Minimum variance lower bound estimation with Gaussian Process models

Naraghi, Mehdi Ghasemi; Alipouri, Yousef

doi:10.1177/0142331217690802

Cited by 3 publications

(1 citation statement)

References 24 publications

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“…(4) Combined modeling and control [15][16][17][18] . The Bayesian process can be used to build a non-parametric model offline or online to approximate the real physical model.…”

Section: Introductionmentioning

confidence: 99%

Gaussian Process Based Modeling and Control of Affine Systems with Control Saturation Constraints

Zhao

Wang

Zheng

et al. 2023

Complex Syst. Model. Simul.

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Model-based methods require an accurate dynamic model to design the controller. However, the hydraulic parameters of nonlinear systems, complex friction, or actuator dynamics make it challenging to obtain accurate models. In this case, using the input-output data of the system to learn a dynamic model is an alternative approach. Therefore, we propose a dynamic model based on the Gaussian process (GP) to construct systems with control constraints. Since GP provides a measure of model confidence, it can deal with uncertainty. Unfortunately, most GP-based literature considers model uncertainty but does not consider the effect of constraints on inputs in closed-loop systems. An auxiliary system is developed to deal with the influence of the saturation constraints of input. Meanwhile, we relax the nonsingular assumption of the control coefficients to construct the controller. Some numerical results verify the rationality of the proposed approach and compare it with similar methods.

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“…(4) Combined modeling and control [15][16][17][18] . The Bayesian process can be used to build a non-parametric model offline or online to approximate the real physical model.…”

Section: Introductionmentioning

confidence: 99%

Gaussian Process Based Modeling and Control of Affine Systems with Control Saturation Constraints

Zhao

Wang

Zheng

et al. 2023

Complex Syst. Model. Simul.

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Batch process modeling by using temporal feature and Gaussian mixture model

Guo

Pan

et al. 2019

Transactions of the Institute of Measurement and Control

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Multi-model/multi-phase modeling algorithm has been widely used to monitor the product quality in complicated batch processes. Most multi-model/ multi-phase modeling methods hinge on the structure of a linearly separable space or a combination of different sub-spaces. However, it is impossible to accurately separate the overlapping region samples into different operating sub-spaces using unsupervised learning techniques. A Gaussian mixture model (GMM) using temporal features is proposed in the work. First, the number of sub-model is estimated by using the maximum interval process trend analysis algorithm. Then, the GMM parameters constrained with the temporal value are identified by using the expectation maximization (EM) algorithm, which minimizes confusion in overlapping regions of different Gaussian processes. A numerical example and a penicillin fermentation process demonstrate the effectiveness of the proposed algorithm.

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