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arXiv · 2311.06787

Data-Driven Moving Horizon Estimation Using Bayesian Optimization

Qing Sun·Shuai Niu·Minrui Fei
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Abstract

In this work, an innovative data-driven moving horizon state estimation is proposed for model dynamic-unknown systems based on Bayesian optimization. As long as the measurement data is received, a locally linear dynamics model can be obtained from one Bayesian optimization-based offline learning framework. Herein, the learned model is continuously updated iteratively based on the actual observed data to approximate the actual system dynamic with the intent of minimizing the cost function of the moving horizon estimator until the desired performance is achieved. Meanwhile, the characteristics of Bayesian optimization can guarantee the closest approximation of the learned model to the actual system dynamic. Thus, one effective data-driven moving horizon estimator can be designed further on the basis of this learned model. Finally, the efficiency of the proposed state estimation algorithm is demonstrated by several numerical simulations.

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Qing Sun, Shuai Niu, Minrui Fei. 2023-11-12. Data-Driven Moving Horizon Estimation Using Bayesian Optimization. https://arxiv.org/abs/2311.06787

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