Launch ascent guidance by discrete multi-model predictive control

Vachon, Alexandre; Desbiens, André; Gagnon, Éric; Bérard, Caroline

doi:10.1016/j.actaastro.2013.10.022

Cited by 18 publications

(5 citation statements)

References 18 publications

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“…Various approaches to PL‐MPC can be found in many disciplines such as wind energy, 10 aeronautics, 11,12 steam generation in nuclear power plants, 13,14 and medicine 15 . One approach from the medical industry changes the parameters in a fixed‐structure model based on patient‐specific medical information 15 .…”

Section: Introductionmentioning

confidence: 99%

Piecewise linear model predictive control of a rapid pressure swing adsorption system

2020

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Rapid pressure swing adsorption (RPSA) is a gas separation technology used in the small-scale oxygen concentrator devices. These devices are commonly used to produce high purity (~90%) oxygen from air for oxygen rehabilitation therapy, but can also produce a much wider range of oxygen purities for other applications. RPSA is a complex, cyclic, nonlinear switched logic process resulting from the coupling of gas adsorption, heat transfer, flow reversal effects, and process logic switches. For RPSA devices to operate satisfactorily, feedback control is critical but challenging due to their inherent complexity. In this article, we present a piecewise linear model predictive control framework for operation and control of a single-bed RPSA system. A set of coupled, nonlinear partial differential equations with flow switching conditions is used as a plant model for the RPSA process. Subspace system identification with pseudo-random binary sequence signals applied to this plant model at multiple operating points is used to generate a family of piecewise linear models for use in the model predictive controller algorithm. Detailed descriptions of the RPSA plant model, the multiple linear model identification procedure, the controller formulation and model switching logic are presented. The closed-loop system is evaluated in simulation using several realistic set point tracking and disturbance rejection cases.

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Section: Introductionmentioning

confidence: 99%

Piecewise linear model predictive control of a rapid pressure swing adsorption system

2020

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“…(2) A bank of linear models are used to approximate the considered nonlinear system, and then a global MPC is designed based on the linear models using the min-max strategy. In the second type of MMPC approach, the objective function is minimized for the worst process model, resulting in a robust MPC. − A possible challenge is that the robust MPC may be too conservative, leading to poor performance. (3) A set of local linear MPC controllers are designed based on local linear models, and then the local MPCs are combined into a global MPC by weighting functions or switching. − We call the third type “Multilinear model predictive control (MLMPC)”.…”

Section: Introductionmentioning

confidence: 99%

Integrated Multilinear Model Predictive Control of Nonlinear Systems Based on Gap Metric

Johansen

2015

Ind. Eng. Chem. Res.

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Two integrated multilinear model predictive control (MLMPC) algorithms are proposed for nonlinear chemical processes. The gap metric and the gap metric stability margin are employed to select local linear models and design local MPC controllers. Thus, the local stability and desired closed-loop performance can be incorporated into the model bank selection process. After that, a gap-metric-based weighting method is used to combine the local MPC controllers into a global MLMPC controller for the nonlinear process. Therefore, the local model selection, the local controller design, and the local controller combination are all completed according to the gap-metric-based criteria. Close connections are established among the three key elements of the multilinear model predictive control approach. Thereby the design of a MLMPC controller is more systematic, which is found to improve the accuracy and robust performance of a MLMPC controller. Since the gap metric does not consider constraints and the use of linear models in the multimodel approach may not lead to a globally stable control system, an additional simulation-based criterion is employed to evaluate the overall closed-loop performance. A single-input, single-output (SISO) and a multi-input, multi-output continuous stirred tank reactor (MIMO CSTR) processes are studied to demonstrate the effectiveness of the proposed algorithms.

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“…Therefore an uncertain fractional-order model is achieved and a robust fractional model predictive control (RFMPC) can be developed. There are recently works that applied the predictive control to the fractional systems [29][30][31][32][33], but in these works the real system is described by a fractional-order model with fixedparameters. The advantages of the RFMPC originate in controlling the uncertain fractional-order system.…”

Section: Introductionmentioning

confidence: 99%

Robust model predictive control of uncertain fractional systems: a thermal application

Rhouma

Bouani

2014

IET Control Theory & Applications

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Launch ascent guidance by discrete multi-model predictive control

Cited by 18 publications

References 18 publications

Piecewise linear model predictive control of a rapid pressure swing adsorption system

Piecewise linear model predictive control of a rapid pressure swing adsorption system

Integrated Multilinear Model Predictive Control of Nonlinear Systems Based on Gap Metric

Robust model predictive control of uncertain fractional systems: a thermal application

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