Implementation and experimental validation of classic MPC on Programmable Logic Controllers

Huyck, Bart; Callebaut, L.; Logist, Filip; Ferreau, Hans Joachim; Diehl, Moritz; Brabanter, Jos De; Impe, Jan Van; Moor, Bart De

doi:10.1109/med.2012.6265716

Cited by 14 publications

(7 citation statements)

References 6 publications

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“…The increased complexity of the solution and the large number of critical regions can consequently affect the efficiency of the online search algorithm. The disproportionate scaling of the memory, the off-line computational demands and the complexity of the online search algorithm with the increase of the problem's complexity has been recognized widely as a barrier for further consideration of the mp-MPC approach in real-time applications [79][80][81]. Many studies have tried to address this problem and suggested countermeasures based on the problem's formulation and solution.…”

Section: Explicit/multi-parametric Mpcmentioning

confidence: 99%

Embedded Control in Wearable Medical Devices: Application to the Artificial Pancreas

et al. 2016

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Significant increases in processing power, coupled with the miniaturization of processing units operating at low power levels, has motivated the embedding of modern control systems into medical devices. The design of such embedded decision-making strategies for medical applications is driven by multiple crucial factors, such as: (i) guaranteed safety in the presence of exogenous disturbances and unexpected system failures; (ii) constraints on computing resources; (iii) portability and longevity in terms of size and power consumption; and (iv) constraints on manufacturing and maintenance costs. Embedded control systems are especially compelling in the context of modern artificial pancreas systems (AP) used in glucose regulation for patients with type 1 diabetes mellitus (T1DM). Herein, a review of potential embedded control strategies that can be leveraged in a fully-automated and portable AP is presented. Amongst competing controllers, emphasis is provided on model predictive control (MPC), since it has been established as a very promising control strategy for glucose regulation using the AP. Challenges involved in the design, implementation and validation of safety-critical embedded model predictive controllers for the AP application are discussed in detail. Additionally, the computational expenditure inherent to MPC strategies is investigated, and a comparative study of runtime performances and storage requirements among modern quadratic programming solvers is reported for a desktop environment and a prototype hardware platform.

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Section: Explicit/multi-parametric Mpcmentioning

confidence: 99%

Embedded Control in Wearable Medical Devices: Application to the Artificial Pancreas

et al. 2016

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“…infeasibility or too long computation time). The other potential solution is to ''Bring EMPC to Chips", for example, integration of EMPC into the Programmable Logic Controller (PLC) [33,34]or Field-Programmable Gate Array (FPGA) [35,36], which has been widely investigated in BAS as field controllers.…”

Section: Hardware and Communicationmentioning

confidence: 99%

Challenges of implementing economic model predictive control strategy for buildings interacting with smart energy systems

Zong

Böning

Santos

et al. 2017

Applied Thermal Engineering

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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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Implementation and experimental validation of classic MPC on Programmable Logic Controllers

Cited by 14 publications

References 6 publications

Embedded Control in Wearable Medical Devices: Application to the Artificial Pancreas

Embedded Control in Wearable Medical Devices: Application to the Artificial Pancreas

Challenges of implementing economic model predictive control strategy for buildings interacting with smart energy systems

Implementation of Model Predictive Control in Programmable Logic Controllers

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