A Primal-Dual Architecture for Embedded Implementation of Linear Model Predictive Control

Sabo, J.; Adegbege, Ambrose A.

doi:10.1109/cdc.2018.8619294

Cited by 4 publications

(3 citation statements)

References 26 publications

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“…Obtain z k+1 2 by solving P 2 (z k+1 1 , z k 3 , λ k ) using (12). , λ k ) using ( 14) and (15). n+m) .…”

Section: Particularizing Eadmm To the Mpct Problemmentioning

confidence: 99%

“…Therefore, the development of optimization algorithms tailored to the specific MPC optimization problem can potentially provide better results. Some noteworthy examples of this approach being used to implement MPC controllers in embedded systems are [14,15,16] for implementations in FPGAs, [17] for microcontrollers and [18,19,20,21] for PLCs. Specifically, the authors proposed in [18] and [22] an implementation approach for MPC controllers in PLCs which takes advantage of the structure of the matrices of the QP problem.…”

Section: Introductionmentioning

confidence: 99%

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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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“…Obtain z k+1 2 by solving P 2 (z k+1 1 , z k 3 , λ k ) using (12). , λ k ) using ( 14) and (15). n+m) .…”

Section: Particularizing Eadmm To the Mpct Problemmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…Some of these implementations rely on code generation to further take advantage of the specifics of the implementations, whereas others implement a hand tailored algorithm in the embedded system. Some noteworthy examples are [17], [18] and [19] for PLC implementations; [20], where the code generation tool µAO-MPC [21] is used, [22], [23] and [24] for FPGA implementations; [25] for microcontrollers; and [26], where in spite of the controller not being implemented in an embedded system, the underlying structure of the MPC's optimization problem was exploited.…”

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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