Computationally Efficient Chance Constrained Covariance Control with Output Feedback
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866916171518836736 |
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| author | Pilipovsky, Joshua Tsiotras, Panagiotis |
| author_facet | Pilipovsky, Joshua Tsiotras, Panagiotis |
| contents | This paper studies the problem of developing computationally efficient solutions for steering the distribution of the state of a stochastic, linear dynamical system between two boundary Gaussian distributions in the presence of chance-constraints on the state and control input. It is assumed that the state is only partially available through a measurement model corrupted with noise. The filtered state is reconstructed with a Kalman filter, the chance constraints are reformulated as difference of convex (DC) constraints, and the resulting covariance control problem is reformulated as a DC program, which is solved using successive convexification. The efficiency of the proposed method is illustrated on a double integrator example with varying time horizons, and is compared to other state-of-the-art chance constrained covariance control methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_02485 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Computationally Efficient Chance Constrained Covariance Control with Output Feedback Pilipovsky, Joshua Tsiotras, Panagiotis Systems and Control Optimization and Control This paper studies the problem of developing computationally efficient solutions for steering the distribution of the state of a stochastic, linear dynamical system between two boundary Gaussian distributions in the presence of chance-constraints on the state and control input. It is assumed that the state is only partially available through a measurement model corrupted with noise. The filtered state is reconstructed with a Kalman filter, the chance constraints are reformulated as difference of convex (DC) constraints, and the resulting covariance control problem is reformulated as a DC program, which is solved using successive convexification. The efficiency of the proposed method is illustrated on a double integrator example with varying time horizons, and is compared to other state-of-the-art chance constrained covariance control methods. |
| title | Computationally Efficient Chance Constrained Covariance Control with Output Feedback |
| topic | Systems and Control Optimization and Control |
| url | https://arxiv.org/abs/2310.02485 |