Applications of Identification in Process Control

Zhu, Yucai

doi:10.1016/b978-008043985-3/50012-0

Cited by 58 publications

(57 citation statements)

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“…An identification experiment is performed by a GBN signal with an average switch time of 5 samples. The test amplitude of the input signal is adjusted so that the noise-to-signal ratios are 8% in power.…”

Section: Case Studiesmentioning

confidence: 99%

Time-Partitioned Piecewise Affine Output Error Model for Batch Processes

Huang

Zhao

et al. 2018

Ind. Eng. Chem. Res.

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In this paper, a time-partitioned piecewise affine output error (PWA-OE) model for batch processes is proposed, in which time index is used to simplify the partition of regression domain by utilizing the repetitive nature of batch processes. The identification problem involves both continuous and discrete variables, and the derivative information on discrete variables is unavailable. Thus, an identification algorithm based on separable nonlinear least-squares is developed to reduce the complexity of nonlinear minimization. Furthermore, the consistent information criterion (CIC) is used to determine model structure. Results show that the time-partitioned PWA-OE model can effectively describe batch processes.

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Section: Case Studiesmentioning

confidence: 99%

Time-Partitioned Piecewise Affine Output Error Model for Batch Processes

Huang

Zhao

et al. 2018

Ind. Eng. Chem. Res.

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“…If the rational assumption of good mass balance and dynamic balance are met, the camera motion model can be easily divided into two parts like in the Wiener form of non‐linear models (Zhu, 2001). Good balance makes decomposition of the frame 1 from the frame 2 movements, and the aircraft acceleration cannot influence camera‐to‐body rotation.…”

Section: Camera Platform Modellingmentioning

confidence: 99%

Software‐based camera stabilisation on unmanned aircraft

̨żek

2003

Aircraft Engineering and Aerospace Technology

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The concept of software‐based camera control and stabilisation and test results for two degree‐of‐freedom onboard cameras are presented in this paper. Contrary to the conventional gimballed observation cameras, the presented solution makes lightweight gimbal's construction possible. The unmanned aircraft state measurements and information of the target position are used in the computation of the desired angles of the camera rotation axes. The method has been tested using simulation technique. The results obtained in this paper show the need for fast enough camera actuation to stabilise image when aircraft attitude is disturbed by turbulence and attitude changes as a manoeuvre effect. The quantitative relationship between the parameters of camera actuators and achievable pointing accuracy are shown for exemplary flight condition simulation test.

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“…An accurate and reliable process model is crucial to the successful application of most advanced control strategies developed for the batch process (e.g., iterative learning control and model predictive control). System identification , has become a useful tool for modeling the dynamical systems from data measurements. Numerous papers have dealt with the identification of the batch process.…”

Section: Introductionmentioning

confidence: 99%

Nonparametric Identification Based on Multi-inherited Gaussian Process Regression for Batch Process

Chen

Zhao

et al. 2020

Ind. Eng. Chem. Res.

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This study presents a multi-inherited Gaussian process regression (GPR)-based nonparametric identification method for batch process. In the GPR framework, the impulse response of each time point consists of two parts: one is the inheritance part for utilizing the model information of previous time points, in which a Gaussian prior is imposed over the unknown inheritance weight, and the other is the residual impulse response, which is interpreted as a zero-mean Gaussian process. Following the empirical Bayes approach, we derive the joint estimation of the inheritance weight and the residual impulse response. The hyperparameters are determined by maximizing the marginal likelihood. As the correlation of process dynamics at adjacent time points is considered by model inheritance, the proposed method can effectively improve the estimation accuracy. Finally, we demonstrate the superiority of the proposed identification method in two case studies.

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Applications of Identification in Process Control

Cited by 58 publications

References 0 publications

Time-Partitioned Piecewise Affine Output Error Model for Batch Processes

Time-Partitioned Piecewise Affine Output Error Model for Batch Processes

Software‐based camera stabilisation on unmanned aircraft

Nonparametric Identification Based on Multi-inherited Gaussian Process Regression for Batch Process

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