Real-time control of a hybrid laboratory plant using a receding horizon control strategy

Gambier, Adrián

doi:10.1109/cca.2004.1387288

Cited by 5 publications

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Consequently, a reduced constant complexity per iteration [16] is required so that the online method is executed at a higher speed than the transmission rate at which the data samples are received. These requisites are well-motivated since they appear in many practical problems, such as online trajectory planning [17], [18], real-time control systems [19], [20], and high-speed digital to analog conversion [21], among others. Although the tasks of estimating smooth signals or delivering a zero-delay response are separately managed by most online methods, handling them together becomes challenging, as we expound next.…”

Section: Data Sample Reconstruction Futurementioning

confidence: 99%

See 1 more Smart Citation

A Trainable Approach to Zero-Delay Smoothing Spline Interpolation

Ruiz-Moreno,

López-Ramos,

Beferull-Lozano

2023

IEEE Trans. Signal Process.

View full text Add to dashboard Cite

The task of reconstructing smooth signals from streamed data in the form of signal samples arises in various applications. This work addresses such a task subject to a zerodelay response; that is, the smooth signal must be reconstructed sequentially as soon as a data sample is available and without having access to subsequent data. State-of-the-art approaches solve this problem by interpolating consecutive data samples using splines. Here, each interpolation step yields a piece that ensures a smooth signal reconstruction while minimizing a cost metric, typically a weighted sum between the squared residual and a derivative-based measure of smoothness. As a result, a zero-delay interpolation is achieved in exchange for an almost certainly higher cumulative cost as compared to interpolating all data samples together. This paper presents a novel approach to further reduce this cumulative cost on average. First, we formulate a zero-delay smoothing spline interpolation problem from a sequential decision-making perspective, allowing us to model the future impact of each interpolated piece on the average cumulative cost. Then, an interpolation method is proposed to exploit the temporal dependencies between the streamed data samples. Our method is assisted by a recurrent neural network and accordingly trained to reduce the accumulated cost on average over a set of example data samples collected from the same signal source generating the signal to be reconstructed. Finally, we present extensive experimental results for synthetic and real data showing how our approach outperforms the abovementioned state-of-the-art.

show abstract

Section: Data Sample Reconstruction Futurementioning

confidence: 99%

“…for every k ∈ N [0,φ] and t ∈ N [1,T ] with u t ≜ x t − x t−1 , and where the step (25b) uses the relations described in (20) and (22). On the other hand, e 0 encodes the initial boundary conditions of the reconstruction and can be set by the user in advance or calculated.…”

Section: A Proof Of Propositionmentioning

confidence: 99%