Analysis and performance of the LP-MPC and QP-MPC cascade control system

Ying, Chao-Ming; Voorakaranam, Srikanth; Joseph, Babu

doi:10.1109/acc.1998.703519

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…These variables together with the output dc-link voltage reference are the inputs of the outer MPC. As usual, in a cascade controller, the output of the outer controller is the reference of the inner controller [32], [33]. Taking this in mind, the outer loop regulates the output dc-link voltage generating the reference currents used by the inner loop.…”

Section: Proposed Control Systemmentioning

confidence: 99%

“…With this proposal, the dynamics performance of the output voltage in case of load changes and voltage sags are improved, making the system low-sensitive to these disturbances. Finally, compared to a single-loop controller, the cascade control system has several advantages, such as disturbance rejection and robustness against model parameter uncertainties [32], [33].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Dual-Loop Continuous Control Set Model Predictive Control for a Three-phase Unity Power Factor Rectifier

Alfaro¹,

Guzmán

Vicuña

et al. 2021

IEEE Trans. Power Electron.

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In this paper, a novel cascade model predictive control for a three-phase unity power factor rectifier is presented. Unlike the traditional control techniques for rectifiers, the proposed method uses a cascade dual-model predictive control for the inner and outer control loops. The three-phase currents of the rectifier are controlled with the inner control loop, while with the outer control loop the desired output voltage is achieved. In this proposal, the output power is used as a feedforward to enhance the transient dynamics of the output dc-voltage. Several advantages can be highlighted such as a fixed switching frequency and output-voltage variations are avoided when a sudden change in the load or a voltage sag appears. The overall control proposal has been fully integrated into a digital signal processor. Selected experimental results are introduced to validate the theoretical contributions of this paper.

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Section: Proposed Control Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dual-Loop Continuous Control Set Model Predictive Control for a Three-phase Unity Power Factor Rectifier

Alfaro¹,

Guzmán

Vicuña

et al. 2021

IEEE Trans. Power Electron.

View full text Add to dashboard Cite

show abstract

Tracking necessary condition of optimality by a data-driven solution combining steady-state and transient data

Demuner

Delou

Secchi

2022

Journal of Process Control

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Comparison of Statistical Metrics and a New Fuzzy Method for Validating Linear Models Used in Model Predictive Control Controllers

Alvarado¹,

Garcia

2018

Ind. Eng. Chem. Res.

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In the advanced process control area, model predictive control (MPC) implementations have been successful in many industrial applications. Despite being an optimization-based control technique, sometimes problems occur with the control algorithm when the dynamic model is not adequate. This work compares statistical techniques for model validation to quantify the quality of identified models used in multivariable MPC controllers. Additionally, a fuzzy validation system is proposed, showing the consistency between the model validation and the predictive controller performance. Multivariable identification, model validation, and predictive controller implementation are performed in an industrial-scale pH neutralization pilot plant.

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Analysis and performance of the LP-MPC and QP-MPC cascade control system

Cited by 4 publications

References 11 publications

Dual-Loop Continuous Control Set Model Predictive Control for a Three-phase Unity Power Factor Rectifier

Dual-Loop Continuous Control Set Model Predictive Control for a Three-phase Unity Power Factor Rectifier

Tracking necessary condition of optimality by a data-driven solution combining steady-state and transient data

Comparison of Statistical Metrics and a New Fuzzy Method for Validating Linear Models Used in Model Predictive Control Controllers

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