A co-processor FPGA platform for the implementation of real-time model predictive control

Bleris, Leonidas; Vouzis, Panagiotis D.; Arnold, Mark G.; Kothare, Mayuresh V.

doi:10.1109/acc.2006.1656499

Cited by 65 publications

(46 citation statements)

References 12 publications

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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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Section: The Basic Ideamentioning

confidence: 99%

Multiplexed model predictive control

et al. 2012

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“…This has prompted a number of researchers to investigate means for increasing the speed with which optimal controls can be computed. Much of this work has focused on improving the algorithms [1], [11].…”

Section: Related Work a Control Backgroundmentioning

confidence: 99%

Methods for efficient implementation of Model Predictive Control on multiprocessor systems

Bhattacharyya

Levine

2010

2010 IEEE International Conference on Control Applications

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Abstract-Model Predictive Control (MPC) has been used in a wide range of application areas including chemical engineering, food processing, automotive engineering, aerospace, and metallurgy. An important limitation on the application of MPC is the difficulty in completing the necessary computations within the sampling interval. Recent trends in computing hardware towards greatly increased parallelism offer a solution to this problem. This paper describes modeling and analysis tools to facilitate implementing the MPC algorithms on parallel computers, thereby greatly reducing the time needed to complete the calculations. The use of these tools is illustrated by an application to a class of MPC problems.

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“…[24] addressed the implementation of MPC on very resource-constrained embedded systems with an FPGA implementation consisting of a soft-core processor attached to a co-processor used to accelerate computations that allowed data reuse. [23] also proposed a mixed software-hardware implementation where the core matrix computations are implemented in parallel custom hardware, whereas the remaining operations are implemented on a soft-processor core synthesized into the FPGA. The computational bottlenecks in implementing a logarithmic-barrier method for solving an unstructured QP were identified for determining which computations should be carried out in which unit.…”

Section: Related Workmentioning

confidence: 99%

Model predictive control for deeply pipelined field-programmable gate array implementation: algorithms and circuitry

Jerez

Ling

Constantinides

et al. 2012

IET Control Theory Appl.

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Model predictive control (MPC) is an optimization-based scheme that imposes a real-time constraint on computing the solution of a quadratic programming (QP) problem. The implementation of MPC in fast embedded systems presents new technological challenges. In this paper we present a parameterized field-programmable gate array (FPGA) implementation of a customized QP solver for optimal control of linear processes with constraints, which can achieve substantial acceleration over a general purpose microprocessor, especially as the size of the optimization problem grows. The focus is on exploiting the structure and accelerating the computational bottleneck in an existing primal-dual interior-point method. We then introduce a new MPC formulation that can take advantage of the novel computational opportunities, in the form of parallel computational channels, offered by the proposed pipelined architecture to improve performance even further. This highlights the importance of the interaction between the control theory and digital system design communities for the success of MPC in fast embedded systems.

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A co-processor FPGA platform for the implementation of real-time model predictive control

Cited by 65 publications

References 12 publications

Multiplexed model predictive control

Multiplexed model predictive control

Methods for efficient implementation of Model Predictive Control on multiprocessor systems

Model predictive control for deeply pipelined field-programmable gate array implementation: algorithms and circuitry

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