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Efficient Discretization Schemes for Embedded Nonlinear Model Predictive Control


A. Zanelli

Master Thesis, HS14 (10367)

Exploiting nonlinear model predictive control in embedded applications is gradually becoming a viable technology thanks to the mature algorithms and increasing computational power available. State-of-the-art algorithms and implementations allow one to solve nonlinear optimization problems arising from NMPC formulations in the millisecond timescale. When solving such problems the main computational burden is associated with a discretization phase in which the infinite-dimensional continuous-time formulation has to be recast into a infinite-dimensional discrete-time one. This task is carried out by integrating the nonlinear differential equations describing the dynamics with highly accurate discretization schemes based on implicit Runge-Kutta methods.

In this thesis efficient discretization schemes will be presented that allow this computational burden to be decreased due to their lower complexity. We present results obtained exploiting different approaches to solve nonlinear optimization. Efficient integration schemes to be used with sequential quadratic programming or interior point methods are proposed. The presented methods can lead to a complexity reduction of more than an order of magnitude in comparison to the state-of-the-art schemes. Finally, the discretization problem is addressed in the context of tackling the optimal control problem with an alternating direction method of multipliers algorithm and an efficient solution is proposed.

Supervisors: Alexander Domahidi, Juan Jerez, Manfred Morari


Type of Publication:

(12)Diploma/Master Thesis

M. Morari

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% Autogenerated BibTeX entry
@PhdThesis { Xxx:2015:IFA_5143
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