@inproceedings{cecchin2023nonlinear,

title = {Nonlinear Model Predictive Control for Efficient Control of Variable Speed Variable Displacement Pumps},

author = { Leonardo Cecchin and Jonathan Frey and Stefan Gering and Maximilian Manderla and Adrian Trachte and Moritz Diehl},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {2023 Modeling, Estimation and Control Conference (MECC)},

pages = {1–6},

organization = {IFAC},

abstract = {Hydraulic pumps are a key component in manufacturing industry and off-highway vehicles.

	Paired with diesel engines or electric motors, they provide hydraulic flow that can conveniently be used to power a variety of actuators.

	Hydraulic power transmission has numerous advantages, unfortunately energy efficiency is usually not one of those.

	The use of Variable Speed Variable Displacement pumps has been proven to be advantageous with respect to constant speed or constant displacement solutions: It allows to achieve higher efficiency and faster flow tracking dynamics.

	This paper presents the development of a Model Predictive Control for this system, considering the nonlinearities and look-up-tables that characterize the system dynamics.

	The Model Predictive Controller is then compared both in simulation and on test bench with a reference controller for such system, showing potential both regarding efficiency and flow tracking dynamics.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{cecchin2023locally,

title = {Locally Weighted Regression with Approximate Derivatives for Data-based optimization},

author = { Leonardo Cecchin and Katrin Baumgärtner and Stefan Gering and Moritz Diehl},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {2023 European Control Conference (ECC)},

pages = {1–6},

organization = {IEEE},

abstract = {Interpolation and approximation of data provided in terms of a Look-Up Table (LUT) is a common and well-known task, and is especially relevant for industrial applications. When using the function for point-wise evaluation, the method choice only affects the accuracy of the function value itself. However, when the LUT is used as part of an optimization problem formulation, a bad method choice can prevent convergence or alter significantly the outcome of the solver. Moreover, computational efficiency becomes critical due to the much higher number of evaluations required. This work focuses on a variation of Locally Weighted Regression, with approximate derivatives computation. The result is a method that allows one to obtain the function value together with the first n derivatives, at a reduced computational cost. Theoretical properties of the approach are analyzed, and the results of a minimization problem using the proposed method are compared with more traditional ones. The new approach shows promising performance and results, both for computational efficiency and effectiveness when used in optimization.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}