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• W3197398651 abstract "For control it is essential to obtain an accurate estimate of the current system state, based on uncertain sensor measurements and existing system knowledge. An optimization-based moving horizon estimation (MHE) approach uses a dynamical model of the system, and further allows to integrate physical constraints on system states and uncertainties, to obtain a trajectory of state estimates. In this work, we address the problem of state estimation in case of constrained linear systems with parametric uncertainty. The proposed approach makes use of differentiable convex optimization layers to formulate an MHE state estimator, for systems with uncertain parameters. This formulation allows us to obtain the gradient of a squared output error, based on sensor measurements and state estimates, with respect to the uncertain system parameters, and update the believe of the parameters online using stochastic gradient descent (SGD). In a numerical example of estimating temperatures of a group of manufacturing machines, we show the performance of learning the unknown system parameters and the benefits of integrating physical state constraints in the MHE formulation." @default.
• W3197398651 created "2021-09-13" @default.
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• W3197398651 date "2021-09-08" @default.
• W3197398651 modified "2023-09-27" @default.
• W3197398651 title "Learning-based Moving Horizon Estimation through Differentiable Convex Optimization Layers." @default.
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• W3197398651 hasPublicationYear "2021" @default.
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