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Embedded Online Optimization for Model Predictive Control at Megahertz Rates

Juan L. Jerez(jlj05***at***imperial.ac.uk)
Paul J. Goulart(pgoulart***at***control.ee.ethz.ch)
Stefan Richter(richters***at***control.ee.ethz.ch)
George A. Constantinides(gac1***at***imperial.ac.uk)
Eric C. Kerrigan(e.kerrigan***at***imperial.ac.uk)
Manfred Morari(morari***at***control.ee.ethz.ch)

Abstract: Faster, cheaper, and more power efficient optimization solvers than those currently offered by general-purpose solutions are required for extending the use of model predictive control (MPC) to resource-constrained embedded platforms. We propose several custom computational architectures for different first-order optimization methods that can handle linear-quadratic MPC problems with input, input-rate, and soft state constraints. We provide analysis ensuring the reliable operation of the resulting controller under reduced precision fixed-point arithmetic. Implementation of the proposed architectures in FPGAs shows that satisfactory control performance at a sample rate beyond 1 MHz is achievable even on low-end devices, opening up new possibilities for the application of MPC on embedded systems.

Keywords: model predictive control, optimization algorithms, embedded platforms

Category 1: Optimization Software and Modeling Systems (Parallel Algorithms )

Category 2: Nonlinear Optimization (Quadratic Programming )

Citation:

Download: [PDF]

Entry Submitted: 03/05/2013
Entry Accepted: 03/05/2013
Entry Last Modified: 03/05/2013

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