Moving-Horizon Estimators for Hyperbolic and Parabolic PDEs in 1-D
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| Format: | Preprint |
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2024
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| _version_ | 1866917850585759744 |
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| author | Bhan, Luke Shi, Yuanyuan Karafyllis, Iasson Krstic, Miroslav Rawlings, James B. |
| author_facet | Bhan, Luke Shi, Yuanyuan Karafyllis, Iasson Krstic, Miroslav Rawlings, James B. |
| contents | Observers for PDEs are themselves PDEs. Therefore, producing real time estimates with such observers is computationally burdensome. For both finite-dimensional and ODE systems, moving-horizon estimators (MHE) are operators whose output is the state estimate, while their inputs are the initial state estimate at the beginning of the horizon as well as the measured output and input signals over the moving time horizon. In this paper we introduce MHEs for PDEs which remove the need for a numerical solution of an observer PDE in real time. We accomplish this using the PDE backstepping method which, for certain classes of both hyperbolic and parabolic PDEs, produces moving-horizon state estimates explicitly. Precisely, to explicitly produce the state estimates, we employ a backstepping transformation of a hard-to-solve observer PDE into a target observer PDE, which is explicitly solvable. The MHEs we propose are not new observer designs but simply the explicit MHE realizations, over a moving horizon of arbitrary length, of the existing backstepping observers. Our PDE MHEs lack the optimality of the MHEs that arose as duals of MPC, but they are given explicitly, even for PDEs. In the paper we provide explicit formulae for MHEs for both hyperbolic and parabolic PDEs, as well as simulation results that illustrate theoretically guaranteed convergence of the MHEs. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2401_02516 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Moving-Horizon Estimators for Hyperbolic and Parabolic PDEs in 1-D Bhan, Luke Shi, Yuanyuan Karafyllis, Iasson Krstic, Miroslav Rawlings, James B. Systems and Control Artificial Intelligence Analysis of PDEs Dynamical Systems Optimization and Control Observers for PDEs are themselves PDEs. Therefore, producing real time estimates with such observers is computationally burdensome. For both finite-dimensional and ODE systems, moving-horizon estimators (MHE) are operators whose output is the state estimate, while their inputs are the initial state estimate at the beginning of the horizon as well as the measured output and input signals over the moving time horizon. In this paper we introduce MHEs for PDEs which remove the need for a numerical solution of an observer PDE in real time. We accomplish this using the PDE backstepping method which, for certain classes of both hyperbolic and parabolic PDEs, produces moving-horizon state estimates explicitly. Precisely, to explicitly produce the state estimates, we employ a backstepping transformation of a hard-to-solve observer PDE into a target observer PDE, which is explicitly solvable. The MHEs we propose are not new observer designs but simply the explicit MHE realizations, over a moving horizon of arbitrary length, of the existing backstepping observers. Our PDE MHEs lack the optimality of the MHEs that arose as duals of MPC, but they are given explicitly, even for PDEs. In the paper we provide explicit formulae for MHEs for both hyperbolic and parabolic PDEs, as well as simulation results that illustrate theoretically guaranteed convergence of the MHEs. |
| title | Moving-Horizon Estimators for Hyperbolic and Parabolic PDEs in 1-D |
| topic | Systems and Control Artificial Intelligence Analysis of PDEs Dynamical Systems Optimization and Control |
| url | https://arxiv.org/abs/2401.02516 |