Nonlinear predictive control on a heterogeneous computing platform

Khusainov, B. G.; Kerrigan, Eric C.; Suardi, Andrea; Constantinides, George A.

doi:10.1016/j.conengprac.2018.06.016

Cited by 12 publications

(9 citation statements)

References 31 publications

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“…Control theory has been applied in High-performance Computing rather scarcely therefore it is still in need for basic research [3], [4], [5]. Most previous approaches propose rather simple linear models that, to our best understanding, lack the ability to reflect several nonlinear behaviors and variable constraints of the real system, since their accuracy is limited to a specific region of operation, failing to ensure the desired performance and stability objectives for all possible situations.…”

Section: A Control Theory For High Performance Computingmentioning

confidence: 99%

A control-theory approach for cluster autonomic management: maximizing usage while avoiding overload

Yabo

Robu

Richard

et al. 2019

2019 IEEE Conference on Control Technology and Applications (CCTA)

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Section: A Control Theory For High Performance Computingmentioning

confidence: 99%

A control-theory approach for cluster autonomic management: maximizing usage while avoiding overload

Yabo

Robu

Richard

et al. 2019

2019 IEEE Conference on Control Technology and Applications (CCTA)

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“…Reducing the fractional length to the minimum needed can lead to a large decrease in the required resources, power requirements, and solution times for FPGA implementations compared with simply choosing either floating-point or a larger fixed-point data type to get stability. This can be seen in Table II, where we present results for an FPGA implementation of the FGM using ProtoIP [9] targeting the Xilinx Zynq 7020 with a clock speed of 100MHz. An implementation with f = 12 uses 77% fewer memory blocks, 33% fewer Digital Signal Processing (DSP) computation blocks, and 25% less time when compared with an implementation for f = 26, and 85% fewer memory blocks taking 45% less time when compared to a singleprecision floating-point implementation.…”

Section: Numerical Experimentsmentioning

confidence: 99%

Modeling Round-off Error in the Fast Gradient Method for Predictive Control

McInerney

Kerrigan

Constantinides

2019

2019 IEEE 58th Conference on Decision and Control (CDC)

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We present a method for determining the smallest precision required to have algorithmic stability of an implementation of the Fast Gradient Method (FGM) when solving a linear Model Predictive Control (MPC) problem in fixedpoint arithmetic. We derive two models for the round-off error present in fixed-point arithmetic. The first is a generic model with no assumptions on the predicted system or weight matrices. The second is a parametric model that exploits the Toeplitz structure of the MPC problem for a Schur-stable system. We also propose a metric for measuring the amount of round-off error the FGM iteration can tolerate before becoming unstable. This metric is combined with the round-off error models to compute the minimum number of fractional bits needed for the fixed-point data type. Using these models, we show that exploiting the MPC problem structure nearly halves the number of fractional bits needed to implement an example problem. We show that this results in significant decreases in resource usage, computational energy and execution time for an implementation on a Field Programmable Gate Array.

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“…We present a new heterogeneous implementation of nonlinear interior-point algorithm for predictive control, that was first introduced in [19]. The main features of the proposed implementation are:…”

Section: Introductionmentioning

confidence: 99%

“…• The proposed controller is experimentally validated in the loop with a gantry crane highfidelity Simscape [22] model. In [19] the controller was only validated in the loop with a nominal model.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear predictive control on a heterogeneous computing platform

et al. 2017

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We propose an implementation of an interior-point-based nonlinear predictive controller on a heterogeneous processor. The workload can be split between a general-purpose CPU and a fieldprogrammable gate array to trade off the contradicting design objectives of control performance and computational resource usage. A new way of exploiting the structure of the KKT matrix yields significant memory savings. We report an 18x memory saving, compared to existing approaches, and a 36x speedup over a software implementation with an ARM Cortex-A9 processor. We also introduce a new release of Protoip, which abstracts low-level details of heterogeneous programming and allows processor-in-the-loop verification.

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Nonlinear predictive control on a heterogeneous computing platform

Cited by 12 publications

References 31 publications

A control-theory approach for cluster autonomic management: maximizing usage while avoiding overload

A control-theory approach for cluster autonomic management: maximizing usage while avoiding overload

Modeling Round-off Error in the Fast Gradient Method for Predictive Control

Nonlinear predictive control on a heterogeneous computing platform

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