Scalability of QP solvers for embedded model predictive control applied to a subsea petroleum production system

Binder, Benjamin; Kufoalor, D. K. M.; Johansen, Tor Arne

doi:10.1109/cca.2015.7320771

Cited by 6 publications

(4 citation statements)

References 20 publications

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“…First, quadratic programming (QP) solvers, such as qpOASES [35], MOSEK [36], ECOS [37], OSQP [38] and ODYS QP [39], can effectively be used. Second, tools such as CVXGEN [40][41][42][43], FORCES [44], FiOrdOs [45] and QPGEN [46] were developed to optimise the code of quadratic programming solvers. The CasADi [47] tool generates an efficient implementation of nonlinear optimisation problems.…”

Section: Introductionmentioning

confidence: 99%

Embedded Model Predictive Control of Tankless Gas Water Heaters to Enhance Users’ Comfort

Conceição,

Quintã,

Ferreira

et al. 2023

Machines

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Water heating is a significant part of households’ energy consumption, and tankless gas water heaters (TGWHs) are commonly used. One of the limitations of these devices is the difficulty of keeping hot water temperature setpoints when changes in water flow occur. As these changes are usually unexpected, the controllers typically used in these devices cannot anticipate them, strongly affecting the users’ comfort. Moreover, considerable water and energy waste are associated with the long-time response to cold starts. This work proposes the development of a model predictive control (MPC) to be deployed in low-cost hardware, such that the users’ thermal comfort and water savings can be improved. Matlab/Simulink were used to develop, validate and automatically generate C code for implementing the controller in microcontroller-based systems. Hardware-in-the-loop simulations were performed to evaluate the performance of the MPC algorithm in 8-bit and 32-bit microcontrollers. A 6.8% higher comfort index was obtained using the implementation on the 32-bit microcontroller compared to the current deployments; concerning the 8-bit microcontroller, a 4.2% higher comfort index was achieved. These applications in low-cost hardware highlight that users’ thermal comfort can be successfully enhanced while ensuring operation safety. Additionally, the environmental impact can be significantly reduced by decreasing water and energy consumption in cold starts of TGWHs.

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Section: Introductionmentioning

confidence: 99%

Embedded Model Predictive Control of Tankless Gas Water Heaters to Enhance Users’ Comfort

Conceição,

Quintã,

Ferreira

et al. 2023

Machines

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“…This has become possible due to recent advances in optimization algorithms for convex optimization problems and to the development of code generation tools that specifically tailor embedded systems, such as, to name a few of the more widespread ones, FiOrdOs [8], CVXGEN [9], FORCES [10] or qpOASES [11]. Examples of these tools being used to implement MPC in embedded platforms include [12], [13], [14] and [15]. Paper [16] provides an overview and comparison of the aforementioned tools for their use in embedded MPC.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Model Predictive Control in Programmable Logic Controllers

Krupa

Limón

Álamo

2021

IEEE Trans. Contr. Syst. Technol.

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In this paper we present an implementation of a low memory footprint Model Predictive Control (MPC) based controller in Programmable Logic Controllers (PLC). Automatic code generation of standardized IEC 61131-3 PLC programming languages is used to solve the MPC's optimization problem online. The implementation is designed for its application in a realistic industrial environment, including timing considerations and accounting for the possibility of the PLC not being exclusively dedicated to the MPC controller. We describe the controller architecture and algorithm, show the results of its memory footprint with regards to the problem dimensions and present the results of its implementation to control a hardware in the loop multivariable chemical plant.

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“…These solvers, although they can be used to successfully implement MPC controllers in embedded systems (see [10,11,12,13] for a few examples), are for generic QP problems. Therefore, the development of optimization algorithms tailored to the specific MPC optimization problem can potentially provide better results.…”

Section: Introductionmentioning

confidence: 99%

Implementation of model predictive control for tracking in embedded systems using a sparse extended ADMM algorithm

Krupa,

Alvarado,

Limon

et al. 2020

Preprint

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This article presents an implementation of a sparse, low-memory footprint optimization algorithm for the implementation of the model predictive control for tracking formulation in embedded systems. The algorithm is based on an extension of the alternating direction method of multipliers to problems with three separable functions in the objective function. One of the main advantages of the proposed algorithm is that its memory requirements grow linearly with the prediction horizon of the controller. Its sparse implementation is attained by identification of the particular structure of the optimization problem, and not by employing the common sparse algebra techniques, leading to a very computationally efficient implementation. We describe the controller formulation and provide a detailed description of the proposed algorithm, including its pseudocode. We also provide a simple (and sparse) warmstarting procedure that can significantly reduce the number of iterations. Finally, we show some preliminary numerical results of the performance of the algorithm.

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Scalability of QP solvers for embedded model predictive control applied to a subsea petroleum production system

Cited by 6 publications

References 20 publications

Embedded Model Predictive Control of Tankless Gas Water Heaters to Enhance Users’ Comfort

Embedded Model Predictive Control of Tankless Gas Water Heaters to Enhance Users’ Comfort

Implementation of Model Predictive Control in Programmable Logic Controllers

Implementation of model predictive control for tracking in embedded systems using a sparse extended ADMM algorithm

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