A FPGA implementation of model predictive control

Ling, Keck Voon; Yue, S.P.; Maciejowski, Jan M.

doi:10.1109/acc.2006.1656502

Cited by 109 publications

(69 citation statements)

References 8 publications

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“…An overview of the design, verification and validation methods for medical devices can be found in [122][123][124]. The performance of different control algorithms and formulations can be evaluated using a prototyping environment before testing with the actual device, to ensure that the suggested control method is suitable for the application [125]. In fact, hardware-in-the-loop (HIL) simulation has been gradually considered as the necessary step from pure virtual simulation to full physical prototyping [126] for many safety-critical applications.…”

Section: Brief Overview On Design Approachesmentioning

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: Brief Overview On Design Approachesmentioning

confidence: 99%

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

et al. 2016

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“…Another possibility is to use a FPGA to solve the optimization problem. The FPGAs are quite effective and have shown promising results related to linear and nonlinear MPC implementations, [13,14]. FPGAs solving MPC problems have also been used for controlling power electronics, which requires low computational costs [15].…”

Section: Implementation Aspects and Requirementsmentioning

confidence: 99%

Management of harmonic propagation in a marine vessel by use of optimization

Skjong

Ochoa-Gimenez

Molinas

et al. 2015

2015 IEEE Transportation Electrification Conference and Expo (ITEC)

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Abstract-Advances in power electronics drive systems for variable speed operation has enabled extensive use of such solutions in the propulsion and thruster systems of marine vessels. These solutions however introduce current and voltage distortions that compromises the overall power quality of the onboard electrical system. This paper presents and discusses one approach for generating the harmonic current reference for an active filter based on optimization. Two relevant results are revealed by this study: 1) lower THD values are attained by performing system optimization compared to local compensation of one load, and 2) the lower THD values are achieved with a smaller active filter rating than the one required for local load compensation.

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“…This paper is concerned with facilitating applications of MPC in which computational complexity, in particular computation time, is likely to be an issue. One can foresee that applications to embedded systems, with the MPC algorithm implemented in a chip or an FPGA [5,13,14,17], are likely to run up against this problem.…”

Section: The Basic Ideamentioning

confidence: 99%

Multiplexed model predictive control

et al. 2012

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This paper proposes a form of MPC in which the control variables are moved asynchronously. This contrasts with most MIMO control schemes, which assume that all variables are updated simultaneously. MPC outperforms other control strategies through its ability to deal with constraints. This requires online optimization, hence computational complexity can become an issue when applying MPC to complex systems with fast response times. The multiplexed MPC scheme described in this paper solves the MPC problem for each subsystem sequentially, and updates subsystem controls as soon as the solution is available, thus distributing the control moves over a complete update cycle. The resulting computational speed-up allows faster response to disturbances, which may result in improved performance, despite finding sub-optimal solutions to the original problem.

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A FPGA implementation of model predictive control

Cited by 109 publications

References 8 publications

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

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

Management of harmonic propagation in a marine vessel by use of optimization

Multiplexed model predictive control

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