Efficient Embedded Model Predictive Vibration Control via Convex Lifting

Martin Gulan 1 Gergely Takacs 1 Ngoc Anh Nguyen 2 Sorin Olaru 3, 2 Pedro Rodriguez-Ayerbe 2 Boris Rohal'-Ilkiv 1
1 Slovak University of Technology in Bratislava
2 L2S - Laboratoire des signaux et systèmes
3 DISCO - Dynamical Interconnected Systems in COmplex Environments
L2S - Laboratoire des signaux et systèmes, Inria Saclay - Ile de France, SUPELEC, CNRS - Centre National de la Recherche Scientifique : UMR8506
Abstract : This paper presents an efficient real-time implementation of embedded model predictive control, adopted in the context of active vibration control with the objective of minimizing the tip deflection of lightly damped cantilever beams. In particular , we focus on memory and time-efficient explicit solutions to the associated constrained optimal control problem that are easily implementable on low-end embedded hardware. To this end, we exploit the concept of convex lifting and show how it can be used to devise low-complexity, regionless piecewise affine controllers without any loss of optimality and performance. Efficiency of this constructive procedure is quantified via an extensive complexity analysis, evidenced by a successful practical deployment and optimal vibration control performance using a family of 32-bit ARM Cortex-M based microcontroller platforms.
Keywords : embedded systems Model predictive control convex lifting explicit solutions vibration control
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Article dans une revue
IEEE Transactions on Control Systems Technology, Institute of Electrical and Electronics Engineers, In press, PP (99), pp.1-15. 〈10.1109/TCST.2017.2764019〉
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CENTRALESUPELEC | TDS-MACS | L2S | INRIA | SUP_LSS | SUP_SYSTEMES | UNIV-PARIS-SACLAY

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Martin Gulan, Gergely Takacs, Ngoc Anh Nguyen, Sorin Olaru, Pedro Rodriguez-Ayerbe, et al.. Efficient Embedded Model Predictive Vibration Control via Convex Lifting. IEEE Transactions on Control Systems Technology, Institute of Electrical and Electronics Engineers, In press, PP (99), pp.1-15. 〈10.1109/TCST.2017.2764019〉. 〈hal-01720261〉

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