An optimistic planning algorithm for switched discrete-time LQR
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866911122900123648 |
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| author | Granzotto, Mathieu Postoyan, Romain Nešić, Dragan Daafouz, Jamal Buşoniu, Lucian |
| author_facet | Granzotto, Mathieu Postoyan, Romain Nešić, Dragan Daafouz, Jamal Buşoniu, Lucian |
| contents | We introduce TROOP, a tree-based Riccati optimistic online planner, that is designed to generate near-optimal control laws for discrete-time switched linear systems with switched quadratic costs. The key challenge that we address is balancing computational resources against control performance, which is important as constructing near-optimal inputs often requires substantial amount of computations. TROOP addresses this trade-off by adopting an online best-first search strategy inspired by A*, allowing for efficient estimates of the optimal value function. The control laws obtained guarantee both near-optimality and stability properties for the closed-loop system. These properties depend on the planning depth, which determines how far into the future the algorithm explores and is closely related to the amount of computations. TROOP thus strikes a balance between computational efficiency and control performance, which is illustrated by numerical simulations on an example. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_19054 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An optimistic planning algorithm for switched discrete-time LQR Granzotto, Mathieu Postoyan, Romain Nešić, Dragan Daafouz, Jamal Buşoniu, Lucian Optimization and Control Systems and Control We introduce TROOP, a tree-based Riccati optimistic online planner, that is designed to generate near-optimal control laws for discrete-time switched linear systems with switched quadratic costs. The key challenge that we address is balancing computational resources against control performance, which is important as constructing near-optimal inputs often requires substantial amount of computations. TROOP addresses this trade-off by adopting an online best-first search strategy inspired by A*, allowing for efficient estimates of the optimal value function. The control laws obtained guarantee both near-optimality and stability properties for the closed-loop system. These properties depend on the planning depth, which determines how far into the future the algorithm explores and is closely related to the amount of computations. TROOP thus strikes a balance between computational efficiency and control performance, which is illustrated by numerical simulations on an example. |
| title | An optimistic planning algorithm for switched discrete-time LQR |
| topic | Optimization and Control Systems and Control |
| url | https://arxiv.org/abs/2508.19054 |