choi |
| author_facet | Jin Sung Kim Ying Shuai Quan Chung Choo Chung Woo Young Choi |
| author_sort | Jin Sung Kim |
| collection | DOAJ |
| description | This paper proposes Koopman operator-based Stochastic Model Predictive Control (K-SMPC) for enhanced lateral control of autonomous vehicles. The Koopman operator is a linear map representing the nonlinear dynamics in an infinite-dimensional space. Thus, we use the Koopman operator to represent the nonlinear dynamics of a vehicle in dynamic lane-keeping situations. The Extended Dynamic Mode Decomposition (EDMD) method is adopted to approximate the Koopman operator in a finite-dimensional space for practical implementation. We consider the modeling error of the approximated Koopman operator in the EDMD method. Then, we design K-SMPC to tackle the Koopman modeling error, where the error is handled as a probabilistic signal. The recursive feasibility of the proposed method is investigated with an explicit first-step state constraint by computing the robust control invariant set. A high-fidelity vehicle simulator, i.e., CarSim, is used to validate the proposed method with a comparative study. From the results, it is confirmed that the proposed method outperforms other methods in tracking performance. Furthermore, it is observed that the proposed method satisfies the given constraints and is recursively feasible. |
| format | Article |
| id | doaj-art-fb3176d4e0f542cc8b7f7c722ac6c130 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-fb3176d4e0f542cc8b7f7c722ac6c1302025-01-25T00:01:16ZengIEEEIEEE Access2169-35362025-01-0113139441395810.1109/ACCESS.2025.353098410844279K-SMPC: Koopman Operator-Based Stochastic Model Predictive Control for Enhanced Lateral Control of Autonomous VehiclesJin Sung Kim0https://orcid.org/0000-0002-4375-2339Ying Shuai Quan1https://orcid.org/0000-0001-5035-4542Chung Choo Chung2https://orcid.org/0000-0002-3262-9300Woo Young Choi3https://orcid.org/0000-0002-6175-5533Department of Mechanical Engineering, University of California, Berkeley, CA, USADepartment of Electrical Engineering, Chalmers University of Technology, Gothenburg, SwedenDepartment of Electrical Engineering, Hanyang University, Seoul, South KoreaDepartment of Control and Instrumentation Engineering, Pukyong National University, Busan, South KoreaThis paper proposes Koopman operator-based Stochastic Model Predictive Control (K-SMPC) for enhanced lateral control of autonomous vehicles. The Koopman operator is a linear map representing the nonlinear dynamics in an infinite-dimensional space. Thus, we use the Koopman operator to represent the nonlinear dynamics of a vehicle in dynamic lane-keeping situations. The Extended Dynamic Mode Decomposition (EDMD) method is adopted to approximate the Koopman operator in a finite-dimensional space for practical implementation. We consider the modeling error of the approximated Koopman operator in the EDMD method. Then, we design K-SMPC to tackle the Koopman modeling error, where the error is handled as a probabilistic signal. The recursive feasibility of the proposed method is investigated with an explicit first-step state constraint by computing the robust control invariant set. A high-fidelity vehicle simulator, i.e., CarSim, is used to validate the proposed method with a comparative study. From the results, it is confirmed that the proposed method outperforms other methods in tracking performance. Furthermore, it is observed that the proposed method satisfies the given constraints and is recursively feasible.https://ieeexplore.ieee.org/document/10844279/Autonomous vehiclesdata-driven controlKoopman operatorpredictive controlstochastic model |
| spellingShingle | Jin Sung Kim Ying Shuai Quan Chung Choo Chung Woo Young Choi K-SMPC: Koopman Operator-Based Stochastic Model Predictive Control for Enhanced Lateral Control of Autonomous Vehicles IEEE Access Autonomous vehicles data-driven control Koopman operator predictive control stochastic model |
| title | K-SMPC: Koopman Operator-Based Stochastic Model Predictive Control for Enhanced Lateral Control of Autonomous Vehicles |
| title_full | K-SMPC: Koopman Operator-Based Stochastic Model Predictive Control for Enhanced Lateral Control of Autonomous Vehicles |
| title_fullStr | K-SMPC: Koopman Operator-Based Stochastic Model Predictive Control for Enhanced Lateral Control of Autonomous Vehicles |
| title_full_unstemmed | K-SMPC: Koopman Operator-Based Stochastic Model Predictive Control for Enhanced Lateral Control of Autonomous Vehicles |
| title_short | K-SMPC: Koopman Operator-Based Stochastic Model Predictive Control for Enhanced Lateral Control of Autonomous Vehicles |
| title_sort | k smpc koopman operator based stochastic model predictive control for enhanced lateral control of autonomous vehicles |
| topic | Autonomous vehicles data-driven control Koopman operator predictive control stochastic model |
| url | https://ieeexplore.ieee.org/document/10844279/ |
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