Spatio-Temporal Modeling of Nonlinear Distributed Parameter Systems

Li, Han-Xiong; Qi, Chenkun

doi:10.1007/978-94-007-0741-2

Cited by 94 publications

(55 citation statements)

References 46 publications

(67 reference statements)

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“…It is usually used to the separation of the spatial-temporal data in the modeling of the distribution parameter system [22], [23]. However, it is never applied to the separation of design variable/model parameter data in the system design.…”

Section: ) Svd Methods For Separation Of Variables and Model Reductionmentioning

confidence: 99%

Data-Driven Robust Design for a Curing Oven

Huang

2014

IEEE Trans. Compon., Packag. Manufact. Technol.

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If no external control is employed, the cure performance will be fully dependent on the oven design. Design of such a system is challenging because of its inherent complexity: complex heat transfer, unknown relationship between design variable and curing performance, unknown boundary conditions and large parameter variation, and so on. In this paper, a novel data-based robust design approach is proposed to design a new curing oven with simple structure. First, a modeling method with hybrid singular value decomposition and response surface method is developed to extract the system model from experimental data. The coupling influence between design variable and model parameter is also separated to make the design simpler, because only the part affected by design variable will be considered in the design. A robust design method is then developed to minimize the influence of parameter variation on the performance. Finally, the effectiveness of the proposed method is demonstrated in the design of the new curing oven.Index Terms-Design for manufacture, design methodology, semiconductor device manufacture, semiconductor device modeling.

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Section: ) Svd Methods For Separation Of Variables and Model Reductionmentioning

confidence: 99%

Data-Driven Robust Design for a Curing Oven

Huang

2014

IEEE Trans. Compon., Packag. Manufact. Technol.

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“…THE PROCESS MODELING The technological heating process of the asphaltic emulsion from the tank is a distributed parameter process [2] because the temperature of the solution mathematically depends both on the time independent variable (t) and on the position in the tank. The position in the tank can be determined considering the three axes of the Cartesian space 0p, 0q and 0r ( Fig.…”

Section: IImentioning

confidence: 99%

Temperature modeling and simulation of the asphaltic emulsion in an industrial tank

Mureşan

Abrudean

Coloşi

2014

2014 IEEE 9th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI)

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In this paper, a solution for the modelingsimulation of the temperature propagation phenomenon in the asphaltic emulsion from an industrial tank is presented. The heating process of the asphaltic emulsion is a distributed parameter one, its model being expressed using a partial differential equation. The modeling-simulation procedure is based on the matrix of partial derivatives of the state vector (Mpdx) associated with Taylor series. Some suggestive 2D and 3D graphical representations are presented in order to show the temperature distribution inside the tank. Also, a solution for the designing of the emulsion's temperature control system is proposed in the end of the paper.

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“…The weighting matrix R in (4) is given as R = 1. The parameter γ s in (16) is given as γ s = 5, then γ > 5 for the L 2 -gain performance (4).…”

Section: Simulation Studiesmentioning

confidence: 99%

Data-Driven <inline-formula> <tex-math notation="LaTeX">$H_\infty$ </tex-math></inline-formula> Control for Nonlinear Distributed Parameter Systems

Luo

Huang

et al. 2015

IEEE Trans. Neural Netw. Learning Syst.

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The data-driven H∞ control problem of nonlinear distributed parameter systems is considered in this paper. An off-policy learning method is developed to learn the H∞ control policy from real system data rather than the mathematical model. First, Karhunen-Loève decomposition is used to compute the empirical eigenfunctions, which are then employed to derive a reduced-order model (ROM) of slow subsystem based on the singular perturbation theory. The H∞ control problem is reformulated based on the ROM, which can be transformed to solve the Hamilton-Jacobi-Isaacs (HJI) equation, theoretically. To learn the solution of the HJI equation from real system data, a data-driven off-policy learning approach is proposed based on the simultaneous policy update algorithm and its convergence is proved. For implementation purpose, a neural network (NN)- based action-critic structure is developed, where a critic NN and two action NNs are employed to approximate the value function, control, and disturbance policies, respectively. Subsequently, a least-square NN weight-tuning rule is derived with the method of weighted residuals. Finally, the developed data-driven off-policy learning approach is applied to a nonlinear diffusion-reaction process, and the obtained results demonstrate its effectiveness.

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Spatio-Temporal Modeling of Nonlinear Distributed Parameter Systems

Cited by 94 publications

References 46 publications

Data-Driven Robust Design for a Curing Oven

Data-Driven Robust Design for a Curing Oven

Temperature modeling and simulation of the asphaltic emulsion in an industrial tank

Data-Driven <inline-formula> <tex-math notation="LaTeX">$H_\infty$ </tex-math></inline-formula> Control for Nonlinear Distributed Parameter Systems

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