Methods for efficient implementation of Model Predictive Control on multiprocessor systems

Gu, Ruirui; Bhattacharyya, Shuvra S.; Levine, Williams S.

doi:10.1109/cca.2010.5611090

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…• The redundant control alternatives can be evaluated using parallel processing, and can exploit multi-core or multi-processor architectures, [53], [58], as well as cloud computing techniques. The performance monitoring can also be partly executed through parallel independent evaluations, but the final comparison and control selection may not be made until all control alternatives have been evaluated.…”

Section: A Functional Redundancymentioning

confidence: 99%

Toward Dependable Embedded Model Predictive Control

Johansen

2017

IEEE Systems Journal

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Abstract-While Model Predictive Control (MPC) is the industrially preferred method for advanced control in the process industries, it has not found much use in consumer products and safety-critical embedded systems applications in industries such as automotive, aerospace, medical and robotics. The main barriers are implementability and dependability, where important factors are implementation of advanced numerical optimization algorithms on resource-limited embedded computing platforms and the associated complexity of verification. This challenge comes from a requirement of the use of ultra-reliable hardware and software architectures in safety-critical applications, low-cost hardware in consumer products, or both. This paper surveys the state-of-the-art in the emerging field of dependable embedded MPC, and discusses some key challenges related to its design, implementation and verification. A novel result is the study of a simulator-based performance monitoring and control selection method that monitors and predicts MPC performance and switches to a highly reliable backup controller in cases when the MPC experiences performance issues.

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Section: A Functional Redundancymentioning

confidence: 99%

Toward Dependable Embedded Model Predictive Control

Johansen

2017

IEEE Systems Journal

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“…We solve the relaxed version of the convex optimization problem (2) by formulating the augmented Lagrangian with respect to the additional equality constraints, using the matrices and vectors defined in (3) and (4). The augmented Lagrangian can be written as…”

Section: Appendixmentioning

confidence: 99%

A hierarchical time-splitting approach for solving finite-time optimal control problems

Stathopoulos

Keviczky

Wang

2013

2013 European Control Conference (ECC)

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Abstract-We present a hierarchical computation approach for solving finite-time optimal control problems using operator splitting methods. The first split is performed over the time index and leads to as many subproblems as the length of the prediction horizon. Each subproblem is solved in parallel and further split into three by separating the objective from the equality and inequality constraints respectively, such that an analytic solution can be achieved for each subproblem. The proposed approach leads to a highly parallelizable nested decomposition scheme. We present a numerical comparison with the standard state-of-the-art solver SDPT3, and provide analytic solutions to several elements of the algorithm, which enhances its applicability in fast large-scale applications.

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