Bounded iteration for multiple box constraints on linear complementarity model predictive control and its application to vehicle steering control
This paper presents linear model predictive control (MPC) for multiple kinds of constraint based on the linear complementarity problem (LCP) that gives the explicit upper bound of computational complexity. MPC generally solves constrained optimization problems. Its computational time should be stric...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2023-12-01
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| Series: | SICE Journal of Control, Measurement, and System Integration |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/18824889.2023.2225922 |
| _version_ | 1797366054274465792 |
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| author | Naoto Kawaguchi Isao Okawa Kenichiro Nonaka |
| author_facet | Naoto Kawaguchi Isao Okawa Kenichiro Nonaka |
| author_sort | Naoto Kawaguchi |
| collection | DOAJ |
| description | This paper presents linear model predictive control (MPC) for multiple kinds of constraint based on the linear complementarity problem (LCP) that gives the explicit upper bound of computational complexity. MPC generally solves constrained optimization problems. Its computational time should be strictly bounded for real-time applications. In a previous study, we proposed MPC based on the LCP for which a modified n-step vector successfully limits the number of iterations for the combinatorial problem. However, its class of applications is limited due to the existence of the modified n-step vector. In addition, MPC with a time-varying system is not included in this class since the modified n-step vector must be found for each problem at the corresponding time instance. This paper introduces a perturbation on constraints and applies a sequential LCP algorithm that gives a priori knowledge of the explicit upper bound of computational complexity and the accuracy of the solution. The iteration bounds are evaluated using the steering control of an autonomous driving vehicle for an obstacle avoidance manoeuvre. |
| first_indexed | 2024-03-08T16:58:43Z |
| format | Article |
| id | doaj.art-51910fd0383d444fb7ef838e144799a2 |
| institution | Directory Open Access Journal |
| issn | 1884-9970 |
| language | English |
| last_indexed | 2024-03-08T16:58:43Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | SICE Journal of Control, Measurement, and System Integration |
| spelling | doaj.art-51910fd0383d444fb7ef838e144799a22024-01-04T15:59:08ZengTaylor & Francis GroupSICE Journal of Control, Measurement, and System Integration1884-99702023-12-0116123724610.1080/18824889.2023.22259222225922Bounded iteration for multiple box constraints on linear complementarity model predictive control and its application to vehicle steering controlNaoto Kawaguchi0Isao Okawa1Kenichiro Nonaka2Graduate School of Integrative Science and Engineering, Tokyo City UniversityDENSO CORPORATION, Haneda Innovation City Zone-D 3FGraduate School of Integrative Science and Engineering, Tokyo City UniversityThis paper presents linear model predictive control (MPC) for multiple kinds of constraint based on the linear complementarity problem (LCP) that gives the explicit upper bound of computational complexity. MPC generally solves constrained optimization problems. Its computational time should be strictly bounded for real-time applications. In a previous study, we proposed MPC based on the LCP for which a modified n-step vector successfully limits the number of iterations for the combinatorial problem. However, its class of applications is limited due to the existence of the modified n-step vector. In addition, MPC with a time-varying system is not included in this class since the modified n-step vector must be found for each problem at the corresponding time instance. This paper introduces a perturbation on constraints and applies a sequential LCP algorithm that gives a priori knowledge of the explicit upper bound of computational complexity and the accuracy of the solution. The iteration bounds are evaluated using the steering control of an autonomous driving vehicle for an obstacle avoidance manoeuvre.http://dx.doi.org/10.1080/18824889.2023.2225922optimal controloptimization problemsquadratic programingreal-time systemsself-driving |
| spellingShingle | Naoto Kawaguchi Isao Okawa Kenichiro Nonaka Bounded iteration for multiple box constraints on linear complementarity model predictive control and its application to vehicle steering control SICE Journal of Control, Measurement, and System Integration optimal control optimization problems quadratic programing real-time systems self-driving |
| title | Bounded iteration for multiple box constraints on linear complementarity model predictive control and its application to vehicle steering control |
| title_full | Bounded iteration for multiple box constraints on linear complementarity model predictive control and its application to vehicle steering control |
| title_fullStr | Bounded iteration for multiple box constraints on linear complementarity model predictive control and its application to vehicle steering control |
| title_full_unstemmed | Bounded iteration for multiple box constraints on linear complementarity model predictive control and its application to vehicle steering control |
| title_short | Bounded iteration for multiple box constraints on linear complementarity model predictive control and its application to vehicle steering control |
| title_sort | bounded iteration for multiple box constraints on linear complementarity model predictive control and its application to vehicle steering control |
| topic | optimal control optimization problems quadratic programing real-time systems self-driving |
| url | http://dx.doi.org/10.1080/18824889.2023.2225922 |
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