Nonlinear Generalized Predictive Control of the Crystal Diameter in CZ-Si Crystal Growth Process Based on Stacked Sparse Autoencoder

Liu, Ding; Zhang, Ni; Jiang, Lei; Zhao, Xiaoguo; Duan, Weifeng

doi:10.1109/tcst.2019.2898975

Cited by 24 publications

(3 citation statements)

References 18 publications

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“…In order to soften the control effect, the output of the controlled object is not directly tracking the setting value, but tracks the reference trajectory. 43,44 The reference trajectory is determined by setting value y ref , output value y, and diffusion coefficient a(0 \ a \ 1).…”

Section: Input Channel Time-delay Compensationmentioning

confidence: 99%

Predictive control compensation for networked control system with time-delay

Tian

Wang

2021

Proceedings of the Institution of Mechanical Engineers, Part I:

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This study proposes a novel predictive control compensation method to deal with random time-delay in networked control system. Different from other compensation strategies, this study adopts different compensation strategies for input time-delay and output time-delay to improve the control effect. First, for the input channel time-delay, the corresponding buffer is set by the time-stamp in the packet. Combining the historical output value and the control variable, the fast implicit generalized predictive control algorithm is adopted to design the predictive controller to compensate for the input channel time-delay. Second, for the output channel time-delay, the controller cannot measure it. A control compensator is designed by adding a feedback loop. The output control variable of the predictive controller is adjusted by predicting the error between the actual control signal and the controller output at the historical sampling time, so as to compensate for the time-delay of the output channel. In addition, the stability of the proposed time-delay compensation method is analyzed. Finally, simulation and experimental results show that the proposed method has good control and compensation effect, and improves the output performance of the system. The networked control system with random time-delay is stable. Furthermore, the simulation results also show that this time-delay compensation method needs less computing time and is more suitable for the practical applications.

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Section: Input Channel Time-delay Compensationmentioning

confidence: 99%

Predictive control compensation for networked control system with time-delay

Tian

Wang

2021

Proceedings of the Institution of Mechanical Engineers, Part I:

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show abstract

“…[8] developed a control strategy with two model predictive controllers to control r cry and melt temperature T mel . Liu et al [9] proposed a control structure where r cry is controlled by manipulating T h with constant v p . Zhang et al [10] and Lee et al [11] used MPC to determine a feedforward trajectory of temperature in the traditional control structure.…”

Section: Rahmanpour Et Almentioning

confidence: 99%

Gray-box model-based predictive control of Czochralski process

Kato

Kim

Mizuta

et al. 2021

Journal of Crystal Growth

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“…Different from PID control algorithm, the control variables at every moment in GPC are derived by rolling optimization based on not only the error between the past measurements and the setting values, but also the error of prediction model outputs to future trajectories. [7][8][9] The first attempt of applying GPC to power plants is made by Hogg' research team, 10 who constitutes GPC after Clarke et al 11 founded this outstanding optimization algorithm. In their works, a multi-loop GPC scheme is devised and applied to control the superheat pressure and steam temperatures in a power plant, and the control performance is substantially improved compared with conventional proportion-integral (PI) control.…”

Section: Introductionmentioning

confidence: 99%

Generalized predictive control of ultra-supercritical power plant by using a composite weighted human learning optimization network

Cheng

Peng

Zeng

2022

Proceedings of the Institution of Mechanical Engineers, Part I:

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The ultra-supercritical power plant is an advanced energy conversion system with high coal utilization and low emission. It is a complex system associated with high nonlinearity, strong coupling, and subjected to various uncertainties and interferences. This article introduces a nonlinear generalized predictive control based on a composite weighted human learning optimization network to incorporate both local linear modeling and local control into a multi-layer dynamic structure. Since composite weighted human learning optimization network can be considered as a linear-in-weight network, the limitation of the non-convex is elaborately broken through incorporating composite weighted human learning optimization models into the convex quadratic programming routine of local linear generalized predictive control. Detailed analysis is given to show the validity and advantage of the proposed method in controlling the ultra-supercritical power plant.

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Nonlinear Generalized Predictive Control of the Crystal Diameter in CZ-Si Crystal Growth Process Based on Stacked Sparse Autoencoder

Cited by 24 publications

References 18 publications

Predictive control compensation for networked control system with time-delay

Predictive control compensation for networked control system with time-delay

Gray-box model-based predictive control of Czochralski process

Generalized predictive control of ultra-supercritical power plant by using a composite weighted human learning optimization network

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