Recursive Least Squares with Variable-Rate Forgetting Based on the F-Test

Mohseni, Nima; Bernstein, Dennis S.

doi:10.23919/acc53348.2022.9867849

Cited by 9 publications

(3 citation statements)

References 8 publications

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“…The PCAC algorithm is presented in this section. Subsection III-A describes the technique used for online identification, namely, RLS with variable-rate forgetting based on the F-test [25]. Subsection III-B presents the block observable canonical form (BOCF), which is used to represent the inputoutput dynamics model as a state space model whose state is given explicitly in terms of inputs, outputs, and modelcoefficient estimates.…”

Section: Predictive Cost Adaptive Controlmentioning

confidence: 99%

“…and Ψ 0 is the performance-regularization weighting in (16). Additional details concerning RLS with forgetting based on the F-distribution are given in [25].…”

Section: A Online Identification Using Recursive Least Squares Withmentioning

confidence: 99%

“…PCAC performs online closedloop linear system identification identification; the identified model is then used as the basis for receding-horizon optimization. The system identification is performed during closed-loop operation by recursive least squares (RLS) [24], [25]. For receding-horizon optimization, quadratic programming (QP) is used in [26].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Experimental Implementation of Retrospective Cost Adaptive Control for Suppressing Thermoacoustic Oscillations in a Rijke Tube

Paredes

Bernstein

2023

IEEE Trans. Contr. Syst. Technol.

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Model predictive control (MPC) has been used successfully in diverse applications. As its name suggests, MPC requires a model for predictive optimization. The present paper focuses on the application of MPC to a Rijke tube, in which a heating source and acoustic dynamics interact to produce self-excited oscillations. Since the dynamics of a Rijke tube are difficult to model to a high level of accuracy, the implementation of MPC requires leveraging data from the physical setup as well as knowledge about thermoacoustics, which is labor intensive and requires domain expertise. With this motivation, the present paper uses predictive cost adaptive control (PCAC) for sampled-data control of an experimental Rijke-tube setup. PCAC performs online closed-loop linear model identification for receding-horizon optimization based on the backward propagating Riccati equation. In place of analytical modeling, open-loop experiments are used to create a simple emulation model, which is used for choosing PCAC hyperparameters. PCAC is applied to the Rijke-tube setup under various experimental scenarios.

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Section: Predictive Cost Adaptive Controlmentioning

confidence: 99%

“…and Ψ 0 is the performance-regularization weighting in (16). Additional details concerning RLS with forgetting based on the F-distribution are given in [25].…”

Section: A Online Identification Using Recursive Least Squares Withmentioning

confidence: 99%