On the implementation of Adaptive and Filtered MHE

TL;DR

This paper demonstrates how output filtering and adaptive sampling techniques can significantly reduce the computational cost of Moving Horizon Estimators (MHE) while maintaining estimation accuracy, through implementation and numerical validation.

Contribution

It introduces practical implementation strategies for adaptive and filtered MHE, focusing on reducing computational load via output filtering and adaptive sampling policies.

Findings

Filtering reduces data volume and computation time.

Adaptive sampling discards uninformative data.

Strategies maintain estimation accuracy with lower computational cost.

Abstract

Optimisation-based algorithms known as Moving Horizon Estimator (MHE) have been developed through the years. This paper illustrates the implementation of the policy introduced in the companion paper submitted to the 18th IFAC Workshop on Control Applications of Optimization [Oliva and Carnevale, 2022], in which we propose two techniques to reduce the computational cost of MHEs. These solutions mainly rely on output filtering and adaptive sampling. The use of filters reduces the total amount of data used by MHE, shortening the length of the moving window (buffer) and consequently decreasing the time consumption for plant dynamics integration. Meanwhile, the proposed adaptive sampling policy discards those sampled data that do not allow a sensible improvement of the estimation error. Algorithms and numerical simulations are provided to show the effectiveness of the proposed strategies.